Bayesian testing for non-linearity in volatility modeling

Tatiana Miazhynskaia, Sylvia Frühwirth-Schnatter, Georg Dorffner

Publication: Scientific journalJournal articlepeer-review

Abstract

Neural networks provide a tool for describing non-linearity in volatility processes of financial data and help to answer the question "how much" non-linearity is present in the data. Non-linearity is studied under three different specifications of the conditional distribution: Gaussian, Student-t and mixture of Gaussians. To rank the volatility models, a Bayesian framework is adopted to perform a Bayesian model selection within the different classes of models. In the empirical analysis, the return series of the Dow Jones Industrial Average index, FTSE 100 and NIKKEI 225 indices over a period of 16 years are studied. The results show different behavior across the three markets. In general, if a statistical model accounts for non-normality and explains most of the fat tails in the conditional distribution, then there is less need for complex non-linear specifications
Original languageEnglish
Pages (from-to)2029 - 2042
JournalComputational Statistics and Data Analysis
Volume51
Issue number3
Publication statusPublished - 1 Oct 2006

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