Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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A kernel machine for estimation of mean and volatility functions

  • Shim, Joo-Yong (Department of Applied Statistics, Catholic University of Daegu) ;
  • Park, Hye-Jung (Computer Course Division of General Education and Teacher's Certification, Daegu University) ;
  • Hwang, Chang-Ha (Department of Statistics, Dankook University)
  • Published : 2009.09.30
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Abstract

We propose a doubly penalized kernel machine (DPKM) which uses heteroscedastic location-scale model as basic model and estimates both mean and volatility functions simultaneously by kernel machines. We also present the model selection method which employs the generalized approximate cross validation techniques for choosing the hyperparameters which affect the performance of DPKM. Artificial examples are provided to indicate the usefulness of DPKM for the mean and volatility functions estimation.

Keywords

References

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