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Estimating value at risk with semiparametric support vector quantile regression

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  • Jooyong Shim
  • Yongtae Kim
  • Jangtaek Lee
  • Changha Hwang

Abstract

Value at Risk (VaR) has been used as an important tool to measure the market risk under normal market. Usually the VaR of log returns is calculated by assuming a normal distribution. However, log returns are frequently found not normally distributed. This paper proposes the estimation approach of VaR using semiparametric support vector quantile regression (SSVQR) models which are functions of the one-step-ahead volatility forecast and the length of the holding period, and can be used regardless of the distribution. We find that the proposed models perform better overall than the variance-covariance and linear quantile regression approaches for return data on S&P 500, NIKEI 225 and KOSPI 200 indices. Copyright Springer-Verlag 2012

Suggested Citation

  • Jooyong Shim & Yongtae Kim & Jangtaek Lee & Changha Hwang, 2012. "Estimating value at risk with semiparametric support vector quantile regression," Computational Statistics, Springer, vol. 27(4), pages 685-700, December.
    • Handle: RePEc:spr:compst:v:27:y:2012:i:4:p:685-700
    DOI: 10.1007/s00180-011-0283-z
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    References listed on IDEAS

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    1. Shim, Jooyong & Hwang, Changha, 2009. "Support vector censored quantile regression under random censoring," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 912-919, February.
    2. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    4. Keith Kuester & Stefan Mittnik & Marc S. Paolella, 2006. "Value-at-Risk Prediction: A Comparison of Alternative Strategies," Journal of Financial Econometrics, Oxford University Press, vol. 4(1), pages 53-89.
    5. Paul Glasserman & Philip Heidelberger & Perwez Shahabuddin, 2002. "Portfolio Value‐at‐Risk with Heavy‐Tailed Risk Factors," Mathematical Finance, Wiley Blackwell, vol. 12(3), pages 239-269, July.
    6. Li, Youjuan & Liu, Yufeng & Zhu, Ji, 2007. "Quantile Regression in Reproducing Kernel Hilbert Spaces," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 255-268, March.
    7. Yuan, Ming, 2006. "GACV for quantile smoothing splines," Computational Statistics & Data Analysis, Elsevier, vol. 50(3), pages 813-829, February.
    8. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    9. Bollerslev, Tim, 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 542-547, August.
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    Cited by:

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    3. Herman Mørkved Blom & Petter Eilif de Lange & Morten Risstad, 2023. "Estimating Value-at-Risk in the EURUSD Currency Cross from Implied Volatilities Using Machine Learning Methods and Quantile Regression," JRFM, MDPI, vol. 16(7), pages 1-23, June.
    4. Zhouwei Wang & Qicheng Zhao & Min Zhu & Tao Pang, 2020. "Jump Aggregation, Volatility Prediction, and Nonlinear Estimation of Banks’ Sustainability Risk," Sustainability, MDPI, vol. 12(21), pages 1-17, October.
    5. Bilin Shao & Zixuan Yao & Yifan Qiang, 2023. "Point-Interval Forecasting for Electricity Load Based on Regular Fluctuation Component Extraction," Energies, MDPI, vol. 16(4), pages 1-20, February.

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