A1226
Title: Asymptotically uniformly most powerful tests for diffusion processes with nonsynchronous observations
Authors: Teppei Ogihara - University of Tokyo (Japan) [presenting]
Abstract: The purpose is to introduce a quasi-likelihood ratio testing procedure for diffusion processes observed under nonsynchronous sampling schemes. High-frequency data, particularly in financial econometrics, are often recorded at irregular time points, challenging conventional synchronous methods for parameter estimation and hypothesis testing. To address these challenges, a quasi-likelihood framework is developed that accommodates irregular sampling while integrating adaptive estimation techniques for both drift and diffusion coefficients, thereby enhancing optimization stability and reducing computational burden. The asymptotic properties of the proposed test statistic are rigorously derived, showing that it converges to a chi-squared distribution under the null hypothesis and exhibits consistency under alternatives. Moreover, it is established that the resulting tests are asymptotically uniformly most powerful. Extensive numerical experiments corroborate the theoretical findings and demonstrate that the method outperforms existing nonparametric approaches.
