density of logreturns. Our proposed approach originates from the Bayesian approach to parameter estimation and time series … forecasting, however it is robust in the sense that it provides a more accurate estimation of the predictive density in the region …. This quasi-Bayesian approach yields more precise parameter estimation than a fully censored posterior for all parameters …

This paper is concerned with simulation based inference in generalized models of stochastic volatility defined by heavy …-tailed student-t distributions (with unknown degrees of freedom) and covariate effects in the observation and volatility equations … applied in detail to daily returns data on the S&P 500 index where several stochastic volatility models are formally compared …

The paper develops a novel realized matrix-exponential stochastic volatility model of multivariate returns and realized …’s seminal work in terms of the estimation of highly non-linear model specifications ("Causality tests and observationally …. The volatility and co-volatility spillovers are examined via the news impact curves and the impulse response functions …

Of the two most widely estimated univariate asymmetric conditional volatility models, the exponential GARCH (or EGARCH …) specification can capture asymmetry, which refers to the different effects on conditional volatility of positive and negative … shocks to volatility. However, the statistical properties of the (quasi-) maximum likelihood estimator (QMLE) of the EGARCH …

Of the two most widely estimated univariate asymmetric conditional volatility models, the exponential GARCH (or EGARCH …) specification can capture asymmetry, which refers to the different effects on conditional volatility of positive and negative … shocks to volatility. However, the statistical properties of the (quasi-) maximum likelihood estimator (QMLE) of the EGARCH …

volatility processes. This is called a GSSF-SV model. We show that conventional MCMC algorithms for this type of model are …