Computational Statistics

, Volume 27, Issue 4, pp 685–700

Estimating value at risk with semiparametric support vector quantile regression


  • Jooyong Shim
    • Department of Data Science and Institute of Statistical InformationInje University
  • Yongtae Kim
    • Department of StatisticsDankook University
  • Jangtaek Lee
    • Department of StatisticsDankook University
    • Department of StatisticsDankook University
Original Paper

DOI: 10.1007/s00180-011-0283-z

Cite this article as:
Shim, J., Kim, Y., Lee, J. et al. Comput Stat (2012) 27: 685. doi:10.1007/s00180-011-0283-z


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.


EWMAGARCHt-GARCHQuantile regressionSemiparametric support vector quantile regressionValue at risk

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© Springer-Verlag 2011