Abstract

Beyond their importance from a regulatory policy point of view, risk measures such as Value-at-Risk (VaR) and Expected Shortfall (ES) play an important role in risk management, portfolio allocation, capital level requirements, trading systems, and hedging strategies. Although there are several methods in the literature to estimate both risk measures, most of them are badly affected by the curse of dimensionality and the presence of extreme observations, which are common characteristics in financial time series. To overcome these issues, we propose a new procedure based on filtered historical simulation, the general dynamic factor model, and robust volatility models. The new procedure is applied in US stocks and the calibration tests and scoring function values indicate that both VaR and ES estimated using our proposal outperform several existing alternatives.