Feasible Inference for Stochastic Volatility in Brownian Semistationary Processes

Phillip Murray; Riccardo Passeggeri; Almut E. D. Veraart; Mikko S. Pakkanen

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arXiv · 2007.06357

Feasible Inference for Stochastic Volatility in Brownian Semistationary Processes

Phillip Murray·Riccardo Passeggeri·Almut E. D. Veraart·Mikko S. Pakkanen

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Abstract

This article studies the finite sample behaviour of a number of estimators for the integrated power volatility process of a Brownian semistationary process in the non semi-martingale setting. We establish three consistent feasible estimators for the integrated volatility, two derived from parametric methods and one non-parametrically. We then use a simulation study to compare the convergence properties of the estimators to one another, and to a benchmark of an infeasible estimator. We further establish bounds for the asymptotic variance of the infeasible estimator and assess whether a central limit theorem which holds for the infeasible estimator can be translated into a feasible limit theorem for the non-parametric estimator.

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BibTeX RIS

Phillip Murray, Riccardo Passeggeri, Almut E. D. Veraart, Mikko S. Pakkanen. 2020-07-13. Feasible Inference for Stochastic Volatility in Brownian Semistationary Processes. https://arxiv.org/abs/2007.06357

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