Arbeitspapier
Full Bayesian inference for asymmetric Garch models with student-t innovations
In this work, we consider modeling the past volatilities through an asymmetric generalised autoregressive conditional heteroskedasticity (Garch) model with heavy tailed sampling distributions. In particular, we consider the Student-t model with unknown degrees of freedom and indicate how it may be used adequately from a Bayesian point of view in the context of smooth transition models for the variance. We adopt the full Bayesian approach for inference, prediction and hypothesis testing. We discuss problems related to the estimation of degrees of freedom in the Student-t model and propose a solution based on independent Jeffreys priors, which correct problems in the likelihood function. A simulated study is presented to investigate how estimation of model parameters in the Student-t Garch model are affected by small sample sizes, prior distributions and mispecification regarding the sampling distribution. An application to the Dow Jones stock market data illustrates the usefulness of the asymmetric Garch model with Student-t erros. In this context, the Student-t model is preferable for prediction in the case of high volatility regimes.
- Sprache
-
Englisch
- Erschienen in
-
Series: Discussion Paper ; No. 215
- Klassifikation
-
Wirtschaft
Bayesian Analysis: General
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- Thema
-
Student-t distribution
Garch model
Bayesian approach
Jeffreys prior
- Ereignis
-
Geistige Schöpfung
- (wer)
-
da Fonseca, Thaís C. O.
Cerqueira, Vinícius dos Santos
Migon, Hélio dos Santos
Torres, Cristian A. C.
- Ereignis
-
Veröffentlichung
- (wer)
-
Institute for Applied Economic Research (ipea)
- (wo)
-
Brasília
- (wann)
-
2016
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- da Fonseca, Thaís C. O.
- Cerqueira, Vinícius dos Santos
- Migon, Hélio dos Santos
- Torres, Cristian A. C.
- Institute for Applied Economic Research (ipea)
Entstanden
- 2016
Ähnliche Objekte (12)
Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations
Bayesian semiparametric multivariate GARCH modeling
Estimating GARCH-type models with symmetric stable innovations : indirect inference versus maximum likelihood
Asymmetric multivariate normal mixture GARCH
Asymmetric multivariate normal mixture GARCH
Why Bayesian inference?
Bootstrapping GARCH models under dependent innovations
Inference and testing in multivariate GARCH models
Bayesian inference of engineering models
Value investing via Bayesian inference
Inference in Bayesian proxy-SVARs
Value investing via Bayesian inference
Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations
Bayesian semiparametric multivariate GARCH modeling
Estimating GARCH-type models with symmetric stable innovations : indirect inference versus maximum likelihood
Asymmetric multivariate normal mixture GARCH
Asymmetric multivariate normal mixture GARCH
Why Bayesian inference?
Bootstrapping GARCH models under dependent innovations
Inference and testing in multivariate GARCH models
Bayesian inference of engineering models
Value investing via Bayesian inference
Inference in Bayesian proxy-SVARs
Value investing via Bayesian inference
Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations
Bayesian semiparametric multivariate GARCH modeling
Estimating GARCH-type models with symmetric stable innovations : indirect inference versus maximum likelihood
Asymmetric multivariate normal mixture GARCH
Asymmetric multivariate normal mixture GARCH
Why Bayesian inference?
Bootstrapping GARCH models under dependent innovations
Inference and testing in multivariate GARCH models
Bayesian inference of engineering models
Value investing via Bayesian inference
Inference in Bayesian proxy-SVARs