Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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Forecasting volatility via conditional autoregressive value at risk model based on support vector quantile regression

  • Shim, Joo-Yong (Department of Data Science, Institute of Statistical Information, Inje University) ;
  • Hwang, Chang-Ha (Department of Statistics, Dankook University)
  • Received : 2011.04.01
  • Accepted : 2011.05.01
  • Published : 2011.05.31
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Abstract

The conditional autoregressive value at risk (CAViaR) model is useful for risk management, which does not require the assumption that the conditional distribution does not vary over time but the volatility does. But it does not provide volatility forecasts, which are needed for several important applications such as option pricing and portfolio management. For a variety of probability distributions, it is known that there is a constant relationship between the standard deviation and the distance between symmetric quantiles in the tails of the distribution. This inspires us to use a support vector quantile regression (SVQR) for volatility forecasts with the distance between CAViaR forecasts of symmetric quantiles. Simulated example and real example are provided to indicate the usefulness of proposed forecasting method for volatility.

Keywords

References

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