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

2021/12/27 by Hamza Ruzayqat, Ajay Jasra, Ruzayqat, Hamza +1
Mathematics · #Statistical Methods and Bayesian Inference #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.2112.13874

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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