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

publication date

  • January 2014

start page

  • 350

end page

  • 358

issue

  • 2

volume

  • 232

international standard serial number (ISSN)

  • 0377-2217

electronic international standard serial number (EISSN)

  • 1872-6860

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). (c) 2013 Elsevier B.V. All rights reserved.

keywords

  • finance; bayesian nonparametrics; dirichlet process mixtures; garch models; risk management; value at risk;