Quantifying Systemic Risk in Cryptocurrency Markets: A High-Frequency Approach

Quantifying Systemic Risk in Cryptocurrency Markets: A High-Frequency Approach

• Author
• João Pedro Malim Franco
• Co-authors
• Márcio P. Laurini
• Abstract

• We conduct a comparative analysis of the original $\text{CoVaR}^=_{\alpha, \beta}(Y|X)$ and  modified $\text{CoVaR}_{\alpha, \beta}(Y|X)$ measures of Conditional Value-at-Risk (CoVaR) using high-frequency returns of Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Solana (SOL), and Binance Coin (BNB) at 5-minute intervals. Additionally, we employ the Kolmogorov-Smirnov (KS) bootstrapping test to assess potential interdependencies among cryptocurrency returns. Our results indicate that, on average, estimates derived from CoVaR$_{\alpha,\beta}(Y|X)$ tend to surpass those from CoVaR$^{=}_{\alpha,\beta}(Y|X)$, with superior performance in the backtesting analysis. Moreover, the Kolmogorov-Smirnov (KS) test underscores a notable degree of interconnectedness within cryptocurrency returns.

• Keywords
• Conditional Value-at-Risk (CoVaR), Cryptocurrencies, Backtesting
• Modality
• Comunicação oral
• Subject Area
• Econometria Financeira (Financial Econometrics)

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