A GARCH-Type Model with Cross-Sectional Volatility Clusters

Pietro Coretto (), Michele La Rocca () and Giuseppe Storti
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Pietro Coretto: DISES, University of Salerno
Michele La Rocca: DISES, University of Salerno

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2021, pp 169-174 from Springer

Abstract: 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.

Keywords: GARCH models; Realized volatility; Model-based clustering; Robust clustering (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-78965-7_25

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DOI: 10.1007/978-3-030-78965-7_25

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