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Estimation of Volatility on the Small Sample with Generalized Maximum Entropy

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Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10758))

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Abstract

Generalized autoregressive conditional heteroscedasticity (GARCH) provides useful techniques for modeling the dynamic volatility model. Several estimation techniques have been developed over the years, for examples Maximum likelihood, Bayesian, and Entropy. Among these, entropy can be considered an efficient tool for estimating GARCH model since it does not require any distribution assumptions which must be given in Maximum likelihood and Bayesian estimators. Moreover, we address the problem of estimating GARCH model characterized by ill-posed features. We introduce a GARCH framework based on the Generalized Maximum Entropy (GME) estimation method. Finally, in order to better highlight some characteristics of the proposed method, we perform a Monte Carlo experiment and we analyze a real case study. The results show that entropy estimator is successful in estimating the parameters in GARCH model and the estimated parameters are close to the true values.

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Acknowledgement

The authors thank Mr. Woraphon Yamaka for his suggestions for GME method and applied to the GARCH(1,1) model. We would like to thank the referee for giving comments on the manuscript.

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Authors and Affiliations

  1. Faculty of Economics, Chiang Mai University, Chiang Mai, 50200, Thailand

    Quanrui Song, Songsak Sriboonchitta & Somsak Chanaim

  2. Center of Excellence in Econometrics, Chiang Mai University, Chiang Mai, 50200, Thailand

    Songsak Sriboonchitta & Somsak Chanaim

  3. Faculty of Commerce and Management, Prince of Songkla University, Trang Campus, Trang, 92000, Thailand

    Chongkolnee Rungruang

Authors
  1. Quanrui Song
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  2. Songsak Sriboonchitta
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  3. Somsak Chanaim
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  4. Chongkolnee Rungruang
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Corresponding author

Correspondence to Quanrui Song .

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Editors and Affiliations

  1. Japan Advanced Institute of Science and Technology, Nomi, Japan

    Van-Nam Huynh

  2. Osaka University, Osaka, Japan

    Masahiro Inuiguchi

  3. Hanoi National University of Education, Hanoi, Vietnam

    Dang Hung Tran

  4. Université de Technologie de Compiègne, Compiègne, France

    Thierry Denoeux

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Song, Q., Sriboonchitta, S., Chanaim, S., Rungruang, C. (2018). Estimation of Volatility on the Small Sample with Generalized Maximum Entropy. In: Huynh, VN., Inuiguchi, M., Tran, D., Denoeux, T. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2018. Lecture Notes in Computer Science(), vol 10758. Springer, Cham. https://doi.org/10.1007/978-3-319-75429-1_27

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  • DOI: https://doi.org/10.1007/978-3-319-75429-1_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75428-4

  • Online ISBN: 978-3-319-75429-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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