This paper analyzes the time-varying parameter vector autoregressive (TVP-VAR) model for the Japanese economy and monetary policy. The time-varying parameters are estimated via the Markov chain Monte Carlo method and the posterior estimates of parameters reveal the time-varying structure of the...

In this paper, we review the most common specifications of discrete-time stochas- tic volatility (SV) models and illustrate the major principles of corresponding Markov Chain Monte Carlo (MCMC) based statistical inference. We provide a hands-on ap- proach which is easily implemented in empirical...

In this paper we apply Bayesian methods to estimate a stochastic volatility model using both the prices of the asset and the prices of options written on the asset. Implicit posterior densities for the parameters of the volatility model, for the latent volatilities and for the market price of...

This paper highlights the ability of the discrete stochastic volatility models to predict some important properties of the data, i.e. leptokurtic distribution of the returns, slowly decaying autocorrelation function of squared returns and the Taylor effect. Although, there are many methods...

We address the problem of parameter estimation for diffusion driven stochastic volatility models through Markov chain Monte Carlo (MCMC). To avoid degeneracy issues we introduce an innovative reparametrisation defined through transformations that operate on the time scale of the diffusion. A...

We estimate a Bayesian structural vector autoregression that allows for time-varying parameters and stochastic volatility in the errors to account for the effects of various aggregate shocks on the real price of oil. We employ US quarterly data from 1948:Q1 to 2011:Q2. We find that aggregate...

In state–space models, parameter learning is practically difficult and is still an open issue. This paper proposes an efficient simulation-based parameter learning method. First, the approach breaks up the interdependence of the hidden states and the static parameters by marginalizing out the...

This paper investigates the dynamics of the real exchange rate and relative output among the US and five of its top six trading partners since the collapse of Bretton Woods. It employs long-run restrictions to identify the usual suspect macroeconomic shocks and their relative importance for...

This paper investigates the existence of spillovers from stock prices onto consumption and the interest rate for South Africa using a time-varying vector autoregressive (TVP-VAR) model with stochastic volatility. In this regard, we estimate a three-variable TVP-VAR model comprising of real...

Kim, Shephard, and Chib (1998) provided a Bayesian analysis of stochastic volatility models based on a fast and reliable Markov chain Monte Carlo (MCMC) algorithm. Their method rules out the leverage effect, which is known to be important in applications. Despite this, their basic method has...