Estimation under copula-based Markov normal mixture models for serially correlated data

Wei Cheng Lin, Takeshi Emura, Li Hsien Sun

研究成果: 雜誌貢獻期刊論文同行評審

2 引文 斯高帕斯(Scopus)

摘要

We propose an estimation method under a copula-based Markov model for serially correlated data. Motivated by the fat-tailed distribution of financial assets, we select a normal mixture distribution for the marginal distribution. Based on the normal mixture distribution for the marginal distribution and the Clayton copula for serial dependence, we obtain the corresponding likelihood function. In order to obtain the maximum likelihood estimators, we apply the Newton-Raphson algorithm with appropriate transformations and initial values. We conduct simulation studies to evaluate the performance of the proposed method. In the empirical analysis, the stock price of Dow Jones Industrial Average is analyzed for illustration.

原文???core.languages.en_GB???
頁(從 - 到)4483-4515
頁數33
期刊Communications in Statistics - Simulation and Computation
50
發行號12
DOIs
出版狀態已出版 - 2021

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