This work deals with multivariate stochastic volatility models, which account for a time-varying variance-covariance structure of the observable variables. We focus on a special class of models recently proposed in the literature and assume that the covariance matrix is a latent variable which...

We develop a Markov-Switching Autoregressive Conditional Intensity (MS-ACI) model with time-varying transitional parameters, and show that it can be reliably estimated via the Stochastic Approximation Expectation-Maximization algorithm. Applying our model to high-frequency transaction data, we...

This chapter reviews Bayesian methods for inference and forecasting with VAR models. Bayesian inference and, by extension, forecasting depends on numerical methods for simulating from the posterior distribution of the parameters and special attention is given to the implementation of the...

Goal of this paper is to analyze and forecast realized volatility through nonlinear and highly persistent dynamics. In particular, we propose a model that simultaneously captures long memory and nonlinearities in which level and persistence shift through a Markov switching dynamics.We consider...

The paper develops a Markov switching multifractal model with dynamic conditional correlations. The objective is to give more flexibility to the initial bivariate Markov switching multifractal model [MSM] (Calvet et al. (2006)) by introducing some time dependency in the comovement structure. The...

Starting from the discrete-time a ne term structure model by Dai, Le & Singleton (2006), this paper proposes a Radon-Nikodym derivative which implies that factors follow a mixture distribution under the physical measure. The model thus maintains attractive features of an affine relation between...