Showing 1 - 10 of 2,680
Several lessons learned from a Bayesian analysis of basic economic time series models by means of the Gibbs sampling algorithm are presented. Models include the Cochrane-Orcutt model for serial correlation, the Koyck distributed lag model, the Unit Root model, the Instrumental Variables model...
Persistent link: https://www.econbiz.de/10011349180
the tedious task of tuning a MCMC sampling algorithm. The usage of the package is shown in an empirical application to …
Persistent link: https://www.econbiz.de/10011380176
A new version of the local scale model of Shephard (1994) is presented. Its features are identically distributed evolution equation disturbances, the incorporation of in-the-mean effects, and the incorporation of variance regressors. A Bayesian posterior simulator and a new simulation smoother...
Persistent link: https://www.econbiz.de/10013120871
This book presents in detail methodologies for the Bayesian estimation of single-regime and regime-switching GARCH models. These models are widespread and essential tools in financial econometrics and have, until recently, mainly been estimated using the classical Maximum Likelihood technique....
Persistent link: https://www.econbiz.de/10013156202
We present an estimation and forecasting method, based on a differential evolution MCMC method, for inference in GARCH …
Persistent link: https://www.econbiz.de/10012956780
Given discrete time observations over a fixed time interval, we study a nonparametric Bayesian approach to estimation of the volatility coefficient of a stochastic differential equation. We postulate a histogram-type prior on the volatility with piecewise constant realisations on bins forming a...
Persistent link: https://www.econbiz.de/10012852986
asset price jumps. The models are estimated by a combination of a MCMC algorithm and a SIR Particle Filter. The performance …
Persistent link: https://www.econbiz.de/10012914862
This paper explains how the Gibbs sampler can be used to perform Bayesian inference on GARCH models. Although the Gibbs sampler is usually based on the analyti-cal knowledge of the full conditional posterior densities, such knowledge is not available in regression models with GARCH errors. We...
Persistent link: https://www.econbiz.de/10014197191
This paper presents the R package bayesGARCH which provides functions for the Bayesian estimation of the parsimonious but effective GARCH(1,1) model with Student-t innovations. The estimation procedure is fully automatic and thus avoids the time-consuming and difficult task of tuning a sampling...
Persistent link: https://www.econbiz.de/10014203852
It is well known that volatility asymmetry exists in financial markets. This paper reviews and investigates recently developed techniques for Bayesian estimation and model selection applied to a large group of modern asymmetric heteroskedastic models. These include the GJR-GARCH, threshold...
Persistent link: https://www.econbiz.de/10014207589