Toward a Bayesian Inference of Time-Deformation Models: Some Simulation Studies

Abstract

This paper focuses on simulation-based inference for the time-deformation models directed by a duration process. In order to describe the heavy tail property of the time series of financial asset returns, the innovation of the observation equation is assumed to have a Student-t distribution. Suitable Markov Chain Monte Carlo (MCMC) algorithms are proposed for the estimation of the parameters of these models. In the algorithms, the parameters of the models can be sampled either directly from known distributions or through an efficient slice sampler. The states are simulated one at a time by using a Metropolis-Hastings (MH) method, where the proposal distributions are sampled through a slice sampler. Simulation studies conducted in this paper suggest that our proposed models and accompanying MCMC algorithms work well in terms of parameter estimation and volatility forecast.

Keywords: Time- deformation model, Bayesian Inference, Slice sampler, Leverage effect.

JEL Classification: C1, C11, C15, G1