framework is a bivariate volatility model, where volatility spillovers of either positive or negative sign are allowed for. Our … countries. Regarding the volatility spillovers, such spillovers from bond returns to those of stocks are stronger than the other … results show that by considering time-varying return and volatility spillovers when calculating the risk-minimising portfolio …

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 …

volatilityinduced stationarity. Our model employs a leveldependent conditional volatility that maintains stationarity despite the …

, our credit and illiquidity proxies can explain almost three quarters of the yield spread-bond volatility relation with …-bond volatility relation is important even after controlling for equity volatility. The relation between yield spreads and … investment-grade sub-sample, consistent with credit risk being relatively more important for understanding the yield spread-volatility …

Affine jump diffusion models in general and affine stochastic volatility models in particular are important modeling … stochastic volatility model, nested in the former as a special case, this paper collects explicit expressions for various …

We propose a mixed frequency stochastic volatility (MFSV) model for the dynamics of intraday asset return volatility …. In order to account for long-memory we separate stochastic daily and intraday volatility patterns by introducing a long …-run component that changes at daily frequency and a short-run component that captures the remaining intraday volatility dynamics. An …