This study explores a Gaussian quasi-likelihood approach for estimating parameters of diffusion processes with Markovian regime switching. Assuming the ergodicity under high-frequency sampling, we will show the asymptotic normality of the unknown parameters contained in the drift and diffusion coefficients and present a consistent explicit estimator for the generator of the Markov chain. Simulation experiments are conducted to illustrate the theoretical results obtained.

翻译：本研究探讨了采用高斯拟似然方法估计具有马尔可夫机制切换的扩散过程参数。在高频采样下的遍历性假设下，我们将证明漂移系数与扩散系数中未知参数的渐近正态性，并提出马尔可夫链生成元的一致显式估计量。通过模拟实验对所获得的理论结果进行了验证。