A NON LINEAR TIME SERIES APPROACH TO MODELLING ASYMMETRY IN STOCK MARKET INDEXES

Giuseppe Storti and Alessandra Amendola ()

No 97, Computing in Economics and Finance 2000 from Society for Computational Economics

Abstract: In this paper we propose an approach to modelling non-linear conditionally heteroscedastic time series characterised by asymmetries in both the conditional mean and variance. This is achieved by combining a TAR model for the conditional mean with a Changing Parameters Volatility (CPV) model for the conditional variance. Empirical results are given for the daily returns of the S&P 500, NASDAQ composite and FTSE 100 stock market indexes.

Date: 2000-07-05
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Related works:
Journal Article: A non-linear time series approach to modelling asymmetry in stock market indexes (2002)
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