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

Kalli, Maria, Walker, Stephen G., Damien, Paul (2013) Modelling the conditional distribution of daily stock index returns: an alternative Bayesian semiparametric model. Journal of Business and Economic Statistics, 31 (4). pp. 371-383. ISSN 0735-0015. (doi:10.1080/07350015.2013.794142)

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https://doi.org/10.1080/07350015.2013.794142

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 stylised 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 returns 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
DOI/Identification number: 10.1080/07350015.2013.794142
Uncontrolled keywords: Stick-breaking processes; Infinite uniform mixture; Markov chain Monte Carlo; Slice sampling
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science
Depositing User: Maria Kalli
Date Deposited: 18 Oct 2018 12:35 UTC
Last Modified: 29 May 2019 21:18 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/69649 (The current URI for this page, for reference purposes)
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