Forecasting Value-at-Risk and Expected Shortfall in Large Portfolios: A General Dynamic Factor Model Approach

26 Pages Posted: 2 Feb 2021 Last revised: 10 Mar 2021

See all articles by Marc Hallin

Marc Hallin

ECARES, Universite Libre de Bruxelles

Carlos Trucíos

Universidade Federal do Rio de Janeiro (UFRJ) - Faculdade de Administracao e Ciencias Contabeis - FACC

Date Written: March 7, 2021

Abstract

Beyond their importance from the regulatory policy point of view, 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. Unfortunately, due to the curse of dimensionality, their accurate estimation and forecast in large portfolios is quite a challenge. To tackle this problem, two procedures are proposed, being one of them based on a filtered historical simulation method in which high-dimensional conditional covariance matrices are estimated via a general dynamic factor model with infinite-dimensional factor space and conditionally heteroscedastic factors, and the other one based on a residual-based bootstrap scheme. The procedures are applied to a panel with concentration ratio close to one. Backtesting and scoring results indicate that both VaR and ES are accurately estimated under our methods, which outperforms alternative approaches available in the literature.

Keywords: conditional covariance, high-dimensional time series, large panels, risk measures, volatility

JEL Classification: C10, C32, C53, C55, G17, G32

Suggested Citation

Hallin, Marc and Trucíos Maza, Carlos César, Forecasting Value-at-Risk and Expected Shortfall in Large Portfolios: A General Dynamic Factor Model Approach (March 7, 2021). Available at SSRN: https://ssrn.com/abstract=3748736 or http://dx.doi.org/10.2139/ssrn.3748736

Marc Hallin

ECARES, Universite Libre de Bruxelles ( email )

Ave. Franklin D Roosevelt, 50 - C.P. 114
Brussels, B-1050
Belgium
+32 2 650 5886 (Phone)
+32 2 650 5899 (Fax)

Carlos César Trucíos Maza (Contact Author)

Universidade Federal do Rio de Janeiro (UFRJ) - Faculdade de Administracao e Ciencias Contabeis - FACC ( email )

Av. Pasteur 250
Rio de Janeiro, 22290-240
Brazil

HOME PAGE: http://https://ctruciosm.github.io

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