A semiparametric bayesian approach to the analysis of financial time series with applications to value at risk estimation
Document Type
Article
Publication Title
European Journal of Operational Research
Abstract
GARCH models are commonly used for describing, estimating and predicting the dynamics of financial returns. Here, we relax the usual parametric distributional assumptions of GARCH models and develop a Bayesian semiparametric approach based on modeling the innovations using the class of scale mixtures of Gaussian distributions with a Dirichlet process prior on the mixing distribution. The proposed specification allows for greater flexibility in capturing the usual patterns observed in financial returns. It is also shown how to undertake Bayesian prediction of the Value at Risk (VaR). The performance of the proposed semiparametric method is illustrated using simulated and real data from the Hang Seng Index (HSI) and Bombay Stock Exchange index (BSE30).
Publication Date
1-4-2014
Publisher
Elsevier
Volume
Vol.232
Issue
Iss.2
Recommended Citation
Ausn, M. Concepcion; Galeano, Pedro; and Ghosh, Pulak, "A semiparametric bayesian approach to the analysis of financial time series with applications to value at risk estimation" (2014). Faculty Publications. 1759.
https://research.iimb.ac.in/fac_pubs/1759
