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

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Abstract

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.

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Authors and Affiliations

  1. Dipartimento di Scienze Economiche e Statistiche, Università di Salerno, Via Ponte Don Melillo, 84084, Fisciano (SA), Italy

    Alessandra Amendola & Giuseppe Storti

Authors
  1. Alessandra Amendola
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  2. Giuseppe Storti
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Amendola, A., Storti, G. A non-linear time series approach to modelling asymmetry in stock market indexes. Statistical Methods & Applications 11, 201–216 (2002). https://doi.org/10.1007/BF02511487

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