On Efficient Bayesian Inference For Models With Stochastic Volatility

Download or read book On Efficient Bayesian Inference for Models with Stochastic Volatility written by Bill Sakaria and published by . This book was released on 2016 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: An efficient method for Bayesian inference in stochastic volatility models uses a linear state space representation to define a Gibbs sampler in which the volatilities are jointly updated. This method involves the choice of an offset parameter and we illustrate how its choice can have an important effect on the posterior inference. A Metropolis-Hastings algorithm is developed to robustify this approach to choice of the offset parameter. The method is illustrated on both simulated data with known parameters and the daily log returns of the Eurostoxx index.