-varying parameter vector autoregression with stochastic volatility developed by Cogley and Sargent (2005) and Primiceri (2005), CSP …

macroeconomic variables and multivariate stochastic volatility models with 100 stock returns. Finally, we perform impulse response …

Cholesky multivariate stochastic volatility model. It establishes that systematically different dynamic restrictions are … divergent when volatility clusters idiosyncratically. It is illustrated that this property is important for empirical … multivariate stochastic volatility model is proposed as a robust alternative. …

In this paper we exploit properties of the likelihood function of the stochastic volatility model to show that it can …

wide variety of stocks, bonds and options. Evidence suggests that both the expected return and the volatility vary over … considerable effort has been devoted to the modelling of time-varying volatility. Recent attention has moved to examining the … daily stock market volatility in a sample of significant emerging stock markets using an Asymetric Volatility Model (ASV …

gap inflation persistence to a UC model that already has stochastic volatility (SV) afflicting trend and gap inflation …

-time version of the stochastic volatility (SV) model. The Bayesian approach represents a feasible way to estimate SV models. Under …

In this paper, we extend the parametric, asymmetric, stochastic volatility model (ASV), where returns are correlated … with volatility, by flexibly modeling the bivariate distribution of the return and volatility innovations nonparametrically …. Its novelty is in modeling the joint, conditional, return-volatility distribution with an infinite mixture of bivariate …

drifting gap inflation persistence to a UC model in which stochastic volatility (SV) affects trend and gap inflation. Another …