Statistical Methods and Applications

, Volume 11, Issue 2, pp 201–216 | Cite as

A non-linear time series approach to modelling asymmetry in stock market indexes

  • Alessandra Amendola
  • Giuseppe Storti
Statistical Applications


In this paper we analyse the performances of a novel approach to modelling non-linear conditionally heteroscedastic time series characterised by asymmetries in both the conditional mean and variance. This is based on the combination of a TAR model for the conditional mean with a Constrained Changing Parameters Volatility (CPV-C) 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.

Key words

Constrained Changing Parameters Volatility model TAR Leverage effect EM algorithm 


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Copyright information

© Springer-Verlag 2002

Authors and Affiliations

  • Alessandra Amendola
    • 1
  • Giuseppe Storti
    • 1
  1. 1.Dipartimento di Scienze Economiche e StatisticheUniversità di SalernoFisciano (SA)Italy

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