An Application Of Extreme Value Theory To Cryptocurrencies Economics Letters

An Application of Extreme Value Theory to Cryptocurrencies

Extreme Value Analysis and Risk Measurement

In the realm of finance, understanding the tail behavior of asset returns is crucial for risk management and portfolio optimization. Extreme value theory (EVT) provides a powerful framework for analyzing and modeling extreme events in financial markets. In this study, we employ EVT to investigate the tail behavior of five major cryptocurrencies: Bitcoin, Ethereum, Litecoin, Ripple, and Bitcoin Cash.

Value-at-Risk and Expected Shortfall

We estimate Value-at-Risk (VaR) and Expected Shortfall (ES) using extreme value modeling. VaR measures the potential loss in a portfolio over a given confidence level, while ES measures the expected loss beyond VaR. By using EVT, we can capture the heavy-tailed nature of cryptocurrency returns and provide more accurate risk estimates compared to traditional methods.

Extreme Correlation in Cryptocurrency Markets

Furthermore, we investigate the extreme correlation between cryptocurrency markets. Extreme correlation measures the co-movement of extreme events across different assets. Our analysis reveals that cryptocurrency markets exhibit significant extreme correlation, indicating that extreme events in one cryptocurrency can have a spillover effect on other cryptocurrencies.

Implications for Investors and Policymakers

Our findings have important implications for investors and policymakers. Investors should be aware of the tail risk associated with cryptocurrencies and consider incorporating EVT-based risk measures into their investment strategies. Policymakers should monitor the extreme correlation between cryptocurrency markets and consider appropriate regulatory frameworks to mitigate systemic risk.