Title: Forecasting VaR and ES using a joint quantile regression and its implications in portfolio allocation
Authors: Luca Merlo - Sapienza University of Rome (Italy) [presenting]
Lea Petrella - Sapienza University of Rome (Italy)
Valentina Raponi - IESE Business School (Spain)
Abstract: A multivariate quantile regression framework is proposed to forecast Value at Risk (VaR) and Expected Shortfall (ES) of multiple financial assets simultaneously. We generalize the Multivariate Asymmetric Laplace (MAL) joint quantile regression to a time-varying setting, which allows us to specify a dynamic process for the evolution of both the VaR and ES of each asset. The proposed methodology accounts for the dependence structure among asset returns. By exploiting the properties of the MAL distribution, we propose a new portfolio optimization method that minimizes portfolio risk and controls for well-known characteristics of financial data. We evaluate the advantages of the proposed approach on both simulated and real data, using weekly returns on three major stock market indices. We show that our method outperforms other existing models and provides more accurate risk measure forecasts than univariate methods.