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Dynamic factor value-at-risk for large, heteroskedastic portfolios

Sirio Aramonte, Marius Rodriguez () and Jason Wu

No 2011-19, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (US)

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.

Keywords: Portfolio management; Financial risk management; Econometric models (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ban, nep-cmp, nep-ecm and nep-rmg
Date: 2011
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2) Track citations by RSS feed

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Journal Article: Dynamic factor Value-at-Risk for large heteroskedastic portfolios (2013) Downloads
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