Non-linear versus non-gaussian volatility models in application to different financial markets

Tatiana Miazhynskaia, Georg Dorffner, Engelbert J. Dockner

Publikation: Working/Discussion PaperWU Working Paper

Abstract

We used neural-network based modelling to generalize the linear econometric return models and compare their out-of-sample predictive ability in terms of different performance measures under three density specifications. As error measures we used the likelihood values on the test sets as well as standard volatility measures. The empirical analysis was based on return series of stock indices from different financial markets. The results indicate that for all markets there was found no improvement in the forecast by non-linear models over linear ones, while nongaussian models significantly dominate the gaussian models with respect to most performance measures. The likelihood performance measure mostly favours the linear model with Student-t distribution, but the significance of its superiority differs between the markets. (author's abstract)
OriginalspracheEnglisch
ErscheinungsortVienna
HerausgeberSFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business
PublikationsstatusVeröffentlicht - 2003

Publikationsreihe

NameReport Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
Nr.84

WU Working Paper Reihe

  • Report Series SFB \Adaptive Information Systems and Modelling in Economics and Management Science\

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