Forecasting Value-at-Risk and Expected Shortfall in Large Portfolios: a General Dynamic Factor Approach
Marc Hallin () and
No 2020-50, Working Papers ECARES from ULB -- Universite Libre de Bruxelles
Beyond their importance from a 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 in large portfolios is quite a challenge. To tackle this problem, we propose 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. The procedure is applied to a panel with concentration ratio close to one. Back-testing and scoring results indicate that both VaR and ES are accurately estimated under our method, which outperforms alternative approaches available in the literature.
Keywords: conditional covariance; high-dimensional time series; large panels; risk measures; volatility (search for similar items in EconPapers)
JEL-codes: C10 C32 C53 G17 G32 (search for similar items in EconPapers)
Pages: 25 p.
New Economics Papers: this item is included in nep-ecm, nep-for, nep-ore and nep-rmg
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