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Granular, Soft and Fuzzy Approaches for Intelligent Systems pp 119–139Cite as

Evolving Possibilistic Fuzzy Modeling and Application in Value-at-Risk Estimation

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Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 344))

Abstract

This chapter suggests an evolving possibilistic fuzzy modeling approach for value-at-risk modeling and estimation. The modeling approach is based on an extension of the possibilistic fuzzy c-means clustering and functional fuzzy rule-based systems. It employs memberships and typicalities to update clusters centers and creates new clusters using a statistical control distance-based criteria. Evolving possibilistic fuzzy modeling (ePFM) also uses an utility measure to evaluate the quality of the current cluster structure. The fuzzy rule-based model emerges from the cluster structure. Market risk exposure plays a key role for financial institutions in risk assessment and management. A way to measure risk exposure is to evaluate the losses likely to incur when the prices of the portfolio assets decline. Value-at-risk (VaR) estimate is amongst the most prominent measure of financial downside market risk. Computational experiments are conducted to evaluate ePFM for value-at-risk estimation using data of the main equity market indexes of United States (S&P 500) and Brazil (Ibovespa) from January 2000 to December 2012. Econometric models benchmarks such as GARCH and EWMA, and state of the art evolving approaches are compared against ePFM. The results suggest that ePFM is a potential candidate for VaR modeling and estimation because it achieves higher performance than econometric and alternative evolving approaches.

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Notes

  1. 1.

    The computation details are found in [19].

  2. 2.

    The data was provided by Bloomberg.

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Acknowledgments

The authors thank the Brazilian Ministry of Education (CAPES), the Brazilian National Research Council (CNPq) grant 304596/2009-4, and the Research of Foundation of the State of São Paulo (FAPESP) for their support.

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Authors and Affiliations

  1. School of Electrical and Computer Engineering, University of Campinas, Campinas, São Paulo, Brazil

    Leandro Maciel & Rosangela Ballini

  2. Institute of Economics, University of Campinas, Campinas, São Paulo, Brazil

    Fernando Gomide

Authors
  1. Leandro Maciel
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  2. Rosangela Ballini
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  3. Fernando Gomide
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Correspondence to Leandro Maciel .

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Editors and Affiliations

  1. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  2. Research and Innovation Center, Ford Motor Company, Dearborn, Michigan, USA

    Dimitar Filev

  3. Deakin University, Burwood, Victoria, Australia

    Gleb Beliakov

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Maciel, L., Ballini, R., Gomide, F. (2017). Evolving Possibilistic Fuzzy Modeling and Application in Value-at-Risk Estimation. In: Kacprzyk, J., Filev, D., Beliakov, G. (eds) Granular, Soft and Fuzzy Approaches for Intelligent Systems. Studies in Fuzziness and Soft Computing, vol 344. Springer, Cham. https://doi.org/10.1007/978-3-319-40314-4_7

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  • DOI: https://doi.org/10.1007/978-3-319-40314-4_7

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