Bayesian Analysis of Inverse Gaussian Stochastic Conditional Duration Model

Abstract

This paper discusses Bayesian analysis of stochastic conditional duration model when the innovations follow inverse Gaussian distribution. Estimation is carried out by the methods of Markov Chain Monte Carlo. Applications of the model and methods are illustrated through simulation and data analysis.

A Note on α-stable and α-inverse Gaussian Laws

“…This motivated [13] to develop AR(1) models with IG marginals. [14] discussed Bayesian analysis of IG stochastic conditional duration models. [10] derived the FPT distribution of IG process.…”