Delatola, Eleni-Ioanna and Griffin, J.E. (2011) Bayesian Nonparametric Modelling of the Return Distribution with Stochastic Volatility. Bayesian Analysis, 6 (4). pp. 901-926. ISSN 1936-0975.
|PDF - Published Version|
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, one asset return series and one 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.
|Subjects:||Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics|
|Divisions:||Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics|
|Depositing User:||Jim Griffin|
|Date Deposited:||30 May 2012 12:30|
|Last Modified:||08 Jun 2012 11:07|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/29597 (The current URI for this page, for reference purposes)|
- Depositors only (login required):