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ABC of SV: Limited information likelihood inference in stochastic volatility jump-diffusion models

Creel, M; Kristensen, D; (2015) ABC of SV: Limited information likelihood inference in stochastic volatility jump-diffusion models. Journal of Empirical Finance , 31 pp. 85-108. 10.1016/j.jempfin.2015.01.002. Green open access

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Abstract

We develop novel methods for estimation and filtering of continuous-time models with stochastic volatility and jumps using so-called Approximate Bayesian Computation which build likelihoods based on limited information. The proposed estimators and filters are computationally attractive relative to standard likelihood-based versions since they rely on low-dimensional auxiliary statistics and so avoid computation of high-dimensional integrals. Despite their computational simplicity, we find that estimators and filters perform well in practice and lead to precise estimates of model parameters and latent variables. We show how the methods can incorporate intra-daily information to improve on the estimation and filtering. In particular, the availability of realized volatility measures help us in learning about parameters and latent states. The method is employed in the estimation of a flexible stochastic volatility model for the dynamics of the S&P 500 equity index. We find evidence of the presence of a dynamic jump rate and in favor of a structural break in parameters at the time of the recent financial crisis. We find evidence that possible measurement error in log price is small and has little effect on parameter estimates. Smoothing shows that, recently, volatility and the jump rate have returned to the low levels of 2004–2006.

Type: Article
Title: ABC of SV: Limited information likelihood inference in stochastic volatility jump-diffusion models
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.jempfin.2015.01.002
Language: English
Additional information: This article is published under a Creative Commons Attribution Non-commercial Non-derivative 4.0 International license (CC BY-NC-ND 4.0). This license allows you to share, copy, distribute and transmit the work for personal and non-commercial use providing author and publisher attribution is clearly stated. Further details about CC BY licenses are available at http://creativecommons.org/ licenses/by/4.0
Keywords: Approximate Bayesian Computation, Continuous-time processes, Filtering, Indirect inference, Jumps, Realized volatility
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Economics
URI: https://discovery.ucl.ac.uk/id/eprint/1461149
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