Testing for Granger non-causality over varying quantile levels could be used to measure and infer dynamic linkages, enabling the identification of quantiles for which causality is relevant, or not. However, dynamic quantiles in financial application settings are clearly affected by...

BAYSTAR provides Bayesian MCMC methods for iteratively sampling to provide parameter estimates and inference for the two-regime SETAR model. A convenient user interface for importing data from a file or specifying true values for simulated data is easy to apply for analysis. Parameter inferences...

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...

Value-at-Risk (VaR) forecasting via a computational Bayesian framework is considered. A range of parametric models are compared, including standard, threshold nonlinear and Markov switching GARCH specifications, plus standard and nonlinear stochastic volatility models, most considering four...

This paper develops a Bayesian framework for the realized exponential generalized autoregressive conditional heteroskedasticity (realized EGARCH) model and adopts a standardized Student-t and a standardized skewed Student-t distributions for the return equation. The Bayesian estimators show more...