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

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  • Eliana Christou
  • Michael Grabchak

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.

Suggested Citation

  • Eliana Christou & Michael Grabchak, 2019. "Estimation of value-at-risk using single index quantile regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 46(13), pages 2418-2433, October.
    • Handle: RePEc:taf:japsta:v:46:y:2019:i:13:p:2418-2433
    DOI: 10.1080/02664763.2019.1597028
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    Cited by:

    1. Rita Pimentel & Morten Risstad & Sjur Westgaard, 2022. "Predicting interest rate distributions using PCA & quantile regression," Digital Finance, Springer, vol. 4(4), pages 291-311, December.
    2. Yan Fang & Jian Li & Yinglin Liu & Yunfan Zhao, 2023. "Semiparametric estimation of expected shortfall and its application in finance," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 835-851, July.
    3. Eliana Christou & Michael Grabchak, 2022. "Estimation of Expected Shortfall Using Quantile Regression: A Comparison Study," Computational Economics, Springer;Society for Computational Economics, vol. 60(2), pages 725-753, August.

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