Autoregressive conditional heteroskedasticity (ARCH) models pioneered by Engle (1982) and their extended version have been proven to be very successful in modeling the volatility of financial time series; see Bollerslev et al. (1994). Bayesian inference on ARCH models has been implemented using the importance sampling technique proposed by Geweke (1989) and more recently using Markov chain Mont...

In this paper we use Markov chain Monte Carlo (MCMC) methods in order to estimate and compare GARCH models from a Bayesian perspective. We allow for possibly heavy tailed and asymmetric distributions in the error term. We use a general method proposed in the literature to introduce skewness into a continuous unimodal and symmetric distribution. For each model we compute an approximation to the ...

We consider the problem of detecting change points (structural changes) in long sequences of data, whether in a sequential fashion or not, and without assuming prior knowledge of the number of these change points. We reformulate this problem as the Bayesian filtering and smoothing of a non standard state space model. Towards this goal, we build a hybrid algorithm that relies on particle filteri...

In this paper we provide a unified methodology for conducting likelihood-based inference on the unknown parameters of a general class of discrete-time stochastic volatility (SV) models, characterized by both a leverage effect and jumps in returns. Given the nonlinear/non-Gaussian state-space form, approximating the likelihood for the parameters is conducted with output generated by the particle...

In this paper we examine the performance of two newly developed procedures that jointly select the number of states and variables in Markov-switching models by means of Monte Carlo simulations. They are Smith, Naik and Tsai (2006) and Psaradakis and Spagnolo (2006), respectively. The former develops Markov switching criterion (MSC) designed specifically for Markov-switching models, while the la...

In this letter, we study the blocking probabilities in an asynchronous optical packet/burst switching system with full wavelength conversion. Most of the existing work use Poisson traffic models that is well-suited for an infinite population of users. In this letter, the optical packet traffic arriving at the switching system is modeled through a superposition of a finite number of identical on...

The BEKK model is a popular multivariate GARCH processes. The paper develops a new general asymmetric BEKK structure, which is based on recent empirical findings by semi-parametric news impact curves. For estimating the new model, a Markov chain Monte Carlo technique is used. Empirical results for triviarte asset returns from firms in the US indicate that the deviance information criterion favo...

Following Hamilton (1989), estimation of Markov regime-switching regressions typically relies on the assumption that the latent state variable controlling regime change is exogenous. We relax this assumption and develop a parsimonious model of endogenous Markov regime-switching. Inference via maximum likelihood estimation is possible with relatively minor modifications to existing recursive fil...

In the present paper we apply the Gibbs Sampling approach to estimate the parameters of a Markov Switching Model which we use to model financial time series. In particular, we estimate the standard deviation of the time series in order to obtain an indicator similar to the VIX index. The Markov Switching technique has been chosen because of the presence of exogenous factors which can have a lar...