Original Paper

Computational Statistics

, Volume 27, Issue 4, pp 685-700

First online:

Estimating value at risk with semiparametric support vector quantile regression

  • Jooyong ShimAffiliated withDepartment of Data Science and Institute of Statistical Information, Inje University
  • , Yongtae KimAffiliated withDepartment of Statistics, Dankook University
  • , Jangtaek LeeAffiliated withDepartment of Statistics, Dankook University
  • , Changha HwangAffiliated withDepartment of Statistics, Dankook University Email author 

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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.


EWMA GARCH t-GARCH Quantile regression Semiparametric support vector quantile regression Value at risk