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arXiv · 2112.13874

Unbiased Parameter Inference for a Class of Partially Observed Levy-Process Models

Hamza Ruzayqat·Ajay Jasra
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Abstract

We consider the problem of static Bayesian inference for partially observed Levy-process models. We develop a methodology which allows one to infer static parameters and some states of the process, without a bias from the time-discretization of the afore-mentioned Levy process. The unbiased method is exceptionally amenable to parallel implementation and can be computationally efficient relative to competing approaches. We implement the method on S & P 500 log-return daily data and compare it to some Markov chain Monte Carlo (MCMC) algorithm.

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Hamza Ruzayqat, Ajay Jasra. 2021-12-27. Unbiased Parameter Inference for a Class of Partially Observed Levy-Process Models. https://arxiv.org/abs/2112.13874

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