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A Non Linear Time Series Approach To Modelling Asymmetry In Stock Market Indexes

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  • Giuseppe Storti

    (Universit degli studi di Salerno)

  • Alessandra Amendola

    (Universit degli Studi di Salerno)

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.

Suggested Citation

  • Giuseppe Storti & Alessandra Amendola, 2000. "A Non Linear Time Series Approach To Modelling Asymmetry In Stock Market Indexes," Computing in Economics and Finance 2000 97, Society for Computational Economics.
    • Handle: RePEc:sce:scecf0:97
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    Cited by:

    1. Mohamed Boutahar & Gilles Dufrénot & Anne Péguin-Feissolle, 2008. "A Simple Fractionally Integrated Model with a Time-varying Long Memory Parameter d t," Computational Economics, Springer;Society for Computational Economics, vol. 31(3), pages 225-241, April.
    2. Roy Cerqueti & Massimiliano Giacalone & Raffaele Mattera, 2020. "Skewed non-Gaussian GARCH models for cryptocurrencies volatility modelling," Papers 2004.11674, arXiv.org.
    3. Giuseppe Storti & Cosimo Vitale, 2003. "BL-GARCH models and asymmetries in volatility," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 12(1), pages 19-39, February.
    4. Giuseppe Storti & Cosimo Vitale, 2003. "Likelihood inference in BL-GARCH models," Computational Statistics, Springer, vol. 18(3), pages 387-400, September.

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