This paper aims to provide a comprehensive overview of the estimation methodology for the time-varying parameter structural vector autoregression (TVP-VAR) with stochastic volatility, in both methodology and empirical applications. The TVP-VAR model, combined with stochastic volatility, enables...

While theoretical predictions establish a strong positive relationship between equity prices and inflation, finding substantiating empirical evidence has been a difficult endeavor. Generally, the data suggests a weak negative relationship between stock prices and inflation. Aided by two...

In this paper, Markov chain Monte Carlo sampling methods are exploited to provide a unified, practical likelihood-based framework for the analysis of stochastic volatility models. A highly effective method is developed that samples all the unobserved volatilities at once using an approximating...

This paper analyzes systematic risk of sovereign bonds in four East Asian countries: China, Malaysia, Philippines, and Thailand. A bivariate stochastic volatility model that allows for time-varying correlation is estimated with Markov Chain Monte Carlo simulation. The volatilities and...

Kim, Shephard and Chib (1998) provided a Bayesian analysis of stochastic volatility models based on a very fast and reliable Markov chain Monte Carlo (MCMC) algorithm. Their method ruled out the leverage effect, which limited its scope for applications. Despite this, their basic method has been...

In this paper, we review the most common specifications of discrete-time stochastic 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 develop and apply Bayesian inference for an extended Nelson-Siegel (1987) term structure model capturing interest rate risk. The so-called Stochastic Volatility Nelson-Siegel (SVNS) model allows for stochastic volatility in the underlying yield factors. We propose a Markov...

The class of Functional Signal plus Noise (FSN) models is introduced that provides a new, general method for modelling and forecasting time series of economic functions.  The underlying, continuous economic function (or 'signal') is a natural cubic spline whose dynamic evolution is driven by a...

Functional Signal plus Noise (FSN) time series models are introduced for the econometric analysis of the dynamics of a large cross-section of prices in which contemporaneous observations are functionally related. A semiparametric FSN model is developed in which a smooth, cubic spline signal...