Modelling the conditional distribution of daily stock index returns: an alternative Bayesian semiparametric model

Journal article


Kalli, M., Damien, P. and Walker, S. 2013. Modelling the conditional distribution of daily stock index returns: an alternative Bayesian semiparametric model. Journal of Business and Economic Statistics. 31 (4). https://doi.org/10.1080/07350015.2013.794142
AuthorsKalli, M., Damien, P. and Walker, S.
Abstract

This paper introduces a new family of Bayesian semi-parametric models for the conditional distribution of daily stock index returns. The proposed models capture key stylized facts of such returns, namely heavy tails, asymmetry, volatility clustering, and the ‘leverage effect’. A Bayesian nonparametric prior is used to generate random density functions that are unimodal and asymmetric.Volatility is modelled parametrically. The new model is applied to the daily re- turns of the S&P 500, FTSE 100, and EUROSTOXX 50 indices and is compared to GARCH, Stochastic Volatility, and other Bayesian semi-parametric models.

KeywordsStick-breaking processes; infinite uniform mixture; Markov chain Monte Carlo; slice sampling
Year2013
JournalJournal of Business and Economic Statistics
Journal citation31 (4)
PublisherTaylor & Francis
ISSN0735-0015
Digital Object Identifier (DOI)https://doi.org/10.1080/07350015.2013.794142
Publication dates
Print2013
Online06 May 2013
Publication process dates
Deposited04 Nov 2013
Accepted author manuscript
Output statusPublished
ContributorsKalli, M., Damien, P. and Walker, S.
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