efficient estimation of markov-switching model with application in stock price classification

In this paper, we discuss the calibration of geometric brownian motion model equipped with markov-switching factor. since the motivation for this research comes from a recent stream of literature in stock economics, we propose an efficient estimation method to sample series of stock prices based on the expectation-maximization algorithm. we also implement an empirical application to evaluate the performance of the suggested model. for this purpose, based on the proposed markov-switching model, we classify market data under various economic regimes by estimating the smoothed probabilities of hidden markov chain states. numerical results through the classification of the data set show that the proposed markov-switching model fits the actual stock prices and re ects the main stylized facts of market dynamics.