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Value at risk estimation by quantile regression and kernel estimator

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  • Alex Huang

Abstract

Risk management has attracted a great deal of attention, and Value at Risk (VaR) has emerged as a particularly popular and important measure for detecting the market risk of financial assets. The quantile regression method can generate VaR estimates without distributional assumptions; however, empirical evidence has shown the approach to be ineffective at evaluating the real level of downside risk in out-of-sample examination. This paper proposes a process in VaR estimation with methods of quantile regression and kernel estimator which applies the nonparametric technique with extreme quantile forecasts to realize a tail distribution and locate the VaR estimates. Empirical application of worldwide stock indices with 29 years of data is conducted and confirms the proposed approach outperforms others and provides highly reliable estimates. Copyright Springer Science+Business Media, LLC 2013

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  • Alex Huang, 2013. "Value at risk estimation by quantile regression and kernel estimator," Review of Quantitative Finance and Accounting, Springer, vol. 41(2), pages 225-251, August.
    • Handle: RePEc:kap:rqfnac:v:41:y:2013:i:2:p:225-251
    DOI: 10.1007/s11156-012-0308-x
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    Cited by:

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    2. Yi-hsun Lai & Wen-chang Lin & Liang-wei Kuo, 2018. "Forestalling capital regulation or masking financial weakness? Evidence from loss reserve management in the property–liability insurance industry," Review of Quantitative Finance and Accounting, Springer, vol. 50(2), pages 481-518, February.
    3. Ding Du & Xiaobing Zhao, 2017. "Financial investor sentiment and the boom/bust in oil prices during 2003–2008," Review of Quantitative Finance and Accounting, Springer, vol. 48(2), pages 331-361, February.
    4. May Huaxi Zhang & Stanley Iat-Meng Ko & Andreas Karathanasopoulos & Chia Chun Lo, 2022. "A two-step quantile regression method for discretionary accounting," Review of Quantitative Finance and Accounting, Springer, vol. 59(1), pages 1-22, July.
    5. Shlomo Yitzhaki & Peter Lambert, 2014. "Is higher variance necessarily bad for investment?," Review of Quantitative Finance and Accounting, Springer, vol. 43(4), pages 855-860, November.
    6. Yu-Yen Ku & Tze-Yu Yen, 2016. "Heterogeneous Effect of Financial Leverage on Corporate Performance: A Quantile Regression Analysis of Taiwanese Companies," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 19(03), pages 1-33, September.

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    More about this item

    Keywords

    Value at risk; Quantile regression; Kernel estimator; C10; C53; G10; G17;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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