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

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). ISSN 0735-0015.

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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.

Item Type: Article
Uncontrolled Keywords: Stick-breaking processes; infinite uniform mixture; Markov chain Monte Carlo; slice sampling
Subjects: H Social Sciences > HA Statistics
H Social Sciences > HB Economic Theory
Q Science > QA Mathematics > QA0273 Probabilities. Mathematical statistics
Divisions: pre Nov-2014 > Faculty of Business and Management > Department of Accounting, Finance & Information Management
Depositing User: Maria Kalli
Date Deposited: 04 Nov 2013 17:03
Last Modified: 28 Jan 2017 19:35
URI: https://create.canterbury.ac.uk/id/eprint/12373

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Last edited: 29/06/2016 12:23:00