Kurtosis Maximization for Outlier Detection in GARCH Models

Nicola Loperfido ()
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Nicola Loperfido: Dipartimento di Economia, Società e Politica, Università degli Studi di Urbino “Carlo Bo”

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2018, pp 455-459 from Springer

Abstract: Abstract Outlier detection in financial time series is made difficult by serial dependence, volatility clustering and heavy tails. We address these problems by filtering financial data with projections achieving maximal kurtosis. This method, also known as kurtosis-based projection pursuit, proved to be useful for outlier detection but its use has been hampered by computational difficulties. This paper shows that in GARCH models projections maximizing kurtosis admit a simple analytical representation which greatly eases their computation. The method is illustrated with a simple GARCH model.

Keywords: GARCH model; Kurtosis; Projection pursuit (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-89824-7_81

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DOI: 10.1007/978-3-319-89824-7_81

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