Quotation Miazhynskaia, Tatiana, Frühwirth-Schnatter, Sylvia, Dorffner, Georg. 2006. Bayesian testing for non-linearity in volatility modeling. Computational Statistics & Data Analysis 51 (3): 2029-2042.


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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

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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Computational Statistics and Data Analysis
Citation Index SCI
WU-Journal-Rating new FIN-A, VW-D, WH-B
Language English
Title Bayesian testing for non-linearity in volatility modeling
Volume 51
Number 3
Year 2006
Page from 2029
Page to 2042
URL http://www.sciencedirect.com/science?_ob=ArticleListURL&_method=list&_ArticleListID=1763689010&_sort=r&_st=13&view=c&_acct=C000022138&_version=1&_urlVersion=0&_userid=464393&md5=1695f8a79b71e39f64a0229af5c7f53a&searchtype=a

Associations

People
Frühwirth-Schnatter, Sylvia (Details)
External
Dorffner, Georg
Miazhynskaia, Tatiana
Organization
Institute for Statistics and Mathematics IN (Details)
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