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Vine Copula-Cross Entropy Evaluation of Dependence Structure and Financial Risk in Agricultural Commodity Index Returns

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Book cover Modeling Dependence in Econometrics

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 251))

Abstract

Many studies used the empirical Kendall’s tau to select a preferable ordering of vine copulas or to fix such a sequence. In this study, for high dimension vine copulas, we propose the vine copula based cross entropy method to figure out a more appropriate ordering of the vine copula. The goal of this study is to estimate the non-conditional, conditional, and tail dependences for agricultural price index returns by using the C-vine and D-vine copula based cross entropy model. In addition, we show that a framework uses the Monte Carlo simulation and the results of vine copula to estimate the expected shortfall (ES) of an equally weighted portfolio. The optimal portfolio allocations can also be estimated using global optimization with the differential evolution algorithm.

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Author information

Authors and Affiliations

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

    Songsak Sriboonchitta & Jianxu Liu

  2. Department of Agricultural Economics and Agricultural Extension, Faculty of Agriculture, Chiang Mai University, Chiang Mai, 50200, Thailand

    Aree Wiboonpongse

Authors
  1. Songsak Sriboonchitta
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  2. Jianxu Liu
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  3. Aree Wiboonpongse
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Corresponding author

Correspondence to Songsak Sriboonchitta .

Editor information

Editors and Affiliations

  1. APAN Advanced Institute of Science and Technology, Ishikawa, Japan

    Van-Nam Huynh

  2. Department of Computer Science, University of Texas at El Paso, El Paso, Texas, USA

    Vladik Kreinovich

  3. Faculty of Economics, Chiangmai University, Chiangmai, Thailand

    Songsak Sriboonchitta

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© 2014 Springer International Publishing Switzerland

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Sriboonchitta, S., Liu, J., Wiboonpongse, A. (2014). Vine Copula-Cross Entropy Evaluation of Dependence Structure and Financial Risk in Agricultural Commodity Index Returns. In: Huynh, VN., Kreinovich, V., Sriboonchitta, S. (eds) Modeling Dependence in Econometrics. Advances in Intelligent Systems and Computing, vol 251. Springer, Cham. https://doi.org/10.1007/978-3-319-03395-2_18

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  • DOI: https://doi.org/10.1007/978-3-319-03395-2_18

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03394-5

  • Online ISBN: 978-3-319-03395-2

  • eBook Packages: EngineeringEngineering (R0)

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