Bayesian Nonparametric Modelling of the Return Distribution with Stochastic Volatility

By Eleni Ioanna Delatola, Jim E. Griffin. 2011

60 citations recorded by the source corpus.

This paper presents a method for Bayesian nonparametric analysis of the return distribution in a stochastic volatility model. The distribution of the logarithm of the squared return is flexibly modelled using an infinite mixture of Normal distributions. This allows efficient Markov chain Monte Carlo methods to be developed. Links between the return distribution and the distribution of the logarithm of the squared returns are discussed. The method is applied to simulated data, two asset return series and two stock index return series. We find that estimates of volatility using the model can differ dramatically from those using a Normal return distribution if there is evidence of a heavy-tailed return distribution.

Subfields: Artificial Intelligence, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/03eb0bc91e4d358ee14f3ffe1de14c645e81ad2e

Citations in the corpus

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