Evaluating Predictive Performance of Value-at-Risk Models in Emerging Markets: A Reality Check

Journal of Forecasting, Vol. 25 (2), 101-128, 2006, DOI: 10.1002/for.977

Posted: 27 Feb 2016

See all articles by Yong Bao

Yong Bao

Purdue University

Tae-Hwy Lee

University of California, Riverside (UCR) - Department of Economics

Burak Saltoglu

Marmara University

Date Written: February 25, 2006

Abstract

We investigate the predictive performance of various classes of value-at-risk (VaR) models in several dimensions — unfiltered versus filtered VaR models, parametric versus nonparametric distributions, conventional versus extreme value distributions, and quantile regression versus inverting the conditional distribution function. By using the reality check test of White (2000), we compare the predictive power of alternative VaR models in terms of the empirical coverage probability and the predictive quantile loss for the stock markets of five Asian economies that suffered from the 1997-1998 financial crisis. The results based on these two criteria are largely compatible and indicate some empirical regularities of risk forecasts. The Riskmetrics model behaves reasonably well in tranquil periods, while some extreme value theory (EVT)-based models do better in the crisis period. Filtering often appears to be useful for some models, particularly for the EVT models, though it could be harmful for some other models. The CaViaR quantile regression models of Engle and Manganelli (2004) have shown some success in predicting the VaR risk measure for various periods, generally more stable than those that invert a distribution function. Overall, the forecasting performance of the VaR models considered varies over the three periods before, during and after the crisis.

Keywords: CaViaR, coverage probability, filtering, quantile loss, reality check, stress testing, VaR

Suggested Citation

Bao, Yong and Lee, Tae-Hwy and Saltoglu, Burak, Evaluating Predictive Performance of Value-at-Risk Models in Emerging Markets: A Reality Check (February 25, 2006). Journal of Forecasting, Vol. 25 (2), 101-128, 2006, DOI: 10.1002/for.977, Available at SSRN: https://ssrn.com/abstract=2738156

Yong Bao

Purdue University ( email )

Department of Economics
West Lafayette, IN 47907
United States

Tae-Hwy Lee (Contact Author)

University of California, Riverside (UCR) - Department of Economics ( email )

900 University Avenue
4136 Sproul Hall
Riverside, CA 92521
United States

Burak Saltoglu

Marmara University ( email )

Goztepe Campus
Istanbul, Istanbul 34840
Turkey

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