Parameter estimation for fractional power type diffusion: A hybrid Bayesian-deep learning approach

Héctor Araya and Francisco Plaza-Vega

Communications in Statistics - Theory and Methods, 2024, vol. 53, issue 22, 8234-8254

Abstract: In this article, we consider the problem of parameter estimation in a power-type diffusion driven by fractional Brownian motion with Hurst parameter in (1/2,1). To estimate the parameters of the process, we use an approximate bayesian computation method. Also, a particular case is addressed by means of variations and wavelet-type methods. Several theoretical properties of the process are studied and numerical examples are provided in order to show the small sample behavior of the proposed methods.

Date: 2024
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DOI: 10.1080/03610926.2023.2280522

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