Bayesian testing for non-linearity in volatility modeling

Tatiana Miazhynskaia, Sylvia Frühwirth-Schnatter, Georg Dorffner

Publikation: Wissenschaftliche FachzeitschriftOriginalbeitrag in FachzeitschriftBegutachtung


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
Seiten (von - bis)2029 - 2042
FachzeitschriftComputational Statistics and Data Analysis
PublikationsstatusVeröffentlicht - 1 Okt. 2006