Evolving fuzzy modelling in risk analysis

  • Authors:

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

  • Affiliations:

  • DTE – IE – UNICAMP, 13083-857 Campinas, São Paulo, Brazil;DTE – IE – UNICAMP, 13083-857 Campinas, São Paulo, Brazil;DAC – FEEC – UNICAMP, 13083-852 Campinas, São Paulo, Brazil

  • Venue:
  • International Journal of Intelligent Systems in Accounting and Finance Management - Risk Analysis in Complex Systems: Intelligent Systems in Finance
  • Year:
  • 2009

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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.