Bayesian model averaging is applied to robustly ascertain the determinants of various output volatility measures, including the downside semi-deviation of growth rates. Financial sophistication variables are found to have qualitatively different effects on volatility. The ratio of government...

We develop importance sampling methods for computing two popular Bayesian model comparison criteria, namely, the marginal likelihood and deviance information criterion (DIC) for TVP-VARs with stochastic volatility. The proposed estimators are based on the integrated likelihood, which are...

This paper evaluates alternative indicators of global economic activity and other market fundamentals in terms of their usefulness for forecasting real oil prices and global petroleum consumption. We find that world industrial production is one of the most useful indicators that has been...

A family of threshold nonlinear generalised autoregressive conditionally heteroscedastic models is considered, that allows smooth transitions between regimes, capturing size asymmetry via an exponential smooth transition function. A Bayesian approach is taken and an efficient adaptive sampling...

An effective approach for forecasting return volatility via threshold nonlinear heteroskedastic models of the daily asset price range is provided. The return is defined as the difference between the highest and lowest log intra-day asset price. A general model specification is proposed, allowing...

The asymmetric stochastic volatility (ASV) models extend the stochastic volatility model (SV) by modeling the correlation between the asset return and its volatility. We prove by simulation studies that fitting the ASV models may infer erroneous estimations of the correlation coefficients. Even...

We examine a group of extended realized stochastic volatility (RSV) models with ex-ante volatility information added to the framework. The most advantageous specification is the one with implied volatility (IV) as an explanatory variable in the latent volatility process, which produces an...

We construct a network volatility index (NetVIX) via market interconnectedness and volatilities to measure global market turbulence. The NetVIX multiplicatively decomposes into an average volatility and a network amplifier index. It also additively decomposes into marginal volatility indices for...

We introduce a new class of stochastic volatility models with autoregressive moving average (ARMA) innovations. The conditional mean process has a flexible form that can accommodate both a state space representation and a conventional dynamic regression. The ARMA component introduces serial...

We introduce a new class of stochastic volatility models with autoregressive moving average (ARMA) innovations. The conditional mean process has a flexible form that can accommodate both a state space representation and a conventional dynamic regression. The ARMA component introduces serial...