Bayesian analysis of switching ARCH models

Bayesian Analysis of Switching ARCH Models

We consider a time series model with autoregressive conditional heteroskedasticity that is subject to changes in regime. The regimes evolve according to a multistate latent Markov switching process with unknown transition probabilities, and it is the constant in the variance process of the innovations that is subject to regime shifts. The joint estimation of the latent process and all model par...

A Bayesian Analysis of Endogenous Switching Models for Count Data

Bayesian analysis of the unobserved ARCH model

The Unobserved ARCH model is a good description of the phenomenon of changing volatility that is commonly appeared in the financial time series. We study this model adopting Bayesian inference via Markov Chain Monte Carlo (MCMC). In order to provide an easy to implement MCMC algorithm we adopt some suitable non-linear transformations of the parameter space such that the resulting MCMC algorithm...

Bayesian Markov Regime-Switching Models for Cointegration

This paper introduces a Bayesian Markov regime-switching model that allows the cointegration relationship between two time series to be switched on and off over time. Unlike classical approaches for testing and modeling cointegration, the Bayesian Markov switching method allows for estimation of the regime-specific model parameters via Markov Chain Monte Carlo and generates more reliable estima...

Bayesian Estimation of Change-Point in Unobserved-ARCH Models

Change-point problem deals with sudden change in the distribution of a set of given data. Change in financial time series is a common event, because many factors for example some news, etc. may affect the series and cause change. In this work, we intend to detect the time of change-point, using Bayesian methods in Unobserved-ARCH models. We estimate the model and the time of the change-point.