models with time varying volatility. In this paper we consider models of this class and examine their potential when it comes …

models with time varying volatility. In this paper we consider models of this class and examine their potential when it comes …

returns. Parameter estimation is carried out by using Markov Chain Monte Carlo methods. For model selection and volatility …This paper proposes a parsimonious threshold stochastic volatility (SV) model for financial asset returns. Instead of … imposing a threshold value on the dynamics of the latent volatility process of the SV model, we assume that the innovation of …

In this paper we examine the empirical performance of affine jump diffusion models with stochastic volatility in a time … method. The support for a stochastic volatility model including jumps in both prices and volatility is strong and the model … clearly outperforms the others in terms of a superior fit to data. Using this model and our estimation methodology we obtain …

The normal error distribution for the observations and log-volatilities in a stochastic volatility (SV) model is …

The asymmetric stochastic volatility (ASV) models extend the stochastic volatility model (SV) by modeling the … correlation between the asset return and its volatility. We prove by simulation studies that fitting the ASV models may infer … erroneous estimations of the correlation coefficients. Even if the true return-volatility correlation structure is different …

stochastic volatility model. Since the number of parameters in the joint correlation matrix of the return and volatility errors …

Based on the fact that realized measures of volatility are affected by measurement errors, we introduce a new family of … discrete-time stochastic volatility models having two measurement equations relating both observed returns and realized … realized measures in inflating the latent volatility persistence - the crucial parameter in pricing Standard and Poor's 500 …

evidence for stochastic intensity and stochastic volatility models based on Ornstein-Uhlenbeck processes. For our empirical …

metric for comparing stochastic volatility models is the DIC based on the conditional likelihood — obtained by conditioning … perform well. The main challenge for obtaining the latter DIC for stochastic volatility models is that the observed …-data likelihoods for a variety of stochastic volatility models using importance sampling. We demonstrate the methodology with an …