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 …

considered for themodelling of economic time series. The focus of this paper is on thesimultaneous estimation of parameters …

Estimation of the volatility of time series has taken off since the introduction of the GARCH and stochastic volatility … unobserved stochastic volatility, and the varying approaches that have been taken for such estimation. In order to simplify the … comprehension of these estimation methods, the main methods for estimating stochastic volatility are discussed, with focus on their …

Estimation of the volatility of time series has taken off since the introduction of the GARCH and stochastic volatility … unobserved stochastic volatility, and the varying approaches that have been taken for such estimation.In order to simplify the … comprehension of these estimation methods, the main methods for estimating stochastic volatility are discussed, with focus on their …

efficient, facilitates parameter estimation for models with high-dimensional state vectors, and overcomes a bias-variance trade …

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

stochastic volatility. Estimation of the model delivers measures of daily variation outperforming their non …When analysing the volatility related to high frequency financial data, mostly non-parametric approaches based on …

In this paper, we review the most common specifications of discrete-time stochastic volatility (SV) models and …