Estimation of value-at-risk using single index quantile regression

Eliana Christou and Michael Grabchak

Journal of Applied Statistics, 2019, vol. 46, issue 13, 2418-2433

Abstract: Value-at-Risk (VaR) is one of the best known and most heavily used measures of financial risk. In this paper, we introduce a non-iterative semiparametric model for VaR estimation called the single index quantile regression time series (SIQRTS) model. To test its performance, we give an application to four major US market indices: the S&P 500 Index, the Russell 2000 Index, the Dow Jones Industrial Average, and the NASDAQ Composite Index. Our results suggest that this method has a good finite sample performance and often outperforms a number of commonly used methods.

Date: 2019
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DOI: 10.1080/02664763.2019.1597028

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