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Gaussian Mixture and Kernel Density-Based Hybrid Model for Volatility Behavior Extraction From Public Financial Data

Author

Listed:
  • Smail Tigani

    (Euromed Research Center, Engineering Unit, Euro-Mediterranean University, Fes 51, Morocco)

  • Hasna Chaibi

    (SIME Lab, ENSIAS, Mohammed V-Souissi University, Rabat 713, Morocco)

  • Rachid Saadane

    (Electrical Engineering Department, Hassania School of Public Labors, Casablanca 8108, Morocco)

Abstract

This paper carried out a hybrid clustering model for foreign exchange market volatility clustering. The proposed model is built using a Gaussian Mixture Model and the inference is done using an Expectation Maximization algorithm. A mono-dimensional kernel density estimator is used in order to build a probability density based on all historical observations. That allows us to evaluate the behavior’s probability of each symbol of interest. The computation result shows that the approach is able to pinpoint risky and safe hours to trade a given currency pair.

Suggested Citation

  • Smail Tigani & Hasna Chaibi & Rachid Saadane, 2019. "Gaussian Mixture and Kernel Density-Based Hybrid Model for Volatility Behavior Extraction From Public Financial Data," Data, MDPI, vol. 4(1), pages 1-11, January.
    • Handle: RePEc:gam:jdataj:v:4:y:2019:i:1:p:19-:d:200420
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    References listed on IDEAS

    as
    1. Blau, Benjamin M. & Griffith, Todd G. & Whitby, Ryan J., 2018. "The maximum bid-ask spread," Journal of Financial Markets, Elsevier, vol. 41(C), pages 1-16.
    2. Armand Fouejieu,, 2017. "Inflation targeting and financial stability in emerging markets," Economic Modelling, Elsevier, vol. 60(C), pages 51-70.
    3. Jieun Lee & KeeH.Chung, 2017. "The Effect of Market Volatility on Liquidity and Stock Returns in the Korean Stock Market," Working Papers 2017-18, Economic Research Institute, Bank of Korea.
    4. Massomeh Hajilee & Farhang Niroomand, 2018. "The impact of interest rate volatility on financial market inclusion: evidence from emerging markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 42(2), pages 352-368, April.
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    Cited by:

    1. Wang, Yuanrong & Aste, Tomaso, 2023. "Dynamic portfolio optimization with inverse covariance clustering," LSE Research Online Documents on Economics 117701, London School of Economics and Political Science, LSE Library.

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