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Efficient estimation of Markov-switching model with application in stock price classification | ||
| Journal of Mathematics and Modeling in Finance | ||
| دوره 1، شماره 2، آذر 2021، صفحه 111-130 اصل مقاله (10.19 MB) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22054/jmmf.2021.13843 | ||
| نویسندگان | ||
Farshid Mehrdoust  1؛ Idin Noorani2؛ Mahdi Khavari2
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| 1Department of Applied Mathematics, Faculty of Mathematical Sciences, University Guilan, Rasht, Iran | ||
| 2Department of Applied Mathematics, Faculty of Mathematical Sciences, University of Guilan, Rasht, Iran | ||
| چکیده | ||
| In this paper, we discuss the calibration of the 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 a series of stock prices based on the expectation-maximization algorithm. We also implement an empirical application to evaluate the performance of the suggested model. Numerical results through the classification of the data set show that the proposed Markov-switching model fits the actual stock prices and reflects the main stylized facts of market dynamics. Since the motivation for this research comes from a recent stream of literature in stock economics, we propose an efficient estimation method to sample a series of stock prices based on the expectation-maximization algorithm. Numerical results through the classification of the data set show that the proposed Markov-switching model fits the actual stock prices and reflects the main stylized facts of market dynamics. Since the motivation for this research comes from a recent stream of literature in stock economics, we propose an efficient estimation method to sample a series of stock prices based on the expectation-maximization algorithm. | ||
| کلیدواژهها | ||
| Regime-switching model؛ Estimation of Parameter؛ Expectation-maximization algorithm؛ Classification | ||
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آمار تعداد مشاهده مقاله: 51 تعداد دریافت فایل اصل مقاله: 37 |
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