64th ISI World Statistics Congress - Ottawa, Canada

64th ISI World Statistics Congress - Ottawa, Canada

Forecasting Value-at-Risk and Expected Shortfall in Large Panels: A Robust General Dynamic Factor Approach.


64th ISI World Statistics Congress - Ottawa, Canada

Format: IPS Abstract

Keywords: high-dimensional, time series

Session: IPS 433 - High-Dimensional Financial Time Series

Tuesday 18 July 10 a.m. - noon (Canada/Eastern)


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.