Beskos, A., Dureau, J. and Kalogeropoulos, K. ORCID: 0000-0002-0330-9105
(2015)
Bayesian inference for partially observed stochastic differential equations driven by fractional Brownian motion.
Biometrika, 102 (4).
pp. 809-827.
ISSN 0006-3444
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Abstract
We consider continuous-time diffusion models driven by fractional Brownian motion. Observations are assumed to possess a nontrivial likelihood given the latent path. Due to the non-Markovian and high-dimensional nature of the latent path, estimating posterior expectations is computationally challenging. We present a reparameterization framework based on the Davies and Harte method for sampling stationary Gaussian processes and use it to construct a Markov chain Monte Carlo algorithm that allows computationally efficient Bayesian inference. The algorithm is based on a version of hybrid Monte Carlo simulation that delivers increased efficiency when used on the high-dimensional latent variables arising in this context. We specify the methodology on a stochastic volatility model, allowing for memory in the volatility increments through a fractional specification. The method is demonstrated on simulated data and on the S&P 500/VIX time series. In the latter case, the posterior distribution favours values of the Hurst parameter smaller than 1/2 , pointing towards medium-range dependence.
Item Type: | Article |
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Official URL: | http://biomet.oxfordjournals.org/ |
Additional Information: | © 2015 Biometrika Trust |
Divisions: | Statistics |
Subjects: | Q Science > QA Mathematics |
Date Deposited: | 04 Jan 2016 15:46 |
Last Modified: | 16 May 2024 02:07 |
Projects: | EP/K001264/1 |
Funders: | Engineering and Physical Sciences Research Council |
URI: | http://eprints.lse.ac.uk/id/eprint/64806 |
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