Assessing network risk with FRM: links with pricing kernel volatility and application to cryptocurrencies
从理论和实证上建立了金融风险计量器（FRM）指数与资产定价核波动率、最大夏普比率及市场波动率的经济联系，并展示了FRM@Crypto在预测加密货币市场风险方面的稳健能力。
The Financial Risk Meter (FRM) employs Quantile-LASSO regression to identify systemic financial risk and dependencies among tail events across financial assets. This paper establishes, both theoretically and empirically, a meaningful economic relationship between the FRM index, derived from the penalization parameter in quantile LASSO regression, and the volatility of assets' pricing kernels, the attainable maximal Sharpe ratio, and market volatility. Despite the rapid growth of the crypto market and its increasing integration with traditional financial markets, there remains a dearth of risk measures in this space. FRM@Crypto exhibits robust predictive capabilities in anticipating future market risk, potentially filling a critical void in this market.