Parametric estimation for a parabolic linear SPDE model based on sampled data

2019/09/30 by Yusuke Kaino, Kaino, Yusuke, Masayuki Uchida +1
Economics, Econometrics and Finance · Mathematics · #FOS: Mathematics #Financial Risk and Volatility Modeling #Statistical Methods and Inference #Statistics Theory (math.ST) #Stochastic processes and financial applications

paper · pdf · doi:10.48550/arxiv.1909.13557

openalex publication_date 2019/09/30 · openalex created_date 2019/10/03 · openalex updated_date 2026/07/28

Abstract

We consider parametric estimation for a parabolic linear second order stochastic partial differential equation (SPDE) from high frequency data which are observed in time and space. By using thinned data obtained from the high frequency data, adaptive estimators of the coefficient parameters including the volatility parameter of a parabolic linear SPDE model are proposed. Moreover, we give some examples and simulation results of the adaptive estimators of the SPDE model based on the high frequency data.

Citations

• On a family of test statistics for discretely observed diffusion processes

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