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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.
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