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Board of Governors of the Federal Reserve System (US)
Finance and Economics Discussion Series
Dynamic factor value-at-risk for large, heteroskedastic portfolios
Sirio Aramonte
Marius del Giudice Rodriguez
Jason J. Wu
Abstract

Trading portfolios at Financial institutions are typically driven by a large number of financial variables. These variables are often correlated with each other and exhibit by time-varying volatilities. We propose a computationally efficient Value-at-Risk (VaR) methodology based on Dynamic Factor Models (DFM) that can be applied to portfolios with time-varying weights, and that, unlike the popular Historical Simulation (HS) and Filtered Historical Simulation (FHS) methodologies, can handle time-varying volatilities and correlations for a large set of financial variables. We test the DFM-VaR on three stock portfolios that cover the 2007-2009 financial crisis, and find that it reduces the number and average size of back-testing breaches relative to HS-VaR and FHS-VaR. DFM-VaR also outperforms HS-VaR when applied risk measurement of individual stocks that are exposed to systematic risk.


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Sirio Aramonte & Marius del Giudice Rodriguez & Jason J. Wu, Dynamic factor value-at-risk for large, heteroskedastic portfolios, Board of Governors of the Federal Reserve System (US), Finance and Economics Discussion Series 2011-19, 2011.
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Keywords: Portfolio management ; Financial risk management ; Econometric models
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