> **Source:** https://permissionless.fi/en/22-volatility-sources
> From *Permissionless Finance* (Permissionless Finance: From Perpetual Futures to the On-Chain Global Market) by Eric Cheung. Licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).

# Chapter 22: A Four-Source Decomposition of Volatility and Excess Volatility

On March 5, 2024, Bitcoin fell more than 10% within hours from an all-time high of approximately $69,000, and derivative positions worth more than $1.1 billion across the market were forcibly liquidated, of which long positions accounted for roughly $870 million [1]. On the surface, this appears to be nothing more than a single episode of asset-price repricing. Behind the aggregate magnitude of the decline, however, lies a complex microstructure driven jointly by different market mechanisms and participant behaviors. Part of the 10% drop originated in the unexpected release of macroeconomic data, which led investors to reprice risk assets; this portion of the price movement reflected the market's absorption of new information and was therefore permanent in character. Another part originated in market makers withdrawing liquidity as volatility rose, so that subsequent sell orders produced a larger price impact on a shallow order book—a component that tends to revert once liquidity is restored. More strikingly, the initial price decline triggered the margin liquidation of a large number of highly leveraged long positions; the liquidation engine executed forced-closure orders at market prices, generating additional price movement on an already-thin order book that carried no fundamental information whatsoever [2]. At the same time, this sharp local volatility was transmitted through cross-asset linkages to Ethereum and other crypto assets, further intensifying selling pressure across the entire market.

This phenomenon raises the central question of this chapter: price volatility in perpetual futures markets is not a homogeneous stochastic process but a mixture of sources with fundamentally different natures. What, then, are these sources? How much volatility does each contribute? And which portion of volatility is efficient volatility that reflects the arrival of new information, while which portion is inefficient volatility manufactured by structural frictions and institutional mechanisms?

To answer this question, this chapter proposes a four-source decomposition framework for volatility, systematically decomposing the price volatility of perpetual futures into informational volatility, liquidity volatility, mechanism-induced volatility, and transmitted volatility. Informational volatility arises from the arrival of new information such as macroeconomic events and on-chain data, and it represents the healthy process of price discovery. Liquidity volatility is caused by insufficient liquidity supply or by fluctuations in liquidity provision, manifesting as temporary, information-free price shocks. Mechanism-induced volatility is the feature that distinguishes perpetual futures markets from traditional markets; it is manufactured by institutional design elements such as liquidation cascades, funding-rate settlement, and mark-price deviations, and this type of volatility is almost nonexistent, or of negligible magnitude, in traditional financial markets [3]. Transmitted volatility reflects the spillover of volatility across different assets and markets. Building on this, the chapter introduces the concept of a volatility budget, arguing that total volatility can be viewed as a budget allocated across different sources, and that this allocation depends heavily on the market regime. In calm periods, informational volatility dominates; under market stress or crisis, the noise generated by liquidity frictions and mechanism design takes over most of the volatility budget. This regime-switching mechanism explains why, under extreme market conditions, price movements systematically overreact and are subsequently followed by predictable reversion.

Starting from the theoretical foundations of volatility decomposition, this chapter analyzes in turn the leverage-amplification effect and round-the-clock continuity of informational volatility in perpetual futures, and examines the time structure of liquidity volatility and its gradient distribution between mainstream and long-tail assets. It then analyzes in depth how liquidation cascades and funding-rate settlement act as independent sources of mechanism-induced volatility that inject periodic or explosive price noise into the market, and explores the transmission paths of volatility across markets and across assets. By tracing the interactions among these four volatility sources, the chapter reveals the microstructural foundations of excess volatility in perpetual futures markets and its welfare implications. After reading this chapter, readers will be able to move beyond the traditional view of volatility as a single metric, master quantitative methods for identifying and measuring different volatility sources, understand how the specific institutional design of perpetual futures endogenously manufactures price noise, and build a solid microstructural analytical framework for the subsequent chapters' explanation of volatility clustering and for the construction of volatility forecasts and trading strategies.

## 22.1 Theoretical foundations of volatility decomposition

Volatility is usually treated as a single risk metric, but this aggregate view obscures the fundamentally different mechanisms that drive price movements. This section first argues for the necessity of volatility decomposition, then reviews the decomposition methods of traditional finance and their limitations in crypto-derivatives markets. On this basis, it proposes a four-source decomposition framework suited to perpetual futures and introduces the analytical concept of a volatility budget to characterize the regime-dependent shares of each source.

### 22.1.1 The necessity of volatility decomposition

In the quantitative analysis of financial markets, undecomposed aggregate volatility is a composite metric that entails substantial information loss. When an asset's annualized volatility is observed to be 60%, that figure alone reveals nothing about the internal structure of the price movement, nor does it indicate what share represents a healthy price-discovery process and what share arises from noise generated by institutional frictions. Treating volatility as a single random-walk process conceals the multiple parallel dynamic mechanisms within market microstructure. In perpetual futures—a market with distinctive leverage and liquidation features—the composition of volatility is more complex than in traditional spot markets, so decomposing it precisely is of central theoretical and practical importance.

The value of volatility decomposition is first evident in academic research, where it provides a quantitative basis for distinguishing information-driven efficient volatility from friction-driven inefficient volatility. Under the efficient market hypothesis (EMH), price changes should reflect only the arrival of new information, and this informational volatility is a manifestation of market efficiency. In real markets, however, there are numerous non-informational price shocks, such as amplified slippage caused by drying-up liquidity. Separating the two is a precondition for a deeper understanding of asset-pricing efficiency and a central link connecting price-discovery theory with market-microstructure research. Through decomposition, researchers can accurately measure the net effect of a specific trading mechanism on market quality, thereby enabling cross-sectional comparisons of efficiency across different asset classes or trading venues.

For traders and risk managers, volatility decomposition has direct strategic value. Volatility from different sources exhibits markedly different predictability and mean-reversion characteristics over time. Price changes triggered by fundamental information are typically permanent, representing a revaluation of an asset's intrinsic value, and traders should adjust their positions in line with this trend. By contrast, volatility triggered by short-term liquidity shocks or large-scale leveraged liquidations tends to exhibit overreaction, with prices tending to revert toward their initial level once the shock passes. If a risk-management model cannot distinguish between these two types of volatility, it may trigger unnecessary stop-losses during a false liquidity crisis, or mistakenly increase risk exposure during a genuine revaluation. Decomposing aggregate volatility into subcomponents with different dynamic characteristics is therefore a necessary step in building robust trading strategies and precise value-at-risk (VaR) models.

From the perspective of market designers, volatility decomposition is a quantitative guide for optimizing trading rules and improving protocol mechanisms. A central goal of market design is to minimize the non-essential volatility artificially manufactured by institutional frictions and structural deficiencies. By identifying the specific components of aggregate volatility that can be eliminated through institutional improvements, designers can adjust parameters in a targeted way. For example, if the decomposition shows that most of a particular asset's volatility stems from periodic arbitrage around funding-rate settlement, protocol developers can consider introducing a smoothing mechanism or a continuous-settlement scheme to reduce this mechanism-induced noise. This diagnostic approach, grounded in a decomposition framework, moves decentralized finance (DeFi) governance from an intuition-driven to a data-driven basis, providing solid empirical support for a wide range of parameter adjustments.

### 22.1.2 A review of traditional decomposition methods

Before examining the distinctive volatility structure of perpetual futures, it is worth reviewing the classic methods that traditional finance uses to address volatility. Early volatility research relied mainly on daily or lower-frequency return data, and this low-frequency sampling severely limited the observation of intraday price dynamics. As high-frequency trading data proliferated, realized volatility came into wide use as a nonparametric estimation method [4]. By summing the squares of high-frequency returns, this method provides a more precise measure of intraday volatility. Realized volatility solves the precision problem of low-frequency data and can effectively capture sharp short-term market movements, but it remains, in essence, an aggregate measure of total volatility and cannot distinguish whether that volatility stems from the release of macroeconomic information, the retreat of market makers, or the endogenous responses of market mechanisms.

To capture the time-varying features of volatility, GARCH models and their many variants have dominated time-series analysis [5]. This family of models has successfully described the volatility clustering that is pervasive in financial markets—large movements tend to be followed by large movements, and small movements by small movements. These models, however, focus on the statistical description of the volatility process rather than on explaining its economic origins. They lump all factors that drive price changes together as random shocks and cannot dissect the nature of these shocks at the microstructural level. When confronted with structural volatility triggered by specific institutional design, purely statistical models struggle to capture its generating mechanism, because they assume that shocks are homogeneous across market regimes.

Market-microstructure theory provides finer analytical tools for understanding the intrinsic mechanisms of price movements. Within a framework of information asymmetry, scholars have proposed a series of decomposition models to isolate different types of price shocks. Among them, the information-share method is widely used to measure the contribution to price discovery in multi-market settings [6] (for a methodological review, see [7]). Based on the common efficient-price random-walk component in a cointegration system, this method separates out the long-run equilibrium price of related assets, thereby quantifying each market's relative contribution to incorporating new information into the price process. In parallel, microstructure research has sought to decompose the price change caused by a single trade into a permanent component and a transitory component. The permanent component reflects the private information embedded in the trade, whereas the transitory component is treated as liquidity friction arising from inventory risk and order-processing costs. In addition, estimation methods based on the bid-ask spread offer a simple way to infer transaction costs and non-informational volatility [8].

Although these traditional methods have achieved notable success in equity and foreign-exchange markets, applying them directly to crypto-asset perpetual futures markets encounters clear limitations. Most classic models divide volatility into only two broad categories—information and noise, or permanent and transitory. This dichotomy may suffice for spot markets that lack complex derivative features, but it is too coarse for perpetual futures. Transitory volatility in perpetual futures is in fact a complex composite: it contains not only market makers' liquidity frictions but also, deeply, the institutional price movements triggered by funding-rate settlement and leveraged liquidation cascades. Without further separating these subcategories of fundamentally different nature, one cannot accurately assess the true operating efficiency of perpetual futures markets. More importantly, traditional methods are usually built on the implicit assumption of a friction-free institutional environment and fail to adequately account for the unique features of crypto markets—round-the-clock trading, the prevalence of high leverage, and intense cross-asset contagion—which requires us to build a more comprehensive and fine-grained decomposition framework on the existing theoretical foundation.

### 22.1.3 The four-source decomposition framework

To fully parse the intrinsic structure of price movements in perpetual futures markets, we propose a four-source volatility decomposition framework. The framework decomposes aggregate realized volatility into four subcomponents of markedly different natures and driving mechanisms: informational volatility, liquidity volatility, mechanism-induced volatility, and transmitted volatility. This conceptual identity is not merely a statistical variance decomposition but an attribution model in the microeconomic sense, revealing how different types of shocks are ultimately converted into observable price changes through specific market channels.

Informational volatility is the permanent price adjustment caused by the arrival of new fundamental information, macroeconomic data releases, or idiosyncratic on-chain events. This volatility is a direct expression of the market's price-discovery function and represents a reasonable revaluation of an asset's intrinsic value. Informational volatility is irreversible: once new information is absorbed, the price stabilizes at a new equilibrium level rather than undergoing systematic mean reversion. In any efficient financial market, informational volatility cannot be eliminated; far from being a market defect, it is a sign of healthy market functioning. In perpetual futures markets, because of leverage, information of a given magnitude may trigger larger-scale position adjustments and thus amplify the magnitude of informational volatility in the short run—but in essence the price is still simply reflecting the new market environment.

Liquidity volatility arises from temporary price shocks produced by insufficient market depth or by adjustments in market makers' quoting strategies. When a large order is executed on an order book that lacks sufficient supporting liquidity, the price slips significantly; this deviation is not due to any change in the asset's fundamentals but is pure trading friction. The core feature of liquidity volatility is its temporariness: after the shock, as liquidity providers re-enter and arbitrageurs act, the price tends to revert toward the pre-shock equilibrium level. In perpetual futures markets, liquidity volatility often exhibits positive-feedback characteristics—an initial rise in volatility causes market makers to widen spreads and retreat, which in turn causes subsequent orders to produce larger price impacts. This volatility is information-free noise and can, in principle, be reduced by introducing more efficient market-making mechanisms or by deepening the market.

Mechanism-induced volatility is the core volatility source that distinguishes perpetual futures markets from traditional markets, and its negative impact on price stability is especially pronounced. It is generated entirely by institutional design features unique to this derivative, including the forced-liquidation cascades of leveraged positions, the periodic settlement pulses of the funding rate, and the computational deviation between the mark price and the last traded price. This type of volatility contains no new information about an asset's true value, yet it can manufacture extreme price movements [2]. For example, when the price touches a key liquidation line, the liquidation engine dumps the liquidated positions at market prices, causing significant price deviation on an order book that lacks depth; such a decline, triggered purely by the mechanical execution of an institutional mechanism, is a textbook case of mechanism-induced volatility. Like liquidity volatility, mechanism-induced volatility usually reverts to some degree after the fact, but if the scale of liquidation is too large, it can also trigger systemic liquidity exhaustion, causing prices to deviate from fundamentals for an extended period. Mechanism-induced volatility is a direct cost of the institutional design of perpetual futures and can, in principle, be substantially reduced by optimizing liquidation algorithms and fee structures.

Transmitted volatility describes the spillover effect that spreads in from the violent volatility of other markets or related assets through cross-market arbitrage and cross-margin mechanisms. In a highly interconnected network of crypto assets, sharp volatility in the Bitcoin spot market is rapidly transmitted to its perpetual futures, and liquidation events in mainstream assets often trigger chain reactions in altcoins through balance-sheet effects. The nature of transmitted volatility depends on the character of its source: if the source is a macroeconomic information release, the transmitted volatility carries some information content; if the source is a local liquidity collapse, the transmitted volatility is purely contagious noise. The continuous trading and high fragmentation of perpetual futures markets make this cross-market, cross-asset transmission exceptionally rapid and large.

These four volatility sources differ fundamentally in nature, duration, information content, and policy implications. Informational volatility is a high-information-content permanent adjustment; liquidity volatility is an information-free temporary friction; mechanism-induced volatility is noise that is endogenous to institutional design and can be substantially reduced but not entirely eliminated; and transmitted volatility is an external input from cross-market linkages.

At the formal level, the four-source decomposition framework can be expressed as an attribution decomposition of aggregate realized variance. Let the total realized variance of an asset's log-price process $p_t$ over the time window $[0, T]$ be $RV_T = \sum_{i=1}^{N} r_i^2$, where $r_i$ is the $i$-th high-frequency return observation. The four-source decomposition treats each micro price change $r_i$ as the superposition of four types of shocks: $r_i = r_i^{info} + r_i^{liq} + r_i^{mech} + r_i^{cond}$. The total variance can then be decomposed as:

$$RV_T = \sum_{i=1}^{N} (r_i^{info})^2 + \sum_{i=1}^{N} (r_i^{liq})^2 + \sum_{i=1}^{N} (r_i^{mech})^2 + \sum_{i=1}^{N} (r_i^{cond})^2 + \sum_{i=1}^{N} \sum_{j \neq k} r_i^{j} r_i^{k}$$

The first four terms on the right-hand side correspond to the direct contributions of the four sources, and the last term is the interaction term. In calm market periods, the shocks from each source are approximately uncorrelated in time, the interaction term approaches zero, and the total variance is approximately additive. In crisis periods, however, liquidity exhaustion and liquidation cascades occur simultaneously within the same time window, the interaction term is significantly positive, and the total variance exceeds the sum of the independent contributions of the individual sources—which is precisely the mathematical basis for the superlinear amplification of volatility under extreme market conditions.

The linear additive decomposition above is essentially a first-order approximation, whose validity depends on the assumption that the shocks from each source are approximately independent in time. In calm periods this assumption broadly holds, the interaction term is negligible, and the total variance is approximately additive. In crisis periods, however, the four sources are highly entangled: a single liquidation-cascade event simultaneously produces a mechanism-induced shock (liquidation-engine execution), a liquidity shock (order-book penetration), and a transmitted shock (cross-asset contagion), and the interaction term may account for a substantial share of total variance. In such cases, the four-source decomposition should be regarded as a conceptual attribution tool rather than a precise statistical decomposition; its core value lies in revealing the multiple generating mechanisms of volatility rather than in providing precisely additive figures. Future research could introduce regime-switching structural models or nonlinear realized-covariance methods to handle the interaction effects of crisis periods more precisely.

At the level of empirical identification, the approximate separation of the four sources can draw on the following strategies. Mechanism-induced volatility can be identified via an event-window method, because liquidation timestamps are precisely known and funding-rate settlement times are fixed. Transmitted volatility can be separated along the cross-asset dimension via Granger causality tests or the Diebold-Yilmaz (DY) spillover index. Informational volatility can be estimated along the time-series dimension via permanent price impact or the information-share method. Liquidity volatility is then treated as a residual category, obtained approximately by subtracting the identifiable portions of the other three from total volatility. This identification strategy works well in normal markets, where the sources are well separated in time, but in crisis periods, when the sources fire simultaneously, the boundaries inevitably become blurred. Treating liquidity volatility as a residual category carries two costs. First, the fact that the four sources sum to exactly 100% is an artifact of this residual definition rather than a result of the framework being empirically tested; whether an unidentified fifth source exists (such as market-maker reflexivity or cross-exchange oracle-latency arbitrage) must be corroborated by examining the time-scale structure of the residual, and will not automatically reveal itself as a residual. Second, the reversion time scale of the narrative-driven volatility mentioned in Section 22.2 (several days to several weeks) is significantly longer than that of pure liquidity shocks (on the order of minutes), and under the residual method it would be merged into the liquidity bucket; a separate robustness check is therefore advisable for the medium- to long-horizon reversion component.

This decomposition framework further refines the permanent/transitory dichotomy proposed by Hasbrouck [9]: the "transitory component" of the traditional framework is here disaggregated into three subcategories—liquidity volatility, mechanism-induced volatility, and transmitted volatility—each with a different generating mechanism and policy implication. Figure 22-1 compares the core characteristics of the four volatility sources along four dimensions: information content, duration, eliminability, and policy implications.

![Figure 22-1](./images/fig-22-1-en.png)

**Figure 22-1.** Comparison of the characteristics of the four volatility sources (drawn by the author)

As shown in Figure 22-1, mechanism-induced volatility is both information-free and highly eliminable, which makes it the primary target for institutional optimization. The core significance of this taxonomy is that it provides a unified framework for assessing the economic meaning of volatility: not all volatility is *bad*, and the key is to distinguish which volatility stems from price discovery and which from institutional friction.

### 22.1.4 The concept of a volatility budget

To understand the internal structural dynamics of aggregate volatility more intuitively, we introduce the analytical concept of a volatility budget. Building on the variance decomposition above, the volatility budget treats the total variance observed within a given time window as a 100% budget pool, in which the four sources occupy shares corresponding to their respective variance contributions (with a reasonable allocation of the interaction term). We adopt the convention of allocating the interaction term symmetrically and in full to both of the sources involved—that is, each pairwise interaction product is counted once in full within each of the two sources it involves. Other allocation schemes would change the absolute share of each source but would not alter the qualitative conclusion, described below, that the volatility budget switches with the market regime. Formally, the budget share of the $j$-th volatility source is defined as:

$$w_j = \frac{\sum_{i=1}^{N} (r_i^{j})^2 + \sum_{i=1}^{N} \sum_{k \neq j} r_i^{j} r_i^{k}}{RV_T}, \quad j \in \{info, liq, mech, cond\}$$

Here $w_j$ is a signed share of the variance contribution: it satisfies the normalization $\sum_j w_j = 1$, but individual terms need not fall within the interval $[0, 1]$—when the interaction term between one source and the others is net negative, $w_j$ may arithmetically be less than zero or greater than one.

This relative-share perspective allows the analyst to look beyond the surface of absolute volatility figures and discern the microstructural health of the market under different operating regimes (note that when the interaction term is negative—for example, when informational and liquidity shocks are negatively correlated—the budget share $w_j$ of an individual source may arithmetically be less than zero, in which case the "100% budget pool" is merely a heuristic expression). The allocation of the volatility budget is not fixed; it undergoes a systematic structural switch as the market evolves from calm to crisis, and its detailed mechanism and empirical features are analyzed in Section 22.6. Figure 22-2 uses a stacked area chart to present the structural evolution of the volatility budget from calm periods to crisis periods.

![Figure 22-2](./images/fig-22-2-en.png)

**Figure 22-2.** State dependence of the volatility budget (illustrative volatility budget: drawn by the author, with proportions referencing Nguyen (2024) [10] and Dimpfl & Peter (2021) [11]; not recomputed from measured data)

As shown in Figure 22-2, the budget share of informational volatility is sharply compressed as the market moves from calm to crisis, while liquidity and mechanism-induced volatility expand dramatically until they cover almost the entire budget pool. This structural reversal conveys the central thesis of this chapter: under extreme market conditions, the economic meaning of price movements undergoes a fundamental change.

## 22.2 Informational volatility

As previewed by the framework in Section 22.1.3, informational volatility is the price change—triggered by the arrival of new information and its digestion by the market—that represents a permanent revaluation of an asset's fundamental value, in contrast to the temporary volatility caused by liquidity frictions. In the four-source framework, "information" is defined broadly, consistent with the EMH: any signal that alters market participants' price expectations—macroeconomic data, on-chain anomalies, and even social-media narratives and opinion-leader statements—falls into this category, so that price changes driven by retail fear of missing out (FOMO) or by herding also count as informational volatility. The persistence of narrative-driven price changes, however, lies between that of fundamental information shocks (highly permanent) and pure liquidity shocks (reversion on the order of minutes): such changes may revert significantly within several days to several weeks, constituting a known gray area of the four-source taxonomy. In perpetual futures markets, round-the-clock trading and the nonlinear amplification of information shocks by high leverage mean that informational volatility often takes on a sharper and more rapid pattern of response.

### 22.2.1 Macro information shocks

As crypto assets have been absorbed into global macro asset allocation as an alternative investment, the pricing of Bitcoin and other major crypto assets has fallen increasingly under the influence of traditional macroeconomic variables. Releases of core macroeconomic indicators—the interest-rate decisions of the Federal Open Market Committee (FOMC), the consumer price index (CPI), and nonfarm payroll data—constitute the largest and most frequent sources of external information shocks in the crypto perpetual futures market. By altering expectations for the risk-free rate and the state of U.S. dollar liquidity, these macro data directly reshape investors' required risk premium on high-risk assets. Empirical research shows that during the window in which the FOMC releases a monetary-policy statement, the realized volatility of Bitcoin perpetual futures rises sharply intraday in a pulse-like pattern [13]; research based on daily-frequency data, by contrast, finds that the overall effect of such macro announcements on the Bitcoin price is relatively limited [12], indicating that this pulse effect is concentrated mainly at the intraday high-frequency level. This sharp rise in volatility is not a random walk but a direct expression of the market repricing its expectations for future funding costs.

Under the shock of these macro events, the response mechanism of the perpetual futures market often exhibits extremely high sensitivity and immediacy. Because macro data releases usually follow a clear schedule, market participants make substantial position adjustments and engage in expectation games before the data are published. Once the actual figures deviate from the market's consensus expectation—producing a so-called macro "surprise"—the perpetual futures market generates enormous trading volume within a short time. At this point, as the stop-loss or take-profit orders of leveraged traders are triggered en masse, liquidity providers, seeking to avoid adverse-selection risk, tend to rapidly withdraw their limit orders, causing order-book depth to thin instantaneously. This deterioration in liquidity conditions, superimposed on the information shock, jointly amplifies the instantaneous magnitude of the price movement. Research indicates that when the CPI comes in above expectations, the instantaneous volatility of the Bitcoin price can reach several times its normal level, and this volatility is largest in the first few minutes before gradually decaying [13].

Beyond traditional macroeconomic data, crypto markets also face a distinctive type of macro information shock: the evolution of regulatory policy and compliance events. The progress of the U.S. Securities and Exchange Commission (SEC) in approving spot exchange-traded funds (ETFs), and regulatory classification announcements on crypto assets by major jurisdictions, can trigger sharp swings in market sentiment. Unlike regularly scheduled economic data, the timing of such regulatory events is often unpredictable, and their impact on industry fundamentals is more far-reaching. For example, during the period in which expectations for the approval of a spot Bitcoin ETF gradually accumulated and finally materialized, the volatility of the crypto market exhibited complex dynamics, encompassing both expectation-driven trending volatility and violent "sell-the-fact" swings after the event materialized [14]. Unlike the mainly pulse-like shocks of regularly scheduled macro data, the informational volatility triggered by regulatory policy is often not a discrete event but a persistent state of uncertainty: even after an ETF has been approved, the SEC's ongoing regulatory authority and its cautious stance toward future products still constitute a long-standing, unresolved risk. Figure 22-3 shows the typical dynamic pattern of intraday realized volatility for BTC perpetual futures before and after a macro information shock.

![Figure 22-3](./images/fig-22-3-en.png)

**Figure 22-3.** Intraday realized volatility of BTC perpetual futures before and after a macro information shock (mechanism illustration: depicting the impulse-decay pattern of 5-minute realized volatility in the ±2 hours around macro events such as FOMC meetings; a synthetic representative curve, not measured Binance data)

As shown in Figure 22-3, realized volatility jumps in a pulse at the instant the macro data are released, peaks within a few minutes, and then gradually decays. This "shock-decay" profile is consistent with the characteristics of informational volatility: the rise in volatility is a necessary accompaniment to the price-discovery process in which the market absorbs new information and completes its repricing.

### 22.2.2 On-chain information and idiosyncratic events

Unlike traditional financial markets, which rely on corporate earnings reports and audited disclosures, the crypto-asset market possesses a unique public information layer: the blockchain ledger. On-chain data—large token transfers, changes in the number of active addresses, shifts in miner balances, and changes in total value locked (TVL) in decentralized finance protocols—constitute a form of "quasi-public inside information" unique to crypto markets. Although these data are publicly visible to all market participants, the barriers and latency of data interpretation mean that investors able to monitor and interpret these on-chain anomalies in real time often gain an informational edge. When a whale address transfers a large amount of Bitcoin into a centralized exchange (CEX), the market typically reads this as a potential selling signal, triggering defensive short-selling and downward price volatility [15]. Volatility triggered by such on-chain anomalies is, in essence, the market digesting the supply-demand imbalance risk implied by this "quasi-public information."

Although this book focuses on the order-book markets of centralized exchanges, protocol-level data changes in DeFi are also transmitted through arbitrage channels to the CEX perpetual futures market and have indirect but significant explanatory power for the price volatility of the relevant governance tokens. As a core metric of protocol liquidity and user trust, TVL exhibits a strong dynamic causal relationship between its trend and token prices. Research finds that when a protocol's TVL breaks through a particular threshold or shows accelerating growth, it often signals strengthening market confidence in that protocol, which in turn attracts more capital inflows and drives up the token price [16]. Conversely, if a protocol suffers a security exploit or large-scale capital outflows, a sharp drop in TVL will trigger panic selling. In these extreme scenarios, the perpetual futures market often becomes the primary venue for risk hedging. Traders use the short-selling mechanism of perpetual futures to hedge their spot exposure or to speculate on the short side, which sharply amplifies, in the derivatives market, the volatility triggered by project-idiosyncratic events.

For small- and mid-cap altcoins, project-idiosyncratic events—such as a mainnet upgrade, a change to the token-economic model, or turnover among core team members—are often the central drivers of price volatility. Because these assets have relatively poor liquidity and lack a mature market-maker ecosystem, any material fundamental information can cause a violent revaluation of the price. In the perpetual futures market, the funding rates of these assets often deviate to extremes before a major event, reflecting the market's strong directional expectations. When the event finally materializes, if the outcome fails to meet the market's high expectations, it often triggers a so-called short squeeze or a concentrated closing of long positions, causing an enormous price swing in a very short time. This volatility, driven by project-idiosyncratic events, reveals how crypto markets price assets under information asymmetry and liquidity stratification.

### 22.2.3 Informed trading and volatility decomposition

The micro-mechanism of informational volatility must be parsed at the level of trading behavior. As discussed in Chapter 13, price discovery is a core function of market microstructure, and informed traders play a central role in this process. In the four-source decomposition framework, informed trading is the main micro-mechanism that generates informational volatility: the strategic trading behavior of informed traders incorporates private information into prices and drives prices toward fundamental value, and the price changes they contribute constitute the permanent component of informational volatility. According to the classic Kyle model, informed traders trade strategically using their informational advantage, a process that inevitably triggers price changes [17]. Complementing this, the probability of informed trading (PIN) model proposed by Easley and colleagues [18] provides a direct tool for quantifying the intensity of informed trading within a given period. A rise in volatility is therefore not always a sign of market inefficiency; in many cases it is a necessary stage in which prices reflect new information and achieve more accurate pricing.

In perpetual futures markets, the price-impact effect of informed trading is significantly amplified by leverage. When informed traders anticipate that an asset's price will change, they tend to use the high-leverage feature of perpetual futures to maximize the returns to their informational advantage. This leveraged informed trading produces enormous directional order flow that rapidly consumes liquidity on the order book. Facing such strong one-sided order flow, market makers realize that they may be trading against counterparties with an informational advantage—that is, they face severe adverse-selection risk. To protect themselves, market makers quickly adjust their quotes, widen the bid-ask spread, and reduce the number of resting orders [19]. This defensive behavior at the microstructural level further intensifies price volatility. Informational volatility in perpetual futures markets therefore contains not only the price revaluation brought by the information itself but also the liquidity premium caused by adverse selection.

From the perspective of volatility decomposition, total volatility can be split into permanent volatility driven by information and transitory volatility driven by microstructure noise. Informed trading contributes mainly to the permanent-volatility component, because these trades reflect changes in an asset's fundamental value, and this price change persists over the long run once the information is fully digested by the market. By contrast, volatility caused by liquidity shocks, order-book imbalances, or irrational speculation usually mean-reverts within a short time and belongs to transitory volatility. In crypto perpetual futures markets, because market sentiment is extremely sensitive and speculative forces are active, the share of transitory volatility tends to be high. When key information is released or a major event occurs, however, the share of permanent volatility rises significantly, indicating that the price-discovery process is dominating market dynamics.

### 22.2.4 Leverage amplification and round-the-clock continuity

Informational volatility in the crypto-asset perpetual futures market has two salient features: a leverage-amplification effect and the absolute continuity of information arrival. First, the leverage mechanism has both an accelerating and an amplifying effect on informational volatility. Leverage itself does not directly create new information; what it amplifies is the magnitude by which an information shock is converted into a price change in the market. But leverage also indirectly affects the incentive structure for information production: when an informational edge can be amplified into higher returns through high leverage, the returns to resources invested in information-discovery activities—such as on-chain data monitoring and macro forecasting—are higher, which attracts more informed traders into the market and indirectly raises the level of informational volatility. When information such as macro data or on-chain anomalies arrives, the presence of highly leveraged traders enables the same amount of capital to generate order flow several or even dozens of times larger. When this amplified order flow hits the order book, it causes deeper price slippage. More importantly, the initial price movement can easily touch the liquidation lines of highly leveraged positions, triggering chain forced liquidations. The passive market orders produced by this forced-liquidation mechanism form a positive-feedback loop with the initial information-driven orders, causing extreme price movements that far exceed the fundamental change implied by the information itself [20].

Second, the round-the-clock trading feature of crypto markets fundamentally changes the temporal distribution of information arrival and digestion. In traditional stock or futures markets, fixed trading sessions and market closures mean that information accumulated during non-trading hours is often released in a concentrated fashion at the open, in the form of gap-ups or gap-downs, causing abnormally high volatility in the early open. The round-the-clock nature of the crypto perpetual futures market allows information to be priced in instantly at any moment. Although this continuity eliminates overnight-gap risk, it also means that market liquidity varies significantly across the hours of the day. Empirical observation shows that intraday volatility in crypto markets exhibits a pronounced geographic time-zone pattern and intraday seasonal structure, with the handover points among the major Asian, European, and North American trading sessions often being peaks of volatility and trading volume [21][13]. When major information arrives during a period of relatively thin market liquidity (such as North American weekends or late at night), even a small information shock can trigger enormous price volatility for lack of sufficient absorbing orders.

Moreover, the degree of information asymmetry in perpetual futures markets is, in many dimensions, higher than in traditional financial markets. Although on-chain data provide unprecedented transparency, this transparency is structural. Institutional investors and quantitative market makers possess powerful data infrastructure and algorithmic capabilities that let them parse on-chain anomalies, exchange fund inflows and outflows, and minute changes in macro data within milliseconds, and quickly establish positions in the perpetual futures market. Retail investors, by contrast, are often at the tail end of information reception and processing. This asymmetry in technology and resources enables informed traders to extract information rents more efficiently in perpetual futures markets, while retail investors more readily become passive providers of liquidity. This deep information asymmetry further reinforces the perpetual futures market's tendency toward one-sided volatility under information shocks and the steepness of its price discovery.

### 22.2.5 Measuring informational volatility

To isolate and measure informational volatility quantitatively, financial econometrics offers a set of rigorous analytical tools, the most central concept being permanent price impact. Permanent price impact measures the net change in price relative to the pre-trade level after a sufficiently long time has elapsed following a trade. This measure rests on a basic assumption: if a trade contains undisclosed private information, that information will ultimately be fully absorbed by the market, causing a permanent change in the asset's fundamental value; conversely, if the trade is driven merely by liquidity needs or noise trading, the price change it triggers will ultimately mean-revert. Within the framework of a vector autoregression (VAR) model, researchers can jointly model trade flow and quote updates to isolate the long-run cumulative impact of trade innovations on price, thereby precisely quantifying the informational-volatility component in the market [9].

In an environment where the same asset trades in multiple markets, assessing the contribution of different markets to price discovery and informational volatility is a key microstructure question. The Hasbrouck information-share model provides a classic solution to this problem. Based on the common efficient-price random-walk component in a cointegration system, the model defines each market's information share by computing the proportion of the variance of that common stochastic trend contributed by each market's innovations [6]. In the crypto-asset space, mainstream assets such as Bitcoin trade simultaneously on multiple spot exchanges and derivatives platforms. Studies applying the information-share model consistently show that lightly regulated offshore perpetual futures markets occupy a dominant position in Bitcoin price discovery. With their high leverage, low trading frictions, and vast liquidity, these platforms attract large numbers of informed traders, and their price innovations often lead compliant spot markets and regulated traditional futures markets [22].

Beyond high-frequency measures at the microstructural level, the event-study method is also a commonly used macro tool: by defining a specific information event (such as an FOMC decision or a protocol being hacked) and examining the abnormal returns and abnormal volatility within the event window, one can assess the market impact of the information shock. In the crypto perpetual futures market, macro monetary-policy tightening typically triggers a systematic rise in market-wide volatility, whereas a negative security event targeting a specific decentralized finance protocol triggers local extreme volatility in that token and may generate cross-asset volatility premia through nested capital pools. In addition, as mentioned in Section 22.2.3, the PIN model [18] provides an independent measurement perspective on the share of informational volatility by modeling the arrival rates of buy and sell orders. Combining permanent price impact, information share, PIN estimation, and event studies can fully describe the path by which information is converted into price volatility in perpetual futures markets. Figure 22-4 presents, using actual data, the decomposition of a price shock in BTC perpetual futures into its permanent and transitory components.

![Figure 22-4](./images/fig-22-4-en.png)

**Figure 22-4.** Decomposition of a BTC perpetual futures price shock into permanent and transitory components (mechanism illustration: a representative pattern based on the vector-autoregression (VAR) decomposition logic of Hasbrouck (1991) [9], not recomputed from measured data)

As shown in Figure 22-4, the permanent component (informational volatility) persists over the long run after the shock, while the transitory component rapidly decays back toward zero. The separation of the two shows that a single price change in perpetual futures contains both information and noise, which is precisely the microstructural empirical basis for this chapter's four-source framework.

## 22.3 Liquidity volatility

In contrast to the permanent nature of informational volatility, and as previewed in Section 22.1.3, liquidity volatility is a temporary price shock produced by insufficient market depth or by adjustments in market makers' quotes: it contains no information about an asset's true value, and the price tends to revert after deviating, simply because the order size exceeds the immediate liquidity the market can provide at the time. Even in calm moments when no new information arrives, executing an order on an illiquid order book produces such a shock. In perpetual futures markets, because of the endogenous nature of liquidity, this volatility manufactured by insufficient liquidity has a strong self-amplifying effect.

### 22.3.1 Illiquidity and spurious volatility

In an ideal frictionless market, price changes should fully reflect the arrival of new information. In the real crypto-trading environment, however, prices frequently jump violently because of pure supply-demand imbalances. This price change, which is not information-driven, is termed liquidity-induced volatility—that is, non-informational temporary volatility—in market-microstructure theory. Understanding this type of volatility hinges on recognizing the nonlinear relationship between price impact and order-book depth.

Consider a stylized example (actual slippage varies with the asset, the time of day, and the state of the order book; for BTC perpetual futures, the slippage of a $1 million market order is typically on the order of single-digit basis points during peak-liquidity hours, but can reach tens of basis points during the low-liquidity hours of late night). When a $1 million sell order enters the market, the price impact it causes depends entirely on the thickness of the order book at that moment. If this order lands on an order book with $10 million of bid depth, it may consume only the top few price levels, causing a price decline of about 20 basis points. But if the same sell order is executed on an order book with only $1 million of total depth, it will penetrate many price levels, causing a decline of 80 basis points or more. In both scenarios, the act of selling itself conveys no different fundamental information; solely because of the difference in absorptive capacity, the price behavior differs sharply. The core parameter $\lambda$ of the Kyle model (the price-impact coefficient) precisely characterizes this mapping between order flow and price change [17].

In the crypto-asset perpetual futures market, such spurious volatility is often more pronounced than in the spot market. The high-leverage feature of perpetual futures means that a small amount of initial capital can drive an enormous notional trading volume, which in practice amplifies the impact of a single order on the order book. At the same time, perpetual futures market makers face more complex risk-management constraints when providing liquidity. When the market moves in one direction or expected volatility rises, market makers, to protect their own positions, tend to rapidly withdraw resting orders or significantly widen the bid-ask spread, causing the order book to become abnormally thin precisely when liquidity is most needed. This instantaneous withdrawal of liquidity depth means that even a normal-sized market order can easily trigger sharp price slippage, producing large outliers in the price series.

The core criterion for distinguishing this liquidity volatility from genuine informational volatility lies in the trajectory of the price after the shock. Price changes driven by genuine information are often permanent: the price jumps or drops to a new equilibrium level and stabilizes there, because market participants have re-assessed the asset's value in light of the new information. By contrast, spurious volatility caused by insufficient liquidity is usually temporary. Once that large, liquidity-exhausting order has been executed, if no follow-on orders appear, market makers and other arbitrageurs will quickly notice that the price has deviated from fair value, and will therefore re-supply liquidity and push the price back toward its pre-shock level. This "shock-reversion" pattern is the typical signature of liquidity volatility. Figure 22-5 illustrates this mechanism along two dimensions: the decisive influence of order-book depth on the magnitude of price impact, and the fundamental difference in the post-shock trajectories of informational and liquidity shocks.

![Figure 22-5](./images/fig-22-5-en.png)

**Figure 22-5.** The relationship between order-book depth and price impact (representative mechanism illustration: depicting the relationship between market-order impact and order-book depth for BTC perpetual futures under different liquidity conditions; not measured Kaiko snapshot data)

As shown in Figure 22-5, when order-book depth is insufficient, the impact of the same order flow is amplified nonlinearly; an informational shock follows an "L-shaped" permanent path, whereas a liquidity shock follows a "V-shaped" temporary reversion path. This difference in post-shock trajectory provides an operable identification strategy for empirically distinguishing the two types of volatility.

### 22.3.2 Market-maker retreat and the volatility positive-feedback loop

Liquidity volatility does not always appear in an isolated, random form. In extreme crypto-market conditions, liquidity volatility often exhibits a highly structured self-amplifying mechanism. The root of this mechanism lies in the endogenous nature of market liquidity: liquidity is not only a result of price volatility but also a cause of further price volatility. In Chapter 20, we examined the formation of the liquidity spiral in detail; here we need to shift our analytical perspective and re-examine this positive-feedback loop from the dimension of volatility.

In the traditional finance framework, volatility is usually treated as an exogenous risk parameter. In crypto markets, however, volatility is to a large extent an endogenous variable. When the market experiences a slight price movement because of some initial random shock, this movement is captured by market makers' risk-management models. To control inventory risk and adverse-selection costs, the rational response of market makers is to widen the bid-ask spread and reduce the number of resting orders at each price level. This collective retreat by market makers directly causes the order book to thin.

Once the order book thins, the market's capacity to absorb order shocks declines significantly. At this point, even normal-sized trade flow triggers larger price slippage than usual on the thin order book. These price jumps produced by slippage statistically manifest as a further rise in realized volatility. This rise then feeds back to market makers' risk models as a new input signal, prompting them to cut liquidity supply further. This loop—in which rising volatility causes liquidity to dry up, and drying-up liquidity in turn produces greater volatility—is the core mechanism of self-fulfilling non-informational volatility [23][24].

In this positive-feedback loop, the liquidation mechanism unique to perpetual futures acts as an accelerator. When the price slides violently on a thin order book, it can easily touch the liquidation lines of highly leveraged positions. The liquidation engine is forced to throw unconditional market orders onto the market, and these forced orders continue to penetrate multiple price levels on an order book that has already lost the protection of market makers, triggering an even larger price decline. In the market turmoil of October 2025, this volatility spiral, woven together by liquidity exhaustion and cascading liquidations, triggered more than $19 billion (about $19.37 billion) in visible forced-liquidation notional within roughly 24 hours, constituting the largest single-day deleveraging event in history (the most violent shock was concentrated within a single trading day, while the full deleveraging window lasted about 36 hours; see Section 22.6.4 for details) [25]. In this process, the overwhelming majority of the price decline was not a reflection of deteriorating fundamentals but a direct reflection of a liquidity crisis in the form of price movement.

### 22.3.3 Time structure

Crypto markets trade around the clock without interruption, but liquidity depth and the capacity to absorb shocks are not equal at every moment. In fact, the liquidity volatility of crypto assets has a highly regular time structure. This time structure is shaped mainly by the alternating activity of traders in different global time zones, the operating cycles of algorithmic trading programs, and the specific settlement mechanisms of derivatives markets.

Empirical research shows that trading activity and liquidity depth in crypto markets exhibit a pronounced bell-shaped curve over the course of a day. Around 16:00–17:00 UTC—the overlap between the tail of the European session and the early New York session—market liquidity reaches its daily peak. At this time, the world's major institutional investors and high-frequency trading firms are all actively participating in the market, order-book depth reaches its highest level of the day, the market's capacity to absorb large orders is strongest, and spurious volatility caused by insufficient liquidity falls to its lowest point [26].

During the midnight-to-early-morning hours in UTC, and especially late at night in the Asian session, market liquidity falls into a deep trough. Within this liquidity-trough window, the activity of mainstream European and American market makers drops sharply, and the depth of the order book may be only one-third of its peak level or even less. It is precisely this periodic liquidity exhaustion that makes crypto markets exceptionally fragile at particular times. Historical data reveal a striking phenomenon: most historical crypto-market flash crashes have occurred in this late-night window when liquidity depth is at its daily low—though, as participation by Asian markets (particularly South Korea and Hong Kong) increases, the time-zone distribution of flash crashes is evolving; for example, the crypto flash crash triggered by the unwinding of the yen carry trade in August 2024 occurred during the active hours of the Asian session. At these moments, even a sell order of no extreme size is enough to form a significant price gap on an order book that lacks depth, producing extremely high instantaneous volatility.

Beyond this intraday pattern created by time-zone alternation, derivatives settlement mechanisms also manufacture predictable price pulses at specific moments: in the run-up to a funding-rate settlement or the expiry of a dated futures contract, arbitrageurs and directional traders reposition en masse to secure a favorable settlement price or to avoid a funding payment, triggering sawtooth-shaped volatility around settlement. Such pulses, in essence, are mechanism-induced volatility rather than liquidity volatility, and their full generating mechanism is detailed in Section 22.4.2. Figure 22-6 uses a two-panel chart to present the empirical features of the above intraday periodic pattern.

![Figure 22-6](./images/fig-22-6-en.png)

**Figure 22-6.** Intraday liquidity and volatility patterns in the crypto market (intraday-pattern illustration: representative hourly patterns of realized volatility and order-book depth, with the pattern referencing Brauneis et al. (2025) [26]; not recomputed hour by hour from measured data)

As shown in Figure 22-6, realized volatility peaks at UTC 16:00–17:00 (the European-American session overlap), while order-book depth falls to its lowest from UTC midnight to early morning. This key asymmetry—that the volatility peak and the liquidity trough do not coincide—means that a unit shock in the late-night hours causes a larger price deviation, making this window a high-incidence period for flash-crash events.

### 22.3.4 The asset dimension gradient

The degree of liquidity volatility not only exhibits regular fluctuations along the time axis but also varies enormously and structurally across assets. If we rank the entire crypto market by market capitalization and liquidity, we can clearly observe a liquidity gradient extending from absolutely mainstream assets such as Bitcoin all the way to tiny-cap altcoins. This liquidity gradient corresponds to a volatility gradient.

As the market's foundational assets, Bitcoin and Ethereum possess the deepest liquidity pools, the most active market-maker communities, and the most complete derivatives ecosystems. On the order books of these assets, orders on the order of tens of millions of dollars often produce only tiny price movements. In the aggregate volatility of mainstream assets, therefore, informational volatility driven by fundamental information, macroeconomic expectations, and overall market sentiment dominates, while spurious volatility caused purely by insufficient liquidity accounts for a relatively small share.

As we move down the market-cap ladder into the range of mid-cap and even tiny-cap altcoins, however, the situation reverses completely. The order books of these assets are usually noticeably thin, with wide bid-ask spreads and no professional market makers providing continuous liquidity support. In these markets, insufficient liquidity is no longer an occasional extreme event but a normalized market feature. These small-cap tokens therefore exhibit volatility levels far higher than those of mainstream assets.

More critically, in these high-volatility altcoins, spurious volatility dominates absolutely. Because liquidity is extremely scarce, any buy or sell order of even modest size—regardless of whether it carries a genuine informational advantage—produces a large price impact on the order book, triggering sharp price jumps or declines. This extreme liquidity sensitivity fills the price trajectory of small-cap tokens with noise and random-walk characteristics. For these assets, their high volatility is to a large extent a risk compensation for their poor liquidity conditions—or, put differently, a premium the market demands for bearing their illiquidity friction [27]. Figure 22-7 shows the correspondence between the liquidity gradient and the volatility gradient from high-cap to low-cap assets.

![Figure 22-7](./images/fig-22-7-en.png)

**Figure 22-7.** Asset liquidity gradient and volatility gradient (gradient values are a representative illustration, not actually pulled from CoinGecko: market capitalization and the Amihud illiquidity ratio [28] reference the grouping of the top 50 crypto assets by CoinGecko market-cap ranking; the informational/liquidity volatility split shown in the figure is an illustrative estimate based on the four-source framework, not produced directly from these data)

As shown in Figure 22-7, aggregate volatility rises steeply as assets move from mainstream coins to mid- and low-cap altcoins; its composition (split illustratively in the figure, conveying only a qualitative gradient rather than precise shares) reverses accordingly: mainstream assets are dominated by informational volatility, whereas the share of liquidity volatility for low-cap assets expands dramatically, even exceeding informational volatility. This means that the economic meaning carried by the volatility of different market-cap tiers differs fundamentally: the volatility of high-cap assets mainly reflects information, while the volatility of low-cap assets mainly reflects friction.

### 22.3.5 Measurement methods

To handle liquidity volatility effectively in quantitative trading and risk management, we need a rigorous measurement system that isolates this spurious volatility from aggregate price movements. In the market-microstructure literature, scholars have developed a variety of metrics to capture and quantify this volatility arising from liquidity friction.

The most direct measurement method is to quantify the transitory price impact. This method tracks the high-frequency price trajectory after a large trade and decomposes the initial price deviation into a permanent component and a transitory component. The permanent component represents the information content contained in the trade, whereas the transitory component—the portion of the price that mean-reverts after the shock—precisely measures the magnitude of the spurious volatility caused by insufficient liquidity. By computing the proportion of this transitory shock in the aggregate price movement, and the time required for the price to complete its mean reversion (that is, the reversion rate of volatility), we can clearly depict the liquidity resilience of a specific asset in a specific period.

Over more macro daily or longer horizons, the Amihud illiquidity ratio is a widely adopted classic metric. This ratio measures the magnitude of price movement induced per unit of trading volume by computing the ratio of an asset's absolute return to its dollar trading volume [28]. In crypto markets, a high Amihud ratio means that very little trading volume can trigger violent price movement, which strongly suggests that the asset's volatility is driven mainly by scarce liquidity. Empirical research confirms that, after controlling for other risk factors, the expected returns of crypto assets are significantly positively correlated with their Amihud illiquidity ratio, indicating that the market does indeed demand risk compensation for bearing this liquidity-induced volatility [29].

In addition, from the micro perspective of market makers, the realized spread is another key metric for measuring liquidity volatility. Unlike the quoted spread displayed on screen, the realized spread measures the profit that a market maker can actually capture after providing liquidity and holding the position for a period. In an environment of violent liquidity volatility, because of frequent price jumps and increased adverse-selection risk, market makers often find that their realized spread is far below the quoted spread, or even negative. This compression of the realized spread is the most direct signal that market makers perceive liquidity-volatility risk, and it is also the micro trigger that prompts them to withdraw liquidity and thereby set off the volatility positive-feedback loop.

With these fine-grained measurement tools, we can isolate, from aggregate price movements, the spurious volatility manufactured purely by market-structure friction. This identification capability is a precondition for building robust trading strategies and risk-management models, and it helps distinguish information-driven permanent price adjustments from temporary price shocks caused by insufficient liquidity. Liquidity-measurement methods tailored to the high-frequency crypto environment have continued to evolve in recent years; methods such as high-frequency estimation of Kyle's lambda from tick-by-tick trade data and limit-order-book resilience metrics can capture the dynamic features of liquidity volatility on shorter time scales, providing finer tools for the empirical application of the four-source framework.

## 22.4 Mechanism-induced volatility

Price volatility in perpetual futures markets stems not only from updates to fundamental information or temporary shortages of liquidity but, more profoundly, from the driving force of its underlying institutional design. As previewed in Section 22.1.3, mechanism-induced volatility is the price movement created by the market rules themselves and unrelated to new information; its distinctive sources—the leveraged liquidation engine, the funding-rate settlement cycle, and the mark-price smoothing mechanism—while maintaining system solvency and anchoring to the spot price, inevitably inject additional noise into the market, making it the core source of the "excess volatility" of perpetual futures. This section focuses on institutional design at the level of algorithms and rules; it does not cover volatility triggered by operational-risk events such as exchange outages, security attacks, or technical failures. Although such events can also manufacture extreme price movements unrelated to fundamentals, their suddenness and unpredictability make them better discussed within a risk-management framework than within a volatility-decomposition one.

### 22.4.1 Liquidation cascades

A liquidation cascade is not merely an amplifier of existing volatility; it is itself an independent source of volatility. When the market is relatively calm, a large directional trade may push the price only a slight initial distance—a movement that belongs to informational or liquidity volatility. If this tiny price movement touches the liquidation lines of highly leveraged positions, however, the liquidation engine automatically takes over these positions and enters the forced-liquidation process. Mainstream CEXs typically use a multi-tier liquidation mechanism rather than a simple market dump: the first tier attempts to liquidate via limit orders backed by the insurance fund, and only when limit orders cannot fill and the insurance fund is exhausted is auto-deleveraging (ADL) triggered. This multi-tier design effectively mitigates the market impact of liquidation in normal markets, but under extreme conditions it can still degrade into quasi-market forced execution. These liquidation orders exert further downward pressure on an already-weak order book, which in turn triggers deeper liquidations, forming a positive-feedback loop. In this process, the price moves violently without any new information arriving, and this additional decline is manufactured purely by the liquidation mechanism.

Liquidation-driven volatility exhibits a highly characteristic "V-shaped" or spike pattern in the price series, in sharp contrast to the "L-shaped" permanent adjustment of informational volatility. During a liquidation cascade, the price usually overshoots significantly and then, once the liquidation pressure is exhausted, quickly rebounds toward a level closer to fundamentals. This shape reflects the cost-insensitive nature of liquidation orders and their extreme consumption of short-term liquidity. In traditional futures markets, because leverage ratios are strictly limited, there is a margin-call buffer period, and price-limit (circuit-breaker) mechanisms provide protection, the scale and frequency of liquidation cascades are greatly compressed. By contrast, crypto perpetual futures markets generally allow extremely high leverage multiples and enforce instant forced liquidation, which makes liquidation cascades a normalized market phenomenon. Figure 22-8 compares the standardized price paths of liquidation-driven and information-driven events, presenting the fundamental difference in the post-event trajectories of the two types of volatility.

![Figure 22-8](./images/fig-22-8-en.png)

**Figure 22-8.** Comparison of the liquidation-driven V-shaped price path and the information-driven L-shaped price path (mechanism-comparison illustration: representative standardized price paths in the ±30 minutes around liquidation events and information events; not measured Coinglass data)

As shown in Figure 22-8, the "V-shaped" path of a liquidation cascade dives rapidly under the shock to an extreme position far below equilibrium, then partially rebounds toward a level closer to fundamentals; the "L-shaped" path of an informational shock, by contrast, stabilizes over the long run after the jump and does not undergo systematic mean reversion. The core of this difference is that liquidation orders contain no information about an asset's true value; their price deviation is purely a product of liquidity consumption and must therefore be corrected once liquidity is restored.

Extreme market events in history have repeatedly verified the destructive power of liquidation cascades as an independent source of volatility. In the collapse of the Terra ecosystem in May 2022, the depegging of the algorithmic stablecoin UST triggered panic across the entire crypto market. As the price of the Luna token plummeted, a large number of leveraged positions that relied on Luna as collateral were liquidated, and these liquidation orders further accelerated the price collapse, ultimately driving Luna's market capitalization to near zero within just a few days [30]. Even in periods of relatively stable macro conditions, crypto markets frequently experience violent volatility triggered by liquidation. For example, in the deleveraging event of October 2025 (whose scale and window are detailed in Sections 22.3.2 and 22.6.4), large-scale visible forced liquidations drove the prices of multiple assets into a deep decline before they rapidly rebounded [25]. These cases show that when leveraged positions are highly concentrated within a particular price range, even a tiny initial trigger is enough to set off a massive liquidation cascade, thereby manufacturing extreme volatility completely disconnected from fundamentals. In assessing the *eliminability* of mechanism-induced volatility, however, the fundamental purpose of the liquidation mechanism—maintaining system solvency—must be kept in view. Reducing the market impact of liquidation (for example, by adopting gradual liquidation or auction-based liquidation [31]) comes at the cost of increased liquidation latency, which under extreme conditions may leave the protocol with bad debt. Mechanism-induced volatility therefore cannot be entirely eliminated, and the goal of institutional optimization is to find the Pareto optimum between minimizing market impact and maximizing solvency.

### 22.4.2 Funding-rate settlement pulses

The funding-rate mechanism is a core design by which perpetual futures maintain their anchor to the spot price, but its periodic settlement process also injects predictable periodic volatility into the market. To push the perpetual futures price back toward the spot price, longs and shorts must periodically exchange the funding rate. Under a typical 8-hour settlement cycle, when the funding rate is positive and high, holders of long positions face pressure to pay a high fee at the settlement moment. To avoid this cost, a large number of long traders tend to close or reduce positions before settlement, thereby forming systematic selling pressure ahead of the settlement window. That said, the magnitude of the settlement pulse depends not only on the level of the funding rate but also on the degree of consensus among market participants about the future price direction. In a strongly one-sided market, even if the rate becomes extreme, the paying side may prefer to pay the rate rather than close out (because it expects the price to keep moving in its favor), in which case the settlement pulse is instead smaller. This concentrated trading, driven by the desire to avoid funding payments, produces a predictable downward price movement when the market direction is uncertain.

As the settlement moment approaches, this directional pressure gradually accumulates, causing the contract price to deviate from the spot price. Once settlement is complete, however, the motive to avoid the funding payment disappears, the selling pressure dissipates, and the price often rebounds quickly to the pre-settlement level. This price volatility triggered by funding-rate settlement contains no fundamental information whatsoever; it is purely pulse-like noise produced by institutional design. In periods when the funding rate is at extreme levels, the magnitude of this periodic pulse is significantly amplified, becoming a key force dominating the short-term market microstructure. Figure 22-9 uses a time-series chart to present the periodic pulse pattern of basis and volatility around funding-rate settlement.

![Figure 22-9](./images/fig-22-9-en.png)

**Figure 22-9.** Volatility pulses under the 8-hour funding-rate settlement cycle (periodic-mechanism illustration: depicting the representative pulse pattern of basis and realized volatility in the ±1 hour around each 8-hour settlement; a synthetic curve, not measured Binance data)

As shown in Figure 22-9, as each 8-hour settlement moment approaches, the concentrated position-closing by the high-rate paying side generates regular downward pressure, manifested as a systematic rise in basis and realized volatility before settlement and a return to normal within a few minutes after settlement. This highly predictable periodic pulse demonstrates the existence of the funding-rate settlement mechanism as an independent source of volatility—it periodically injects noise into the market without any change in fundamental information.

Cross-platform comparison across exchanges with different settlement frequencies can more clearly isolate the contribution of the funding-rate mechanism to volatility. On platforms that use the traditional 8-hour settlement cycle, price volatility around settlement exhibits a clear clustering feature. Some emerging decentralized exchanges (DEXs), or platforms that adopt continuous-settlement mechanisms, effectively smooth traders' avoidance behavior by dispersing funding payments into every block or into a finer time granularity. Theoretical models show that the funding rate is an algorithmic feedback rule rather than a passive transfer, and that its design parameters—such as settlement frequency and rate caps—affect the mean reversion of the basis and its tail risk [32]. Continuous settlement fundamentally eliminates the concentrated-trading motive brought by discrete settlement, thereby significantly reducing this type of mechanism-induced volatility. But continuous settlement may also introduce new problems in an on-chain environment: the tiny rate exchange in each block increases the opportunity for maximal extractable value (MEV) extraction and may generate mechanism-induced noise of a different form. Even so, funding-rate settlement pulses remain the source of mechanism-induced volatility with the greatest room for institutional optimization.

### 22.4.3 The lag effect of the mark-price mechanism

To guard against erroneous liquidations triggered by market manipulation and anomalous trades, perpetual futures platforms generally introduce a mark-price mechanism as the benchmark for computing unrealized profit and loss and triggering forced liquidation. The calculation of the mark price usually involves two steps. First, a price index is aggregated as a weighted composite of component prices from multiple external spot exchanges, with single-exchange prices that deviate too far from the median excluded. Second, a time smoothing such as an exponential moving average (EMA) is applied on top of the price index, together with a reasonable basis-adjustment term, so that the mark price can smoothly track the theoretical fair value of the perpetual futures in normal markets (see Chapter 14 for the full governance implications of the mark-price mechanism). This two-layer design makes the volatility of the mark price lower than that of the last traded price, effectively filtering short-term noise and reducing unnecessary liquidation events. In extreme conditions where the market moves rapidly in one direction, however, the exponential-moving-average nature of the mark price can produce lag risk.

When the true market price changes sharply, the smoothing algorithm causes the mark price to respond noticeably more slowly than the last traded price. This lag protects, in the short run, some positions that should have been liquidated, but it also allows systemic risk to build up unnoticed. Over time, when the mark price finally catches up with the true market price, those loss-making leveraged positions accumulated during the lag period face concentrated forced-liquidation risk. This dynamic of accumulation and release effectively compresses liquidation events that should have occurred in a dispersed fashion into an extremely short time window, thereby producing a larger and more forceful liquidation pulse when the lag ends.

Moreover, the mark-price mechanism itself constitutes a transmission channel for cross-market volatility. Because the mark price synthesizes data from multiple external exchanges, when one of the spot exchanges experiences an extreme price deviation due to liquidity exhaustion or a technical failure, this deviation is partially passed on to the perpetual futures market through the mark-price formula. If this deviation is enough to trigger the perpetual futures liquidation engine, it will manufacture, in the derivatives market, a spurious liquidation unrelated to that market's own fundamentals and liquidity conditions. Notably, the lag effect of the mark price takes on different forms in decentralized perpetual futures exchanges. The design of DEX perpetual futures has already diverged significantly: the mark price of oracle-based DEXs (such as GMX) relies entirely on oracle price feeds, traders act as price takers, and their lag risk comes from the oracle's update frequency, so that even a tiny deviation in the mark price can trigger a significant chain reaction [33]; the mark-price mechanism of order-book DEXs (such as Hyperliquid and dYdX v4) is closer to that of a CEX, but their lag risk comes mainly from sequencer latency rather than oracle latency. By comparison, a CEX's mark price is based on a weighted index of multiple spot exchanges plus a reasonable basis adjustment, and its degree of lag varies with the setting of the smoothing parameters.

### 22.4.4 Attribution of volatility differences

To quantify the scale of mechanism-induced volatility accurately, one can compare the volatility of the same underlying asset under different market structures. Take Bitcoin as an example: its spot market, dated futures market, and perpetual futures market share the same macro information inputs and fundamental expectations, yet the volatility characteristics of the three differ significantly. The volatility of the spot market consists mainly of informational volatility and a small amount of liquidity volatility, and can be regarded as the benchmark for an asset's intrinsic volatility. The dated futures market adds, on this basis, volatility from basis changes and a small amount of liquidation volatility under limited leverage. The perpetual futures market not only contains all the volatility components of the spot market but also superimposes more severe liquidity volatility and a large amount of mechanism-induced volatility.

The literature and market observation consistently show that the realized volatility of perpetual futures tends to be higher than that of contemporaneous spot and dated futures. This volatility premium—the excess volatility that Section 22.7 will analyze systematically—is caused mainly by the unique institutional design of perpetual futures, and its specific source attribution is detailed in Section 22.7.2. Figure 22-10 presents the source of this excess volatility by comparing the volatility levels and their composition for the same underlying asset under three different market structures.

![Figure 22-10](./images/fig-22-10-en.png)

**Figure 22-10.** Comparison of the volatility composition of the same underlying asset in the spot, dated futures, and perpetual futures markets (the RV bar heights for each market are a representative illustration, not recomputed from measured data, referencing the contemporaneous daily realized volatility of BTC on Coinbase spot, CME dated futures, and Binance perpetual futures, 2023–2025; the four-source composition split within each market is an illustrative estimate based on this chapter's framework)

As shown in Figure 22-10, volatility rises successively from spot (dominated by the informational component, the lowest benchmark), to dated futures (adding limited basis and a small amount of liquidation volatility), to perpetual futures (superimposing the mechanism-induced volatility of liquidation cascades and funding-rate settlement). Because the three markets share the same underlying asset and information set, this systematic difference can only be attributed to institutional design, providing natural-experiment-style evidence for quantifying the net contribution of mechanism-induced volatility.

This cross-market comparison is not only of theoretical significance; it also provides a natural-experiment method for isolating and measuring mechanism-induced volatility. When the markets face the same information shock, the perpetual futures market often exhibits a deeper decline and higher short-term volatility, a difference that cannot be explained by differences in fundamental information but can only be attributed to institutional friction. A horizontal comparison across platforms with different liquidation mechanisms and settlement frequencies can further confirm the significant role of institutional design in generating non-informational volatility. For example, platforms that adopt gradual liquidation rather than cliff-edge liquidation exhibit a markedly weaker volatility-amplification effect under extreme conditions.

### 22.4.5 Measuring mechanism-induced volatility

The core feature of mechanism-induced volatility is its high degree of rule dependence, which makes it largely predictable. To measure this portion of volatility accurately in empirical research, researchers usually adopt the event-window method. In the liquidation event-window method, isolating the excess volatility in the few minutes before and after a large-scale liquidation makes it possible to strip out the price movement caused purely by the liquidation engine's dumping. Similarly, funding-settlement event studies focus on the short window around the settlement moment, computing the volatility pulse that deviates from the norm within that period and classifying it as mechanism-induced volatility.

Beyond the event-window method, building a no-liquidation counterfactual model is another effective measurement approach. By simulating the theoretical price path in the absence of a forced-liquidation mechanism and comparing it with the actually observed price path, the difference between the two represents the net contribution of the liquidation mechanism to volatility. Together, these measurement methods reveal one fact: under extreme market conditions, mechanism-induced volatility not only accounts for a significant share of total volatility but may even become the decisive force dominating short-term price movements.

The predictability of mechanism-induced volatility has given rise to trading strategies that arbitrage institutional friction specifically. The certainty of funding-rate settlement times and the visibility of liquidation cascades on the order book allow some high-frequency traders to position themselves in advance. They profit from institutionally manufactured noise by providing liquidity before settlement to absorb the funding-avoidance selling pressure, or by buying against the trend when a liquidation cascade causes a price overshoot. In actual execution, however, this arbitrage faces significant constraints: during a liquidation cascade the exchange may trigger overload protection or restrict new position openings; the actual fill price of a contrarian buy may be far worse than expected because of slippage; and if the liquidation pressure does not subside as expected but continues to intensify, the losses on the contrarian position may far exceed the initial budget. Moreover, while this arbitrage dampens some mechanism-induced volatility, it may also, in extreme cases, accelerate the triggering of liquidations. From the perspective of market design, clearly distinguishing and quantifying mechanism-induced volatility carries important guidance for optimizing the liquidation logic of perpetual futures, improving the frequency of funding-rate settlement, and enhancing the robustness of the mark price.

## 22.5 Transmitted volatility

The crypto-asset perpetual futures market is not a closed, independent system; its volatility stems not only from internal microstructure or supply-demand imbalances but also, and more so, from the continuous influx of external shocks. As previewed in Section 22.1.3, transmitted volatility is the spillover effect that spreads in through cross-market arbitrage and cross-margin mechanisms; in essence it is a form of second-hand volatility, highly contagious and strongly cross-correlated, with that correlation often approaching one when a crisis erupts. This volatility includes both inter-asset spillover within the crypto market and the macro transmission of fundamental changes in traditional financial markets.

### 22.5.1 Spillover effects within the crypto market

Within the crypto market, the transmission of volatility exhibits a highly structured and hierarchical network feature. When the market encounters a sudden shock, volatility is not evenly dispersed across all assets but follows specific transmission paths. As the crypto asset with the largest market capitalization and deepest liquidity, Bitcoin is usually the core hub of this volatility-transmission network. Research shows that Bitcoin is, in most periods, a major net transmitter of volatility connectedness, passing its own volatility shocks to Ethereum and other smaller-cap crypto assets through trading pairs and arbitrage mechanisms [34]. This transmission is especially rapid in the perpetual futures market, often completing cross-asset volatility co-movement within minutes. Under a unified-margin system, moreover, the transmission path is not one-directional. When a trader holds altcoin perpetual futures positions with BTC as collateral, the liquidation of the altcoin triggers the forced sale of the BTC collateral, thereby transmitting the altcoin's volatility back to BTC. This reverse-transmission path means that BTC can also become a net receiver of volatility in extreme crises, challenging its positioning as a pure "net transmitter."

Ethereum acts as a relay node in this network. As DeFi and the non-fungible token ecosystem have flourished, Ethereum's role in volatility transmission has grown, and in certain periods it has even surpassed the spillover of Bitcoin [35]. From Bitcoin to Ethereum, and from Ethereum on to numerous altcoins, this hierarchical transmission constitutes the basic skeleton of internal volatility spillover in the crypto market. Figure 22-11 uses a directed network diagram to visualize the volatility-spillover relationships among major crypto assets.

![Figure 22-11](./images/fig-22-11-en.png)

**Figure 22-11.** The crypto-market volatility-spillover network (the network is a conceptual illustration, not a computed DY matrix: the method references the DY spillover-network framework of Yi et al. (2018) [34] and Diebold & Yilmaz (2012) [36], depicting the representative spillover structure among BTC, ETH, and the top 10 altcoins by market capitalization)

As shown in Figure 22-11, Bitcoin sits at the core hub, and the thickness of the arrows it emits (spillover intensity) is the greatest, confirming its status as a major net transmitter of volatility (note that Yi et al. (2018) [34] point out that although Bitcoin is a major net transmitter, it is not the sole dominant one, and some small-cap coins can also become significant net transmitters in particular periods); Ethereum is a secondary hub, while the peripheral mid- and low-cap assets mainly play the role of receivers, with most of their volatility changes traceable to spillover from the core assets. This star-hierarchical topology shows that volatility transmission is not evenly diffused but has clear directionality and hierarchy.

In calm market periods, the connection strength of this transmission network is relatively moderate, and each asset can still maintain some degree of independent pricing. When a crisis occurs or an extreme volatility event appears, however, the transmission strength increases sharply and nonlinearly. Empirical analysis confirms that under a high-volatility regime, the spillover effect among crypto assets is significantly enhanced, exhibiting strong regime dependence [37]. In a panic sell-off, the high-leverage feature of the perpetual futures market accelerates the cascading liquidation of margin, causing the volatility of all crypto assets to spike in sync; a once-diversified portfolio instantly loses its protective value, and the entire market moves in lockstep.

### 22.5.2 BTC dominance and beta volatility

In assessing the relative volatility of crypto assets, the market often borrows the beta-coefficient concept from traditional finance. Altcoins typically exhibit extremely high-beta characteristics relative to major assets, meaning they are exceptionally sensitive to price changes in the core assets. When Bitcoin's volatility rises moderately, altcoins often experience much more violent price swings—that is, a small rise in Bitcoin's volatility is significantly amplified in altcoins.

This high-beta feature is not fixed; it is significantly time-varying and deeply influenced by the market cycle. In the exuberant phase of a bull market, as liquidity is ample and risk appetite runs high, the beta coefficients of altcoins tend to inflate, and investors tend to hold high-beta assets to capture excess returns. Conversely, in the capital-flight phase of a bear market, although altcoins still decline significantly, the absolute value of their beta coefficient may change structurally because of liquidity exhaustion and trading stagnation.

The widespread use of perpetual futures has greatly amplified this transmission effect. Because the spot liquidity of altcoins is usually poor, the perpetual futures market becomes the primary venue for speculators to express their views. When Bitcoin faces a macro shock or an internal structural sell-off, arbitrageurs and market makers rapidly adjust the risk exposure of the entire crypto-asset matrix. Highly leveraged positions in altcoin perpetual futures are extremely fragile in the face of such transmitted shocks, and even a slight movement in the benchmark asset is enough to trigger large-scale forced liquidation of altcoin contracts, producing transmitted volatility several times the size of the initial shock.

### 22.5.3 Arbitrage transmission between spot and perpetual

The price linkage between perpetual futures and the spot market is another core path of volatility transmission. These two markets are tightly linked through the funding-rate mechanism and the uninterrupted operation of arbitrageurs. In an ideal state, the spot market and the perpetual futures market should maintain price consistency, but in reality microstructural differences and the asymmetry of information inflow often cause a price gap—the basis—between them.

In the process of closing the basis, arbitrageurs inadvertently serve as an intermediary channel for volatility transmission. When the spot market experiences violent volatility because of external news or a large order, the rapid movement of the spot price widens the basis against the perpetual futures. At this point, statistical arbitrageurs and basis traders quickly step in, buying in the undervalued market and selling in the overvalued one. Although this cross-market high-frequency trading helps price discovery, it also effectively replicates and transmits the spot market's volatility to the perpetual futures market. Academic research indicates that the spot market is usually the main transmission source of volatility spillover and has a significant causal driving relationship with the derivatives market [38].

This transmission, however, is not always instantaneous and frictionless. Under extreme market conditions, structural arbitrage constraints cause transmission to lag. Research by the Bank for International Settlements (BIS) shows that the basis in crypto markets can sometimes reach extremely high levels, with annualized returns even exceeding 40%, reflecting that during extreme volatility the deployment of arbitrage capital is severely constrained by multiple execution frictions: blockchain confirmation delays (on-chain BTC confirmation takes 10 to 60 minutes) prevent cross-exchange fund transfers from completing instantly; exchanges freezing or restricting withdrawals under extreme conditions further block capital rebalancing; and margin calls sharply reduce the capital available to arbitrageurs [39]. When the arbitrage pipeline is blocked, the basis widens abnormally, and the spot market's volatility may take longer to be fully reflected in the perpetual futures. In certain information-driven events, however, such as a macro data release or the finalization of a regulatory policy, the perpetual futures market, with its higher leverage and lower trading costs, sometimes leads the spot market in completing price discovery and becomes the origin of volatility [40].

### 22.5.4 Macro transmission from traditional finance

The crypto market was once viewed as a safe-haven asset or "digital gold" independent of the traditional financial system. Several forces have since eroded this isolation, however: institutional investors allocating directly to crypto assets, the common global-liquidity factor created by the Federal Reserve's quantitative easing, and the rise of retail cross-asset speculation in a zero-interest-rate environment. The correlation between the crypto market and traditional financial assets has since changed markedly, and macroeconomic volatility can now be transmitted efficiently to the crypto perpetual futures market.

Before 2020, the correlation between Bitcoin and traditional equity assets such as the S&P 500 index was extremely low, and in some periods even negative. The outbreak of COVID-19 became a historic turning point, however. A report by the International Monetary Fund notes that since the outbreak of the pandemic, the correlation between the volatility of the Bitcoin price and the volatility of equity indices has increased significantly, with Bitcoin's spillover effect accounting for 14% to 18% of the change in global equity-market volatility [41]. Figure 22-12 uses a time-series chart to show the long-term evolution of the dynamic correlation between Bitcoin and the S&P 500 index.

![Figure 22-12](./images/fig-22-12-en.png)

**Figure 22-12.** Time-varying correlation between BTC and the S&P 500 (representative illustration: the 90-day rolling correlation between BTC and the S&P 500 (2018–2024), consistent with the empirical findings of Iyer (2022) [41] but not actually computed)

As shown in Figure 22-12, before 2020 the rolling correlation coefficient between Bitcoin and the S&P 500 was for a long time close to zero or even occasionally negative, supporting the "digital gold" narrative; after the COVID-19 outbreak in 2020 it jumped structurally, once climbing above 0.5 during the macro tightening of 2022. This indicates that, as institutions have become deeply involved, crypto assets have been incorporated into the pricing system for global macro risk assets, so that volatility shocks in traditional markets are effectively transmitted to the perpetual futures market.

The core driving force of this macro transmission is the cyclical shift in global monetary policy. When the Federal Reserve implements extremely loose monetary policy, the excess liquidity simultaneously pushes up the prices of traditional risk assets and crypto assets. When monetary policy turns to tightening, the rise in the risk-free rate rapidly compresses the valuations of all risk assets. Research finds that when the shadow policy rate turns negative (that is, monetary policy is extremely loose), the spillover effect of monetary policy on the crypto market is especially large [42].

This transmission forms a clear chain: the Federal Reserve's policy decisions first shock the U.S. Treasury and equity markets; traditional financial institutions then, to balance the risk of their overall portfolios, adjust their crypto-asset exposure; this adjustment is transmitted through the Bitcoin spot market to Bitcoin perpetual futures, and finally spills over through the internal network to the entire altcoin perpetual futures market. During typical macro-tightening crises such as that of 2022, the correlation between crypto assets and traditional risk assets rose sharply toward one, and the traditional portfolio-diversification strategy failed significantly in the face of this systemic transmitted volatility.

### 22.5.5 Measuring transmitted volatility

To quantify and monitor this complex transmitted volatility precisely, financial econometrics offers a set of advanced analytical tools. The most widely adopted is the spillover-index model proposed by Diebold and Yilmaz. Based on a VAR system and the generalized forecast-error variance decomposition, this model can strip out what proportion of an asset's volatility is caused by shocks from other assets in the system [36].

By constructing a dynamic spillover-index matrix, researchers can clearly compute whether each market is a net receiver or a net transmitter of volatility. In the crypto perpetual futures market, this matrix usually exhibits a highly asymmetric structure. The diagonal elements represent an asset's own idiosyncratic volatility, while all the off-diagonal elements quantify the intensity of cross-market spillover. Figure 22-13 uses a heat map to present the directional-spillover matrix among the perpetual futures of major crypto assets.

![Figure 22-13](./images/fig-22-13-en.png)

**Figure 22-13.** DY spillover-index heat map (conceptual illustration, not a computed matrix: based on the framework of Diebold & Yilmaz (2012) [36], depicting the directional-spillover structure among major crypto assets such as BTC, ETH, BNB, SOL, and XRP and the S&P 500 and gold)

As shown in Figure 22-13, the matrix exhibits a highly asymmetric structure: Bitcoin's spillover to other assets (the corresponding row) is significantly higher than the spillover it receives (the corresponding column), further confirming its status as a major net transmitter of volatility. The diagonal elements of small-cap assets (the proportion of their own idiosyncratic volatility explained) tend to be low, meaning their volatility is driven to a large extent by external contagion rather than by changes in their own fundamentals.

Beyond the spillover index, multivariate GARCH models, especially the BEKK specification, are also a core tool for capturing volatility transmission. This model can not only estimate the mean-reversion property of an asset's volatility but, more importantly, dynamically track the conditional covariance among different crypto assets. By introducing a time variable, researchers have confirmed that the shock transmission and volatility-spillover effects among crypto assets have significant two-way features, and that their conditional correlation evolves dynamically over time [43]. Both the DY spillover index and BEKK-MGARCH are linear frameworks, however, with known limitations in handling extreme tail events and nonlinear contagion. In recent years, nonlinear transmission-measurement methods based on Copula-DCC and quantile regression have been applied in the crypto-market literature and can more accurately capture nonlinear features such as sudden correlation shifts and enhanced tail dependence during crises.

Further, combining network-topology theory, these econometric results can be visualized as complex directed weighted graphs. In these network topologies, centrality metrics such as eigenvector centrality or betweenness centrality are used to identify which perpetual futures occupy a systemically important position in volatility transmission. This network-perspective analysis not only reveals the direction of volatility transmission but also provides market regulators and risk managers with an early-warning system, helping them identify the key nodes in the network with high systemic-risk concentration before local volatility evolves into a global crisis.

## 22.6 Four-source interaction and regime switching

The four volatility sources do not operate independently of one another; among them there exist systematic nonlinear interactions and feedback-amplification mechanisms. More critically, the share that these sources occupy in total volatility (the volatility budget) is not fixed but undergoes a violent structural reorganization as the market regime switches. Understanding this regime dependence is central to revealing the microstructural foundations of volatility clustering.

### 22.6.1 The nonlinear interaction of the four sources

The four volatility sources exhibit approximate independence in calm market periods, but in a high-volatility environment their interaction terms amplify sharply, forming a complex feedback network. This interaction is first evident in the one-way triggering of liquidity volatility by informational volatility. When a new macro shock or fundamental information arrives, the degree of information asymmetry rises, and the adverse-selection risk faced by market makers intensifies. To compensate for this risk, market makers rationally widen the bid-ask spread and reduce order-book depth. At this point, subsequent order flow executes on thinner liquidity, producing a larger temporary price impact. Informational volatility therefore endogenously triggers liquidity volatility by changing microstructure parameters [23].

The most destructive link in the four-source interaction is the two-way positive feedback between liquidity volatility and mechanism-induced volatility. When insufficient liquidity causes a relatively large temporary price decline, this decline may itself touch the liquidation threshold of long positions. Once forced liquidation is triggered, the liquidation engine submits market sell orders to the market, producing mechanism-induced volatility. These liquidation orders further consume the already-fragile liquidity, causing the price to continue falling and thereby triggering more liquidations. This spiraling feedback loop drives the price far from the equilibrium level implied by the initial information.

Transmitted volatility plays a cross-market amplifying role in this process. In a highly interconnected crypto-asset market, the volatility of a single asset is often rapidly contagious through market makers' cross-asset risk management and cross-margin mechanisms. When Bitcoin experiences liquidity contraction and liquidation, market makers, to control the risk exposure of their overall portfolio, synchronously withdraw their liquidity quotes in the Ethereum and altcoin markets. At the same time, the decline in the Bitcoin price lowers the portfolio collateral ratio of multi-asset investors, which in turn triggers chain liquidations of altcoin positions. This cross-asset contagion allows transmitted volatility to directly activate mechanism-induced and liquidity volatility in other markets, forming a nonlinear cascade effect.

![Figure 22-14](./images/fig-22-14-en.png)

**Figure 22-14.** The nonlinear feedback loop of four-source interaction (drawn by the author, referencing the liquidity-spiral framework of Brunnermeier & Pedersen (2009) [23])

As shown in Figure 22-14, the feedback loop begins with an information shock: market makers retreat to avoid adverse selection, triggering the expansion of liquidity volatility; a temporary decline on the thin order book crosses the liquidation threshold, activating the liquidation engine and triggering mechanism-induced volatility; the liquidation sell orders then further consume liquidity, forming a two-way positive feedback. Ultimately, the extreme volatility spills over to other markets through cross-asset arbitrage and cross-margin, producing transmitted volatility, which may in turn re-trigger the above spiral in the target market, forming a global cascade effect.

### 22.6.2 The state dependence of the volatility budget

As introduced in Section 22.1.4, total volatility can be viewed as a budget jointly composed of four sources: informational, liquidity, mechanism-induced, and transmitted. The allocation of this budget depends heavily on the market regime, exhibiting a systematic structural switch from calm periods to crisis periods.

When the market is in a normal or calm period, the volatility budget is dominated mainly by informational volatility. At this time, market liquidity is ample, market makers compete fully, and the bid-ask spread stays at a low level. New fundamental information or macro data can be smoothly incorporated into prices, and the temporary price impact produced by large trades is small and quickly absorbed. Dimpfl and Peter's study of cross-spot-exchange price-discovery efficiency shows that the signal-to-noise ratio in crypto markets is about 36%, whereas that of the NYSE and NASDAQ is about 90% [11]. Although this metric measures the noise in cross-exchange price discovery (rather than the volatility composition in this chapter's four-source framework), it provides a useful reference benchmark: even in calm periods, the share of non-informational components in crypto markets is significantly higher than in traditional stock markets. This signal-to-noise difference partly reflects the structural features of crypto markets (24/7 trading, market fragmentation) rather than pure *inefficiency*, and the high signal-to-noise ratio of traditional markets partly stems from institutional noise-suppression mechanisms such as price limits and market-maker obligations. In the crypto perpetual futures market, informational volatility remains the largest of the four sources in calm periods. Mechanism-induced volatility contributes only limitedly at this stage, manifesting only as tiny arbitrage pulses around funding-rate settlement, while transmitted volatility also stays within the normal range of cross-market linkage.

When the market enters a tense period (for example, facing the uncertainty expectations of a major macro event or the eve of a key technical upgrade), the structure of the volatility budget begins to shift. As uncertainty rises, market makers, to avoid adverse-selection risk, actively widen quote spreads and reduce order-book depth. The deterioration in liquidity conditions causes order flow of the same size to produce larger price slippage, driving up the share of liquidity volatility in the total budget. At the same time, as the price range widens, some highly leveraged positions begin to face margin-call pressure, and small-scale liquidation events occur sporadically, so that the share of mechanism-induced volatility also begins to expand. The absolute amount of informational volatility may not decrease, but because of the rise in the other volatility sources, its relative share of the total budget declines, and more and more frictional noise mixes into market prices.

In an extreme crisis period, the structure of the volatility budget reverses violently. When the initial negative shock breaks through the market's tolerance threshold, sharp liquidity contraction and large-scale liquidation cascades occur simultaneously and reinforce each other. Market makers retreat on a large scale, and liquidity volatility expands sharply; the liquidation engine indiscriminately fires market orders, and mechanism-induced volatility dominates; panic and cross-asset sell-offs push transmitted volatility to its peak. Nguyen's research shows that the contribution of the noise component to the total variance of crypto prices reaches as high as 40%, far higher than the 21% of traditional stocks and the 18% of the foreign-exchange market [10] (note that this 40% is the unconditional mean for the full 2015–2024 sample, not the share under crisis conditions). In a crisis period, the share of non-informational components is very likely to rise significantly relative to calm periods and become dominant, but its precise value awaits empirical testing on a crisis subsample. In this regime, a considerable portion of the observed aggregate volatility no longer reflects fundamental information but is manufactured by the collapse of market microstructure and institutional friction. This means that price movements in a crisis exhibit systematic overreaction, with a large number of assets sold off to levels far below their fundamental value.

This regime switch indicates that under extreme market conditions, the high volatility observed is mainly a representation of market-structure failure rather than a violent change in fundamental information. At the same time, this structural overreaction also provides a theoretical basis for strategists: since most of the volatility in a crisis period is information-free temporary noise, prices will undergo a systematic mean-reversion process after liquidity is restored and liquidation pressure subsides.

Taken together (for the compositional evolution of the volatility budget across the three states of calm, tense, and crisis, see Figure 22-2), under extreme market conditions the overwhelming majority of observable price movements have lost their price-discovery function and are driven purely by institutional friction and the liquidity spiral.

### 22.6.3 Triggers and early warning of regime switching

The switch of the market from a calm period to a crisis period is not a gradual, linear process but has a distinct critical-point effect. When external shocks or the accumulation of internal frictions break through a certain threshold, the market microstructure undergoes a sudden change. This nonlinear phase transition can be given early warning through specific microstructure indicators.

An abnormal decline in order-book depth is usually the earliest signal of a regime switch. Before the price moves substantially, market makers have often already sensed the rise in adverse-selection risk through the imbalance in micro order flow, and have withdrawn their limit orders in advance. When the depth at the top of the order book shrinks to below one standard deviation of its historical mean, the market's capacity to absorb shocks declines significantly, raising the probability of a liquidity-volatility outbreak.

The extremization of the funding rate and the widening of the cross-exchange basis are clear signs that mechanism-induced volatility is about to take over the market. When the funding rate of perpetual futures continuously deviates from the neutral level, and arbitrage capital cannot effectively close the basis between spot and contract, it indicates that the balance-sheet constraints of arbitrageurs have already tightened. In this state, the market is extremely vulnerable to the shock of a one-sided move, which in turn triggers large-scale liquidation. An initial rise in liquidation volume is the direct catalyst for the full eruption of the crisis state: once the selling pressure of the liquidation engine exceeds the residual liquidity-bearing capacity, the system irreversibly enters a cascade-liquidation state.

### 22.6.4 The liquidity-volatility spiral in the crisis state

In the crisis state, the exponential amplification of volatility is essentially the mapping of a liquidity tragedy of the commons onto the price dimension. As described in Chapter 20, liquidity in crypto markets has the property of a quasi-public good (more precisely, a common-pool resource). When volatility rises, the optimal rational choice for an individual market maker is to widen spreads or suspend quoting to protect its own capital. When all market makers adopt this defensive strategy simultaneously, however, the liquidity of the entire market rapidly disappears.

This collective liquidity retreat directly triggers the liquidity-volatility spiral described in Sections 22.3.2 and 22.6.1: the extreme thinness of the order book causes stop-loss orders or liquidation orders to produce enormous slippage and extreme realized volatility; this spurious volatility is then captured by risk-management models, pushing up volatility forecasts and prompting market makers to exit even more decisively.

In this spiral, mechanism-induced volatility further intensifies the process. The mechanical execution of the liquidation mechanism is not price-sensitive; it indiscriminately submits market sell orders at the moment of worst liquidity, converting a temporary price shock into deeper balance-sheet destruction. In the liquidation-cascade event of October 2025 (whose deleveraging scale and window are detailed in Section 22.3.2), the most violent shock was concentrated within about 40 minutes, during which the intensity of cross-asset contagion was about 20% stronger than the spillover effect of the 2018 trade war [44]. This shows clearly how, in a crisis, the collapse of microstructure amplifies a limited information shock into a systemic, sharp rise in volatility.

### 22.6.5 Practical implications

The regime-switching framework of the volatility budget provides a new perspective for traders, risk managers, and market designers. For traders, identifying the core source of current volatility is a precondition for formulating a strategy. If one judges that the current high volatility is driven mainly by liquidity and mechanism-induced factors, this means the price movement contains a large amount of temporary shocks and overreaction, and a mean-reversion strategy may have a relatively high expected return. This judgment requires two preconditions, however. First, the shock must indeed contain no fundamental information; if it stems from a fundamentally destructive event such as a protocol being attacked or a project team absconding, the price will not revert (Luna in 2022 is a textbook counterexample). Second, the strategy must be actually executable, whereas under extreme conditions the exchange may restrict new position openings, throttle the API, or freeze withdrawals, making the theoretical mean-reversion opportunity impossible to capture. Making this ex ante judgment is precisely the task for which this chapter's four-source decomposition is designed. Conversely, if volatility is dominated by informational factors, contrarian trading will face enormous adverse-selection risk.

From a risk-management perspective, the traditional VaR model usually assumes that volatility is a homogeneous stochastic process, which is a fatal flaw in the perpetual futures market. Regime-aware risk management requires not being overconfident in calm periods and being able to dynamically adjust parameters in stress periods to adapt to the nonlinear eruption of mechanism-induced volatility. The funding rate, as an endogenous feedback rule, has an update frequency and cap whose settings directly affect the tail risk of basis volatility, and a reasonable mechanism design can effectively suppress the generation of an extreme basis [32]. The four-source framework provides a conceptual basis for building regime-aware risk management: by monitoring microstructure indicators such as order-book depth, funding-rate extremity, and liquidation volume in real time, and dynamically adjusting the tail parameters of the VaR model (for example, switching from a normal distribution to a fat-tailed one) when the combination of indicators triggers a signal of a switch from calm to tense, one embeds the regime-switching logic of the volatility budget into the risk-management process.

For market designers, the key to reducing excess volatility in crisis periods lies in interrupting the vicious interaction among the four sources. Introducing a dynamic margin-buffer mechanism that appropriately relaxes liquidation conditions when volatility spikes can slow the occurrence of liquidation cascades. At the same time, optimizing cross-exchange circuit-breaker mechanisms to prevent the liquidity exhaustion of a single platform from rapidly spreading network-wide through oracles and arbitrageurs is a necessary direction of institutional evolution for enhancing the microstructural resilience of the crypto derivatives market. In addition, traditional financial markets solve the collective-action problem of liquidity retreat in crisis periods through a market-maker-obligation system; whether the market-maker incentive programs of CEXs can effectively maintain liquidity supply in a crisis is worthy of further empirical testing. In terms of leverage management, major jurisdictions have introduced differentiated leverage-cap policies (for example, Japan limits retail crypto margin-trading leverage to 2x, and the EU, under the MiFID II framework, has limited retail crypto contracts for difference (CFDs) to 2:1, with crypto derivatives not falling under the jurisdiction of MiCA), while offshore exchanges still allow leverage of more than 100x. This regulatory fragmentation is itself one of the institutional sources of excess volatility: the concentrated migration of traders toward high-leverage platforms intensifies the liquidation-cascade risk of these platforms.

## 22.7 The excess volatility of perpetual futures

Synthesizing the analysis of the preceding six sections on the microstructure of perpetual futures, the funding-rate mechanism, and the liquidation engine, this section directly answers a question of central importance in crypto finance: why the volatility of perpetual futures is systematically higher than that of the spot market. This phenomenon is termed *excess volatility* in the traditional finance literature, but in the crypto derivatives market, because of frictionless access to leverage and round-the-clock trading, it exhibits a distinctive generating mechanism and distinctive welfare-distribution consequences. This section disaggregates the sources of this excess volatility and assesses its welfare impact on different market participants.

### 22.7.1 Definition and measurement

The theoretical origin of excess volatility can be traced back to Shiller's (1981) classic study of the stock market, which showed that stock-price volatility systematically exceeds the level that dividend fundamentals can explain [45]. In the crypto derivatives market, excess volatility is defined as the difference between an asset's actual volatility in the derivatives market and the volatility that should prevail in the spot market under the same information set. This is a theoretical benchmark definition: the subtrahend—"the spot volatility that should prevail under the same information set"—is not directly observable, nor can the observed spot volatility simply be substituted for it (the spot market itself also contains a noise component of about 40%); empirically, it can only be approximately identified with the help of the natural experiments discussed below [46] and the permanent/transitory variance decomposition [10], so the credibility of this benchmark is limited by the credibility of the spot-noise decomposition. Unlike the stock excess volatility Shiller found, which stems mainly from investor sentiment and discount-rate volatility, the excess volatility of perpetual futures is driven to a large extent by the mechanism-induced design and liquidity frictions analyzed in this chapter, which is an important feature distinguishing the crypto derivatives market from traditional asset markets (this chapter does not claim that the excess volatility of perpetuals can be explained by Shiller's discount-rate channel; it borrows only his measurement structure of "actual volatility exceeding the volatility that fundamentals should support"). In a fully efficient market, the price change of a derivative should reflect only the change in the discounted value of the underlying asset's future cash flows or updates to macro fundamental information. In crypto markets, however, the price-discovery process is often accompanied by significant microstructure noise. As cited in Section 22.6.2, the signal-to-noise ratio of the crypto market is significantly lower than that of traditional stock markets such as the NYSE and NASDAQ [11]. This high proportion of noise trading means that most price volatility is driven not by fundamental information but by the trading behavior of market participants itself.

The core challenge in measuring the excess volatility of perpetual futures is how to control for information differences. Because perpetual futures and the spot market share the same underlying asset, their price-discovery processes are highly intertwined, and a simple subtraction of volatilities cannot accurately isolate the common volatility driven by information flow. To isolate pure excess volatility, financial economists usually adopt a natural-experiment design. For example, by observing the changes in market structure before and after the approval of U.S. spot Bitcoin ETFs, researchers found that spot-market volatility declined significantly after the introduction of a more information-efficient investment channel, which supports the stabilization hypothesis that over-speculation in the derivatives market causes the spot market to bear additional volatility [46].

Another measurement framework separates the permanent component from the transitory component through variance decomposition of high-frequency data. The permanent component represents the information-driven price random walk, while the transitory component captures the mean-reverting volatility caused by liquidity shocks and market frictions. As cited in Section 22.6.2, the share of the noise component in the total variance of crypto prices is significantly higher than in traditional stock and foreign-exchange markets [10]. This systematically high noise share provides a benchmark for quantifying the microstructure frictions unique to perpetual futures.

### 22.7.2 Source attribution

The excess volatility of perpetual futures is not caused by a single factor but is the joint result of its unique market-microstructure features. One categorical point must be clarified first: by definition, excess volatility has already removed the informational component common to perpetuals and spot (the two cancel each other out in the subtraction), so the source decomposition in this section focuses on three sources unique to perpetuals—mechanism-induced, liquidity, and transmitted. The "informational" item appearing below and in Figure 22-15 refers specifically to the information-overshoot differential that perpetuals produce relative to spot because of high leverage (its amplification mechanism is classified under mechanism-induced volatility), not to informational volatility itself. Figure 22-15 illustratively presents the relative contribution of these sources to the excess volatility of perpetual futures.

![Figure 22-15](./images/fig-22-15-en.png)

**Figure 22-15.** Source-attribution decomposition of the excess volatility of perpetual futures (drawn by the author: the relative share of each source is an illustrative estimate based on the logic of event-window/variance-ratio decomposition and the four-source conceptual framework; the four-source decomposition is a first-order approximation and liquidity is a residual category, see Section 22.1.3; not a precise measurement based on a single empirical sample)

As shown in Figure 22-15 (the proportions in the figure are an illustrative ordering, not a precise statistical decomposition), mechanism-induced volatility usually accounts for a larger share of the excess volatility (its core being the periodic noise of liquidation cascades and funding-rate settlement), followed by liquidity volatility, with transmitted volatility being especially prominent in crisis events; the information-overshoot differential, though a relatively small share, nevertheless shows by its existence that even a *good* price-discovery process generates some excess volatility through overshoot in a high-leverage environment.

Based on the analysis in Sections 22.4 and 22.3, the contribution of each source to excess volatility can be summarized as follows (the following is a conceptual attribution ordering based on mechanism analysis, not a precise measurement of variance shares). Mechanism-induced volatility is very likely the main contributor to excess volatility. As argued in Sections 22.4.1 and 22.4.2, liquidation cascades and the funding-rate settlement cycle are its core driving forces. Empirical evidence further shows that during the every-8-hour funding-rate settlement window of perpetual futures, adverse-selection risk in the spot market also increases significantly, causing quote spreads to widen and market depth to deteriorate [20], which means that the impact of mechanism-induced volatility has already spilled over into the microstructure of the spot market.

Liquidity volatility is another main contributor to excess volatility, rooted in the fragmentation of crypto-market depth and the constraints of market makers' inventory management. On high-frequency time scales, the temporary price deviation triggered by a large order and its subsequent partial reversal are a typical manifestation of liquidity friction [47]. Transmitted volatility contributes prominently in crisis events, as the extreme volatility of large-cap assets is rapidly transmitted to less liquid altcoin markets [48]. Although informational volatility is theoretically an efficient price-discovery process, because the high leverage of perpetual futures naturally attracts informed traders, the process of information being incorporated into prices is often accompanied by overshoot, and this overshoot itself also constitutes a component of excess volatility.

### 22.7.3 The heterogeneity of excess volatility

Excess volatility exhibits significant heterogeneity across different types of crypto assets and different stages of market development. From the asset dimension, Bitcoin's excess volatility is systematically lower than that of Ethereum and other mid-cap altcoins. This difference is mainly attributable to the fact that the Bitcoin spot market has the deepest liquidity and the broadest institutional participation, and its perpetual futures market has the highest arbitrage efficiency. By comparison, the altcoin spot market has poorer liquidity and lacks an effective short-selling mechanism, causing its perpetual futures to often accumulate a large basis in one-sided markets, which in turn triggers more violent funding-rate arbitrage volatility and liquidation shocks. Figure 22-16 tracks the evolution over time of the excess volatility of different asset classes.

![Figure 22-16](./images/fig-22-16-en.png)

**Figure 22-16.** Time evolution of the excess volatility of perpetual futures across asset classes (representative illustration: the evolution trend, across the market cycle, of the annualized excess volatility of the perpetual futures of BTC, ETH, and altcoins ranked 10–30 by market capitalization relative to contemporaneous spot; a trend illustration, not measured monthly RV differences)

As shown in Figure 22-16, the excess volatility of altcoins is consistently higher than that of Bitcoin and Ethereum, and jumps by a larger amount when volatility intensifies; Bitcoin, by contrast, is relatively moderate and stable, reflecting its mature market-making ecosystem and efficient cross-market arbitrage. The long-term downward trend of the three curves suggests that the crypto derivatives market is gradually moving toward institutional maturity.

Examined from the time dimension, excess volatility exhibits a strong procyclical feature. In the bull-market phase, driven by retail FOMO and strong demand for leverage, market-wide open interest and leverage rise sharply. This high-leverage environment greatly amplifies the sensitivity of prices to small shocks, making excess volatility during a bull market significantly higher than during a bear or range-bound market. When the observation horizon is extended to the 10-year period from 2015 to 2025, however, one finds that the price-formation mechanism of the crypto market is undergoing a structural transformation. Since 2017, the noise component of the market's total variance has shown an overall downward trend, but with cyclical rebounds during speculative frenzies—for example, temporary jumps in the noise share can be observed during the 2020 DeFi boom and the 2024 memecoin wave. At the same time, the component reflecting overall market information has risen steadily from a long-term perspective.

This long-term downward trend reflects the gradual maturation of the crypto market. As market regulation has tightened, derivatives infrastructure has matured, and institutional investors have entered in depth, the market's pricing efficiency has improved substantially. For example, empirical analysis of successive Bitcoin halving events shows that, as the market becomes more standardized, the abnormal returns and excess volatility around halving events are both declining significantly [49]. The abundance of institutional arbitrage capital allows the calendar and cross-market basis to be closed more quickly, thereby suppressing the extreme price volatility triggered by local liquidity exhaustion.

### 22.7.4 Welfare implications

Quantifying excess volatility ultimately requires answering its welfare-distribution impact on market participants. The additional volatility created by perpetual futures is not evenly distributed among all participants but profoundly reshapes the market's wealth-transfer pattern. For profit-maximizing informed traders and high-frequency arbitrage institutions, high volatility is directly equivalent to high-frequency trading opportunities and rich arbitrage space. Using advanced algorithms and low-latency infrastructure, they extract profits from the periodic swings of the funding rate and the instantaneous mispricing triggered by liquidation events.

Market makers' attitude toward excess volatility, however, is more complex. Moderate volatility can bring considerable bid-ask spread income, but when volatility breaks through a certain threshold, the inventory risk faced by market makers rises sharply. Empirical data from the options market show that when market makers' aggregate gamma inventory is imbalanced, they significantly widen quote spreads to hedge risk, causing overall market liquidity to dry up [50]. This liquidity retreat often occurs precisely when the market most needs depth, further intensifying the spiraling price decline. Figure 22-17 uses a tiered framework diagram to summarize the differentiated welfare impact of excess volatility on different groups of market participants.

![Figure 22-17](./images/fig-22-17-en.png)

**Figure 22-17.** The welfare impact of excess volatility on different market participants (Data source: drawn by the author, supplemented with data on retail loss rates from Cornelli et al. (2023) [51])

As shown in Figure 22-17, the welfare effect of excess volatility is markedly asymmetric: informed traders and high-frequency arbitrage institutions sit at the positive end, benefiting from higher volatility and more frequent mispricing opportunities; market makers are in the middle, with moderate volatility bringing spread income but extreme volatility bringing inventory losses; retail investors are at the negative end, lacking an informational advantage and risk-control tools, more easily triggering forced liquidation and thereby becoming the bearers of a systemic wealth transfer.

Near the positive end of the welfare distribution, there is also a class of size-driven participants who win by position size rather than analytical capability—namely, large holders. Although these participants do not rely on an informational or analytical advantage, their large positions give them the ability to influence the timing of a liquidation cascade through strategic selling or accumulation. In excess volatility, large holders play a dual role: they may profit by intensifying mechanism-induced volatility through selling, or they may become a key force for market stability when liquidity is exhausted. This internal heterogeneity suggests that the three-way framework of Figure 22-17 still admits further stratification at the informed-trader end.

Retail investors are the main losers in excess volatility. Lacking sophisticated risk-management tools and a deep understanding of market microstructure, retail investors are extremely prone to triggering forced liquidation during excess volatility. A BIS research report reveals this striking wealth transfer: during the crypto-market crisis of 2022 (during which market-wide capitalization shrank by more than $1.8 trillion), large investors successfully reduced their holdings before the crisis erupted, while a large number of retail investors added to positions against the trend during the decline, ultimately causing most crypto-app users to suffer substantial losses [51]. This systematic transfer of wealth to institutions indicates that the high-leverage mechanism of perpetual futures has a significant negative welfare effect in an environment lacking investor protection.

From the macro perspective of regulators, persistent excess volatility is not only a signal of market manipulation and institutional deficiency but also a manifestation of financial-resource misallocation. If the price movements of a market are driven mainly by endogenous mechanism friction and speculative noise rather than reflecting fundamental value discovery, then that market cannot effectively perform the core function of capital allocation. How to optimize the design of the liquidation mechanism—for example, by introducing auction-based liquidation to reduce price impact [31]—and how to regulate leverage caps will therefore be key challenges for the crypto derivatives market as it moves toward maturity and ultimately integrates into the mainstream financial system.

## 22.8 Chapter summary

Price volatility in perpetual futures markets is not a homogeneous random-walk process but a complex product jointly shaped by four mechanisms of fundamentally different natures. Informational volatility reflects how the market prices new fundamentals, macro events, or on-chain data, and is an indispensable price-discovery mechanism of a healthy market. Liquidity volatility arises from temporary price shocks when order-book depth is insufficient and is a friction cost of market microstructure. Mechanism-induced volatility is driven entirely by the institutional design unique to perpetual futures, including the forced liquidations triggered by liquidation cascades and the predictable pulses brought by the funding-rate settlement cycle. Transmitted volatility reveals the linkage across assets and markets, allowing an isolated event to spread globally through arbitrage channels and margin-cross effects.

The relative shares of these four sources under different market regimes constitute the dynamic switching mechanism of the volatility budget. In a normal market, informational volatility dominates, and price movements mainly reflect the genuine arrival of information. When the market faces stress or falls into crisis, however, sharp liquidity contraction and mechanism-induced liquidation rapidly take over the volatility budget. At this point, most of the price movement observed in the market no longer contains new information but is pure structural noise. This exit of informational volatility and sharp expansion of frictional volatility under extreme conditions is central to understanding the fragility of the crypto market.

A direct corollary of the four-source decomposition framework is that perpetual futures markets exhibit significant excess volatility. Comparison with the spot market and traditional financial derivatives shows that the total volatility of perpetual futures is systematically higher than the level its information content should support. This excess portion is contributed mainly by liquidity frictions and mechanism-induced deficiencies and is a hidden cost that the current institutional design imposes on all market participants. The existence of excess volatility not only increases market makers' inventory risk but also causes retail traders to trigger stop-losses more frequently, systematically harming market welfare. More critically, because mechanism-induced volatility such as liquidation cascades and funding-rate settlement is largely endogenous to the rules, this portion of excess volatility can in principle be substantially reduced through institutional optimization (such as introducing gradual liquidation or a continuous funding rate); but because there is a fundamental trade-off between liquidation efficiency and system solvency, eliminating mechanism-induced volatility entirely is neither realistic nor desirable.

Decomposing the sources of volatility not only clarifies the multiple causes behind a single price movement but also lays the foundation for understanding the evolution of volatility over time. The four volatility sources do not occur in isolation along the time dimension but reinforce one another through systematic interaction effects. When an initial information shock triggers a price adjustment, the ensuing adverse-selection risk causes market makers to retreat, thereby activating liquidity volatility. The enlarged temporary price shock further touches the bankruptcy price of leveraged traders, setting off a liquidation cascade and causing mechanism-induced volatility to rise sharply. Ultimately, this extreme local volatility spills outward through cross-asset arbitrage and cross-margin mechanisms, forming transmitted volatility, which in turn further intensifies the global liquidity contraction.

This nonlinear positive-feedback loop—triggered by information, amplified by liquidity, detonated by mechanisms, and finally transmitted market-wide—explains why high volatility is often not a fleeting, isolated event. Once volatility breaks through a critical threshold, the mutual reinforcement among the sources makes it difficult for the system to return to calm in the short run. This leads to the next core topic: now that we have clarified the constituent elements of volatility and their interaction mechanisms, what macro features does this positive-feedback process exhibit over time? Why does volatility always exhibit strong time-variation and autocorrelation in the market? Chapter 23 will formally examine the dynamic mechanism of this volatility-clustering phenomenon, analyzing how slow variables, feedback loops, and state machines jointly determine the market's switching between low- and high-volatility states.

## References

[1] Sandor, K. (2024, March 5). Bitcoin tumbles 10% after hitting record high; triggers \$1B crypto liquidations. *CoinDesk*. https://www.coindesk.com/markets/2024/03/05/bitcoin-tumbles-6-after-hitting-record-high-as-mounting-sell-orders-cap-price-gains

[2] He, S., Manela, A., Ross, O., & von Wachter, V. (2022). Fundamentals of perpetual futures. *arXiv*. https://arxiv.org/abs/2212.06888

[3] Soska, K., Dong, J.-D., Khodaverdian, A., Zetlin-Jones, A., Routledge, B. R., & Christin, N. (2021). Towards understanding cryptocurrency derivatives: A case study of BitMEX. In *Proceedings of the Web Conference 2021* (pp. 45–57). ACM. https://doi.org/10.1145/3442381.3450059

[4] Andersen, T. G., Bollerslev, T., & Meddahi, N. (2005). Correcting the errors: Volatility forecast evaluation using high-frequency data and realized volatilities. *Econometrica*, *73*(1), 279–296.

[5] Palm, F. C. (1996). GARCH models of volatility. In G. S. Maddala & C. R. Rao (Eds.), *Handbook of statistics*: Vol. 14. *Statistical methods in finance* (pp. 209–240). Elsevier. https://doi.org/10.1016/s0169-7161(96)14009-8

[6] Hasbrouck, J. (1995). One security, many markets: Determining the contributions to price discovery. *The Journal of Finance*, *50*(4), 1175–1199. https://doi.org/10.1111/j.1540-6261.1995.tb04054.x

[7] Hasbrouck, J. (2002). Stalking the "efficient price" in market microstructure specifications: An overview. *Journal of Financial Markets*, *5*(3), 329–339. https://doi.org/10.1016/s1386-4181(02)00029-0

[8] Roll, R. (1984). A simple implicit measure of the effective bid-ask spread in an efficient market. *The Journal of Finance*, *39*(4), 1127–1139. https://doi.org/10.2307/2327617

[9] Hasbrouck, J. (1991). Measuring the information content of stock trades. *The Journal of Finance*, *46*(1), 179–207. https://doi.org/10.1111/j.1540-6261.1991.tb03749.x

[10] Nguyen, K. Q. (2024). *Why are cryptocurrencies so volatile?* [Working paper]. Auckland University of Technology.

[11] Dimpfl, T., & Peter, F. J. (2021). Nothing but noise? Price discovery across cryptocurrency exchanges. *Journal of Financial Markets*, *54*, Article 100584. https://doi.org/10.1016/j.finmar.2020.100584

[12] Pyo, S., & Lee, J. (2020). Do FOMC and macroeconomic announcements affect Bitcoin prices? *Finance Research Letters*, *37*, Article 101386. https://doi.org/10.1016/j.frl.2019.101386

[13] Ben Omrane, W., Dabbou, H., Saadi, S., Savaser, T., & Sebai, S. (2025). Exploring volatility reactions in cryptocurrency markets using intraday macroeconomic news analysis. *International Review of Economics & Finance*, *103*, Article 104509. https://doi.org/10.1016/j.iref.2025.104509

[14] Mehdian, S., Gherghina, Ș. C., & Stoica, O. (2025). The reaction of cryptocurrencies to the approval of spot Bitcoin and Ethereum ETFs: An intraday event study. *Borsa Istanbul Review*. Advance online publication.

[15] Magner, N., & Sanhueza, A. (2025). The Moby Dick effect: Contagious Bitcoin whales in the crypto market. *Finance Research Letters*, *85*, Article 107906. https://doi.org/10.1016/j.frl.2025.107906

[16] Grande, M., & Borondo, J. (2025). Trust as a driver in the DeFi market: Leveraging TVL/MCAP bands as confidence indicators to anticipate price movements. *Finance Research Letters*, *75*, Article 106705. https://doi.org/10.1016/j.frl.2024.106705

[17] Kyle, A. S. (1985). Continuous auctions and insider trading. *Econometrica*, *53*(6), 1315–1336. https://doi.org/10.2307/1913210

[18] Easley, D., Kiefer, N. M., O'Hara, M., & Paperman, J. B. (1996). Liquidity, information, and infrequently traded stocks. *The Journal of Finance*, *51*(4), 1405–1436. https://doi.org/10.1111/j.1540-6261.1996.tb04074.x

[19] Tiniç, M., Sensoy, A., Akyildirim, E., & Corbet, S. (2023). Adverse selection in cryptocurrency markets. *The Journal of Financial Research*, *46*(2), 497–546. https://doi.org/10.1111/jfir.12317

[20] Ruan, Q., & Streltsov, A. (2022). *Perpetual futures contracts and cryptocurrency market quality* (SSRN Working Paper No. 4218907). https://doi.org/10.2139/ssrn.4218907

[21] Ben Omrane, W., Houidi, F., & Savaser, T. (2024). Macroeconomic news and intraday seasonal volatility in the cryptocurrency markets. *Applied Economics*, *56*(38), 4594–4610. https://doi.org/10.1080/00036846.2023.2212970

[22] Alexander, C., & Heck, D. F. (2020). Price discovery in Bitcoin: The impact of unregulated markets. *Journal of Financial Stability*, *50*, Article 100776. https://doi.org/10.1016/j.jfs.2020.100776

[23] Brunnermeier, M. K., & Pedersen, L. H. (2009). Market liquidity and funding liquidity. *The Review of Financial Studies*, *22*(6), 2201–2238. https://doi.org/10.1093/rfs/hhn098

[24] Forino, A., & Morelli, G. (2024). *Liquidity and volatility nexus in cryptocurrency markets* (SSRN Working Paper No. 5128381). https://doi.org/10.2139/ssrn.5128381

[25] FTI Consulting. (2025, December 24). *The crypto crash of October 2025: When leverage met liquidity*. https://www.fticonsulting.com/insights/articles/crypto-crash-october-2025-leverage-met-liquidity

[26] Brauneis, A., Mestel, R., & Theissen, E. (2025). The crypto world trades at tea time: Intraday evidence from centralized exchanges across the globe. *Review of Quantitative Finance and Accounting*, *64*(1), 275–304. https://doi.org/10.1007/s11156-024-01304-1

[27] Al-Yahyaee, K. H., Mensi, W., Ko, H. U., Yoon, S. M., & Kang, S. H. (2020). Why cryptocurrency markets are inefficient: The impact of liquidity and volatility. *The North American Journal of Economics and Finance*, *52*, Article 101168. https://doi.org/10.1016/j.najef.2020.101168

[28] Amihud, Y. (2002). Illiquidity and stock returns: Cross-section and time-series effects. *Journal of Financial Markets*, *5*(1), 31–56. https://doi.org/10.1016/S1386-4181(01)00024-6

[29] Zhang, W., & Li, Y. (2023). Liquidity risk and expected cryptocurrency returns. *International Journal of Finance & Economics*, *28*(1), 472–492. https://doi.org/10.1002/ijfe.2431

[30] Fasanara Digital. (2022, May 21). May 2022 Terraquake. The demise of Luna. *Medium*. https://fasanaradigital.medium.com/terraquake-3080f4b5e5cf

[31] Tian, P., & Zhu, Y. (2025). *Liquidation mechanisms and price impacts in DeFi* (Staff Working Paper 2025-12). Bank of Canada. https://www.bankofcanada.ca/2025/03/staff-working-paper-2025-12/

[32] Zhang, T. (2026). *Funding rate mechanism in perpetual futures* (SSRN Working Paper No. 6185958). https://doi.org/10.2139/ssrn.6185958

[33] Chen, E., Ma, M., & Nie, Z. (2024). Exploring the impact: How decentralized exchange designs shape traders' behavior on perpetual future contracts. *arXiv*. https://arxiv.org/abs/2402.03953

[34] Yi, S., Xu, Z., & Wang, G.-J. (2018). Volatility connectedness in the cryptocurrency market: Is Bitcoin a dominant cryptocurrency? *International Review of Financial Analysis*, *60*, 98–114. https://doi.org/10.1016/j.irfa.2018.08.012

[35] Korkusuz, B. (2025). Volatility transmission in digital assets: Ethereum's rising influence. *Journal of Risk and Financial Management*, *18*(3), 111. https://doi.org/10.3390/jrfm18030111

[36] Diebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. *International Journal of Forecasting*, *28*(1), 57–66. https://doi.org/10.1016/j.ijforecast.2011.02.006

[37] Shahzad, S. J. H., Bouri, E., Kang, S. H., & Saeed, T. (2021). Regime specific spillover across cryptocurrencies and the role of COVID-19. *Financial Innovation*, *7*, Article 5. https://doi.org/10.1186/s40854-020-00210-4

[38] Apostolakis, G. N. (2024). Bitcoin price volatility transmission between spot and futures markets. *International Review of Financial Analysis*, *94*, Article 103251. https://doi.org/10.1016/j.irfa.2024.103251

[39] Schmeling, M., Schrimpf, A., & Todorov, K. (2023). *Crypto carry* (BIS Working Papers No. 1087). Bank for International Settlements. https://www.bis.org/publ/work1087.htm

[40] Fassas, A. P., Papadamou, S., & Koulis, A. (2020). Price discovery in bitcoin futures. *Research in International Business and Finance*, *52*, Article 101116. https://doi.org/10.1016/j.ribaf.2019.101116

[41] Iyer, T. (2022). *Cryptic connections: Spillovers between crypto and equity markets* (Global Financial Stability Notes No. 2022/01). International Monetary Fund.

[42] Elsayed, A. H., & Sousa, R. M. (2024). International monetary policy and cryptocurrency markets: Dynamic and spillover effects. *The European Journal of Finance*, *30*(16), 1855–1875. https://doi.org/10.1080/1351847X.2022.2068375

[43] Katsiampa, P., Corbet, S., & Lucey, B. (2019). Volatility spillover effects in leading cryptocurrencies: A BEKK-MGARCH analysis. *Finance Research Letters*, *29*, 68–74. https://doi.org/10.1016/j.frl.2019.03.009

[44] Ali, Z. (2025). *Anatomy of the Oct 10–11, 2025 crypto liquidation cascade: Macroeconomic triggers, market microstructure, and systemic risk lessons* (SSRN Working Paper No. 5611392). https://doi.org/10.2139/ssrn.5611392

[45] Shiller, R. J. (1981). Do stock prices move too much to be justified by subsequent changes in dividends? *American Economic Review*, *71*(3), 421–436.

[46] Babalos, V., Bouri, E., & Gupta, R. (2025). Does the introduction of US spot Bitcoin ETFs affect spot returns and volatility of major cryptocurrencies? *The Quarterly Review of Economics and Finance*, *102*, Article 102006. https://doi.org/10.1016/j.qref.2025.102006

[47] Morin, A., & Moore, T. (2024). *The impact of cryptocurrency spot price volatility on derivative markets* (SSRN Working Paper No. 4729600). https://doi.org/10.2139/ssrn.4729600

[48] Korkusuz, B., & Sahiner, M. (2025). Coin impact on cross-crypto realized volatility and dynamic cryptocurrency volatility connectedness. *Financial Innovation*, *11*. https://doi.org/10.1186/s40854-025-00881-x

[49] Veloso, V., Gatsios, R. C., Magnani, V. M., & Lima, F. G. (2025). Is Bitcoin's market maturing? Cumulative abnormal returns and volatility in the 2024 halving and past cycles. *Journal of Risk and Financial Management*, *18*(5), 242. https://doi.org/10.3390/jrfm18050242

[50] Atanasova, C., Miao, T., Segarra, I., & Willeboordse, F. (2025). Aggregate illiquidity and crypto option returns. *Finance Research Letters*, *85*(C), Article 108003. https://doi.org/10.1016/j.frl.2025.108003

[51] Cornelli, G., Doerr, S., Frost, J., & Gambacorta, L. (2023). *Crypto shocks and retail losses* (BIS Bulletin No. 69). Bank for International Settlements. https://www.bis.org/publ/bisbull69.htm