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A GARCH-Type Model with Cross-Sectional Volatility Clusters

In: Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author

Listed:
  • Pietro Coretto

    (DISES, University of Salerno)

  • Michele La Rocca

    (DISES, University of Salerno)

  • Giuseppe Storti

    (DISES, University of Salerno)

Abstract

In this work we exploit the inhomogeneity of the cross-sectional distribution of realized stock volatilities, and we propose to use it improve the predictive performance of GARCH-type models. The inhomogeneity is shown to be well captured by a finite Gaussian mixture model plus a uniform component that represents the “noise” generated by abnormal variations in returns. In fact, it is common that in a cross-section of realized volatilities there is a small proportion of stocks showing extreme behavior. The mixture model is used to estimate the probability that, at a given time point, the stock belongs to a specific volatility group. The latter is profitably used for specifying parsimonious state-dependent models for volatility forecasting. We propose novel GARCH-type specifications whose parameters act “clusterwise” conditional on past information on the volatility clusters. Finally the empirical performance of the proposed models is assessed by means of an application to a panel of U.S. stocks traded on the NYSE.

Suggested Citation

  • Pietro Coretto & Michele La Rocca & Giuseppe Storti, 2021. "A GARCH-Type Model with Cross-Sectional Volatility Clusters," Springer Books, in: Marco Corazza & Manfred Gilli & Cira Perna & Claudio Pizzi & Marilena Sibillo (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 169-174, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-78965-7_25
    DOI: 10.1007/978-3-030-78965-7_25
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