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Probabilistic fuzzy systems in value‐at‐risk estimation

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  • R. J. Almeida
  • U. Kaymak

Abstract

Value‐at‐risk (VaR) is a popular measure for quantifying the market risk that a financial institution faces into a single number. Owing to the complexity of financial markets, the risks associated with a portfolio varies over time. Consequently, advanced methods of VaR estimation use parametric conditional models of portfolio volatility (e.g. generalized autoregressive heteroscedasticity (GARCH) models) to adapt risk estimation to changing market conditions. However, more flexible semi‐parametric methods that adapt to the highly flexible underlying data distribution are better suited for accurate VaR estimation. In this paper, we consider VaR estimation by using probabilistic fuzzy systems (PFSs). A PFS is a semi‐parametric method that combines a linguistic description of the system behaviour with statistical properties of the data. Therefore, they provide the potential to adapt estimations of probability density to the linguistic framework of the modeller. We study two approaches to designing probabilistic fuzzy VaR models and compare their performances with the performance of a GARCH model. It is found that statistical back testing always accepts PFS models after tuning, whereas GARCH models may be rejected. Copyright © 2009 John Wiley & Sons, Ltd.

Suggested Citation

  • R. J. Almeida & U. Kaymak, 2009. "Probabilistic fuzzy systems in value‐at‐risk estimation," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 16(1‐2), pages 49-70, January.
  • Handle: RePEc:wly:isacfm:v:16:y:2009:i:1-2:p:49-70
    DOI: 10.1002/isaf.293
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    References listed on IDEAS

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    1. Zmeskal, Zdenek, 2005. "Value at risk methodology of international index portfolio under soft conditions (fuzzy-stochastic approach)," International Review of Financial Analysis, Elsevier, vol. 14(2), pages 263-275.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Gilles Teyssière & Alan P. Kirman (ed.), 2007. "Long Memory in Economics," Springer Books, Springer, number 978-3-540-34625-8, September.
    4. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    5. van den Berg, J.H. & van den Bergh, W.-M. & Kaymak, U., 2003. "Financial Markets Analysis by Probabilistic Fuzzy Modelling," ERIM Report Series Research in Management ERS-2003-036-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    6. Rama Cont, 2007. "Volatility Clustering in Financial Markets: Empirical Facts and Agent-Based Models," Springer Books, in: Gilles Teyssière & Alan P. Kirman (ed.), Long Memory in Economics, pages 289-309, Springer.
    7. Zmeskal, Zdenek, 2005. "Value at risk methodology under soft conditions approach (fuzzy-stochastic approach)," European Journal of Operational Research, Elsevier, vol. 161(2), pages 337-347, March.
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