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Evolving fuzzy modelling in risk analysis

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  • R. Ballini
  • A. R. R. Mendonça
  • F. Gomide

Abstract

Traditionally, forecast methodologies emphasize precise point‐forecasts of stationary data. Risk analysis demands forecasts that, in practice, must be developed using imprecise and nonstationary data. Currently, value‐at‐risk (VaR) is widely employed in risk analysis. VaR requires a form of interval forecasts. Generalized autoregressive conditional heteroskedasticity (GARCH) models are stochastic recursive systems commonly adopted in financial prediction. This paper addresses a new approach to handle imprecise and nonstationary data using evolving fuzzy modelling translated into a recursive, adaptive forecasting procedure. VaR analysis is conducted to compare the performance and robustness of evolving fuzzy forecasting against GARCH using São Paulo Stock Exchange data. Copyright © 2009 John Wiley & Sons, Ltd.

Suggested Citation

  • R. Ballini & A. R. R. Mendonça & F. Gomide, 2009. "Evolving fuzzy modelling in risk analysis," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 16(1‐2), pages 71-86, January.
    • Handle: RePEc:wly:isacfm:v:16:y:2009:i:1-2:p:71-86
    DOI: 10.1002/isaf.294
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