Using Exponentially Weighted Quantile Regression to Estimate Value at Risk and Expected Shortfall

Posted: 17 Jun 2008

See all articles by James W. Taylor

James W. Taylor

University of Oxford - Said Business School

Date Written: Summer 2008

Abstract

We propose exponentially weighted quantile regression (EWQR) for estimating time-varying quantiles. The EWQR cost function can be used as the basis for estimating the time-varying expected shortfall associated with the EWQR quantile forecast. We express EWQR in a kernel estimation framework, and then modify it by adapting a previously proposed double kernel estimator in order to provide greater accuracy for tail quantiles that are changing relatively quickly over time. We introduce double kernel quantile regression, which extends the double kernel idea to the modeling of quantiles in terms of regressors. In our empirical study of 10 stock returns series, the versions of the new methods that do not accommodate the leverage effect were able to outperform GARCH-based methods and CAViaR models.

Keywords: C22, C53, G10, exponential weighting, financial risk, kernel smoothing, kernel density estimation, quantile regression

Suggested Citation

Taylor, James W., Using Exponentially Weighted Quantile Regression to Estimate Value at Risk and Expected Shortfall (Summer 2008). Journal of Financial Econometrics, Vol. 6, Issue 3, pp. 382-406, 2008, Available at SSRN: https://ssrn.com/abstract=1146712 or http://dx.doi.org/nbn007

James W. Taylor (Contact Author)

University of Oxford - Said Business School ( email )

Park End Street
Oxford, OX1 1HP
Great Britain

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