Value at Risk models of autoregressive quantiles for improved performance on financial criteria

41 Pages Posted: 24 Aug 2015 Last revised: 25 Oct 2017

See all articles by Gelly Mitrodima

Gelly Mitrodima

London School of Economics & Political Science (LSE) - Department of Statistics

Jaideep S. Oberoi

Kent Business School

Date Written: October 25, 2017

Abstract

We study alternative specifications of conditional quantile models that are used to estimate Value at Risk (VaR). Our proposed specifications include the incorporation of a slow moving component in the quantile process, along with recent aggregate returns as regressors. We consider a range of criteria with the aim of identifying models with improved performance in both statistical and financial terms. These criteria include the potential to lower transaction costs and realized losses in excess of the VaR on exceedance days. We find that for many assets, the proposed specifications lead to improved performance.

Keywords: Value at Risk; CAViaR model; Component model; Conditional loss; Conditional excess loss

JEL Classification: G11, C58

Suggested Citation

Mitrodima, Gelly and Oberoi, Jaideep S., Value at Risk models of autoregressive quantiles for improved performance on financial criteria (October 25, 2017). Available at SSRN: https://ssrn.com/abstract=2649348 or http://dx.doi.org/10.2139/ssrn.2649348

Gelly Mitrodima

London School of Economics & Political Science (LSE) - Department of Statistics ( email )

Houghton Street
London, England WC2A 2AE
United Kingdom

Jaideep S. Oberoi (Contact Author)

Kent Business School ( email )

University of Kent
Sibson Building, Parkwood Road
Canterbury, Kent CT2 7FS
United Kingdom

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