Stock market volatility prediction using possibilistic fuzzy modelling. (2016)

• Record Type: Journal Article
• Authors: Maciel, Leandro; Gomide, Fernando; Ballini, Rosangela
• Is Part Of: International journal of innovative computing and applications. Volume 7:Number 4(2016)
• Page Start: 181
• Page End: 190
• Publication Date: 2016
• Subjects: recursive possibilistic modelling -- fuzzy logic -- fuzzy modelling -- forecasting -- stock market volatility -- volatility prediction -- stock markets -- asset returns -- data clustering -- volatility modelling -- financial markets; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

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.