This paper focuses on the extraction of volatility of financial returns. The volatility process is modeled as a … the volatility is not observable, the logarithm of the daily high-low range is employed as its proxy. The estimation of … parameters and volatility extraction are performed using a modified version of the Kalman filter which takes into account the …

The Heston model stands out from the class of stochastic volatility (SV) models mainly for two reasons. Firstly, the … process for the volatility is nonnegative and mean-reverting, which is what we observe in the markets. Secondly, there exists …

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 …

Most multivariate variance or volatility models suffer from a common problem, the “curse of dimensionality”. For this … stochastic volatility models. The empirical analysis on stock returns on the US market shows that 1% and 5 % Value …

Most of the empirical applications of the stochatic volatility (SV) model are based on the assumption that the … conditional distribution of returns given the latent volatility process is normal. In this paper the SV model based on a … choice of the conditional distribution has systematic effects on the parameter estimates of the volatility process. …

We consider a stochastic volatility model of the mean-reverting type to describe the evolution of a firm’s values … default probability. Our simulation results indicate that the stochastic volatility model tends to predict higher default … probabilities than the corresponding Merton model if a firm’s credit quality is not too low. Otherwise the stochastic volatility …