Efficient parameter estimation for parabolic SPDEs based on a log-linear model for realized volatilities

TL;DR

This paper introduces a novel estimation method for parameters in parabolic SPDEs using realized volatilities within a log-linear model, achieving smaller variances and efficient inference in high-frequency data settings.

Contribution

It develops a new estimator based on realized volatilities that has optimal convergence rates and lower asymptotic variances compared to existing methods.

Findings

Establishes central limit theorems for the proposed estimators.

Demonstrates efficiency gains through numerical simulations.

Provides feasible asymptotic confidence intervals.

Abstract

We construct estimators for the parameters of a parabolic SPDE with one spatial dimension based on discrete observations of a solution in time and space on a bounded domain. We establish central limit theorems for a high-frequency asymptotic regime. The asymptotic variances are shown to be substantially smaller compared to existing estimation methods. Moreover, asymptotic confidence intervals are directly feasible. Our approach builds upon realized volatilities and their asymptotic illustration as response of a log-linear model with spatial explanatory variable. This yields efficient estimators based on realized volatilities with optimal rates of convergence and minimal variances. We demonstrate efficiency gains compared to previous estimation methods numerically and in Monte Carlo simulations.