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Using HMM Approach for Assessing Quality of Value at Risk Estimation: Evidence from PSE Listed Company

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  • Tomáš Konderla

    (Department of Statistics and Operation analysis, Faculty of Business and Economics, Mendel University in Brno, Zemědělská 1, 613 00 Brno, Czech Republic)

  • Václav Klepáč

    (Department of Statistics and Operation analysis, Faculty of Business and Economics, Mendel University in Brno, Zemědělská 1, 613 00 Brno, Czech Republic)

Abstract

The article points out the possibilities of using Hidden Markov model (abbrev. HMM) for estimation of Value at Risk metrics (abbrev. VaR) in sample. For the illustration we use data of the company listed on Prague Stock Exchange in range from January 2011 to June 2016. HMM approach allows us to classify time series into different states based on their development characteristic. Due to a deeper shortage of existing domestic results or comparison studies with advanced volatility governed VaR forecasts we tested HMM with univariate ARMA-GARCH model based VaR estimates. The common testing via Kupiec and Christoffersen procedures offer generalization that HMM model performs better that volatility based VaR estimation technique in terms of accuracy, even with the simpler HMM with normal-mixture distribution against previously used GARCH with many types of non-normal innovations.

Suggested Citation

  • Tomáš Konderla & Václav Klepáč, 2017. "Using HMM Approach for Assessing Quality of Value at Risk Estimation: Evidence from PSE Listed Company," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 65(5), pages 1687-1694.
    • Handle: RePEc:mup:actaun:actaun_2017065051687
    DOI: 10.11118/actaun201765051687
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    References listed on IDEAS

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