Title: Stock market volatility prediction using possibilistic fuzzy modelling

Authors: Leandro Maciel; Fernando Gomide; Rosangela Ballini

Addresses: Institute of Economics, University of Campinas, Campinas, Brazil ' School of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil ' Institute of Economics, University of Campinas, Campinas, Brazil

Abstract: This paper suggests a recursive possibilistic modelling approach (rPFM) for assets return volatility forecasting with jumps. The model employs memberships and typicalities to cluster data, and affine functions in the fuzzy rule consequents. The possibilistic idea provides model robustness to noisy and outlier data, essential for financial markets volatility modelling, which is affected by news, expectations and investors psychology. Computational experiments include actual intraday data from the main equity market indexes in global markets, namely, S&P 500 and Nasdaq (USA), FTSE (UK), DAX (Germany), IBEX (Spain) and Ibovespa (Brazil). Performance of rPFM is compared with well established recursive fuzzy and neural fuzzy modelling. The results show that rPFM produces parsimonious models with better accuracy than the alternative approaches.

Keywords: recursive possibilistic modelling; fuzzy logic; fuzzy modelling; forecasting; stock market volatility; volatility prediction; stock markets; asset returns; data clustering; volatility modelling; financial markets.

DOI: 10.1504/IJICA.2016.080852

International Journal of Innovative Computing and Applications, 2016 Vol.7 No.4, pp.181 - 190

Received: 22 Jan 2016
Accepted: 16 Mar 2016
Published online: 09 Dec 2016 *

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