Bayesian semiparametric multivariate GARCH modeling

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. www.econstor.eu Abstract: This paper proposes a Bayesian nonparametric modeling approach for the return distribution in multivariate GARCH models. In contrast to the parametric literature, the return distribution can display general forms of asymmetry and thick tails. An infinite mixture of multivariate normals is given a flexible Dirichlet process prior. The GARCH functional form enters into each of the components of this mixture. We discuss conjugate methods that allow for scale mixtures and nonconjugate methods, which provide mixing over both the location and scale of the normal components. MCMC methods are introduced for posterior simulation and computation of the predictive density. Bayes factors and density forecasts with comparisons to GARCH models with Student-t innovations demonstrate the gains from our flexible modeling approach. Terms of use: Documents inJEL classification: C11, C14, C53, C58

Volatility prediction based on scheduled macroeconomic announcements

“…It is interesting to investigate whether days with certain macroeconomic announcements provide better predictive ability for specific specifications when compared with non‐announcements days. We examined the one‐day‐ahead predictive likelihood of the model specifications as in Jensen & Maheu (), but we did not observe any systematic pattern with this respect. In Figure the cumulative difference of the one‐day‐ahead log predictive likelihood amongst the Flexible Threshold‐GARCH and Spline‐GARCH specifications is displayed, whereas the Consumer Confidence and Unemployment rate announcement days are indicated.…”

Bayesian semiparametric Markov switching stochastic volatility model

“…Delatola and Griffin 21,22 proposed to approximate the distribution of t as an infinite mixture of Normals by relying on DPM models. Dirichlet process mixture models, firstly introduced by Lo, 26 have been widely used for modeling time-varying volatilities with univariate and multivariate SV and GARCH-type models (see other works [19][20][21][22][23][49][50][51][52].…”

Stochastic Multivariate Mixture Covariance Model

“…Their mixture formulation allows a multiple time period density forecast of realized covariance and competitive point forecast of realized covariance. A similar approach to using a DP to model stochastic covariance can be found in the work of Jensen and Maheu (2013). Utilizing an infinite mixture of normal distribution with DP, Jensen and Maheu (2013) constructed a hierarchical Bayesian framework for the Student t MGARCH model.…”

“…A similar approach to using a DP to model stochastic covariance can be found in the work of Jensen and Maheu (2013). Utilizing an infinite mixture of normal distribution with DP, Jensen and Maheu (2013) constructed a hierarchical Bayesian framework for the Student t MGARCH model. Asai and So (2013) proposed an alternative using a stochastic covariance model based on the Wishart distribution.…”