Crashing Together, Rallying Apart: Dynamic Conditional Tail Dependence in Cryptocurrency Markets

2026-06-15

AI Summary

Traditional covariance-based measures struggle to capture tail dependence in cryptocurrency markets during extreme market conditions, often underestimating systemic risk and overestimating diversification benefits. This study pioneers the application of a dynamic Hüsler–Reiss extremal graphical model to characterize upper- and lower-tail dependence among crypto assets, integrating rolling-window analysis with conditional tail dependence estimation to examine the evolving dependence structure of thirteen major cryptocurrencies from late 2021 through 2025. The findings reveal that lower-tail dependence remains highly stable and nearly fully connected, whereas upper-tail connectivity becomes increasingly sparse over time, exhibiting distinct sectoral clustering. Compared to conventional Gaussian graphical models, the extremal graph approach uncovers that the probability of a market-wide crash has been underestimated by approximately eightfold, substantially enhancing the capacity for systemic risk monitoring.

Abstract

Cryptocurrency markets are prone to violent, synchronised drawdowns, challenging the claim that a basket of crypto-assets offers genuine internal diversification. Because standard covariance-based metrics fail to capture asymptotic tail dependence, they systematically understate systemic risk and overstate diversification benefits precisely when markets crash. This study maps the conditional dependence structure of the cryptocurrency market directly in the joint tails, isolating direct extremal linkages from those mediated by the rest of the system. We analyse the daily returns of the thirteen largest cryptocurrencies over a sequence of 89 overlapping windows spanning late 2021 to 2025. We apply dynamic Hüsler-Reiss graphical models of extremes, estimated separately for joint crashes and rallies, and benchmark them against a Gaussian graphical model of ordinary co-movement. The results reveal a near-complete and stable lower-tail graph, an upper tail that thins over time to re-form sectoral structures, and the dissolution of ordinary token categories into a single block anchored by a Bitcoin-Ethereum core. These findings imply that intra-crypto diversification fails on the downside, standard risk models underestimate market-wide crash probabilities by roughly eight-fold, and dynamic extremal graphs offer a superior tool for systemic risk monitoring.

Problem

• tail dependence
• cryptocurrency markets
• systemic risk
• diversification
• extreme events

Innovation

• dynamic Hüsler-Reiss graphical models
• conditional tail dependence
• extremal graphical models
• systemic risk
• cryptocurrency diversification