Quantifying systemic risk in cryptocurrency markets: A high-frequency approach

Abstract

This study compares two approaches for measuring Conditional Value-atRisk (CoVaR), emphasizing the role of high-frequency intraday data in assessing systemic risk within financial systems. The first approach, AB CoVaR, estimates the risk of an asset Y conditional on another asset X being exactly at its Value-at-Risk (VaR) threshold. In contrast, the GE CoVaR refines this measure by capturing the risk of Y when X exceeds its VaR threshold, thereby accounting for more extreme scenarios and larger potential losses. To estimate these CoVaR measures, we employ high-frequency data sampled at five-minute intervals from major cryptocurrencies, including Bitcoin, Ethereum, Ripple, Solana, and Binance Coin. The results indicate that the GE CoVaR approach systematically yields higher risk estimates and exhibits superior predictive performance when applied to intraday data. Moreover, the analysis reveals strong interconnectedness among cryptocurrency returns. Bitcoin and Ethereum emerge as the primary sources of systemic risk, whereas Solana and Binance Coin are the most heavily affected assets. These findings underscore the granular risk dynamics captured through intraday analysis.