of the causes of systematic risk and shows that (i) network exposures act as an inflating factor for systematic exposure …

Adding multivariate stochastic volatility of a flexible form to large Vector Autoregressions (VARs) involving over a … literature either works with homoskedastic models or smaller models with restrictive forms for the stochastic volatility. In this … paper, we develop composite likelihood methods for large VARs with multivariate stochastic volatility. These involve …

The normal error distribution for the observations and log-volatilities in a stochastic volatility (SV) model is … distribution which enables an efficient Gibbs sampling algorithm. These representations also provide a means for outlier …

capturing interest rate risk. The so-called Stochastic Volatility Nelson-Siegel (SVNS) model allows for stochastic volatility in … evidence for time-varying volatility in the yield factors. This is mostly true for the level and slope volatility revealing … also the highest persistence. It turns out that the inclusion of stochastic volatility improves the model's goodness …

realized measure of co-volatility matrix simultaneously. The paper also considers an alternative multivariate asymmetric …

estimating trivariate hybrid time-varying parameter Bayesian VAR models with stochastic volatility for the three-month Treasury … corporate bond-yield spread decreases the risk free rate. Finally, we note that while allowing for heavy tails receives a fair …

these proposal densities are used in an independent Metropolis-Hastings algorithm or in importance sampling. Our method … evidence for stochastic intensity and stochastic volatility models based on Ornstein-Uhlenbeck processes. For our empirical …