A semiparametric Bayesian approach to the analysis of financial time series with applications to value at risk estimation

M. Concepción Ausín, Pedro Galeano and Pulak Ghosh

European Journal of Operational Research, 2014, vol. 232, issue 2, 350-358

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).

Keywords: Finance; Bayesian nonparametrics; Dirichlet process mixtures; GARCH models; Risk management; Value at risk (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (16)

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Working Paper: A semiparametric Bayesian approach to the analysis of financial time series with applications to value at risk estimation (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:232:y:2014:i:2:p:350-358

DOI: 10.1016/j.ejor.2013.07.008

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