Dynamic factor value-at-risk for large, heteroskedastic portfolios
Marius Rodriguez () and
No 2011-19, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (US)
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.
Keywords: Portfolio management; Financial risk management; Econometric models (search for similar items in EconPapers)
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Journal Article: Dynamic factor Value-at-Risk for large heteroskedastic portfolios (2013)
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