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Likelihood ratio-type tests in weighted composite quantile regression of DTARCH models
Liu, Xiaoqian1; Song, Xinyuan2; Zhou, Yong3,4,5
2019-12-01
Source PublicationSCIENCE CHINA-MATHEMATICS
ISSN1674-7283
Volume62Issue:12Pages:2571-2590
AbstractThe double-threshold autoregressive conditional heteroscedastic (DTARCH) model is a useful tool to measure and forecast the mean and volatility of an asset return in a financial time series. The DTARCH model can handle situations wherein the conditional mean and conditional variance specifications are piecewise linear based on previous information. In practical applications, it is important to check whether the model has a double threshold for the conditional mean and conditional heteroscedastic variance. In this study, we develop a likelihood ratio test based on the estimated residual error for the hypothesis testing of DTARCH models. We first investigate DTARCH models with restrictions on parameters and propose the unrestricted and restricted weighted composite quantile regression (WCQR) estimation for the model parameters. These estimators can be used to construct the likelihood ratio-type test statistic. We establish the asymptotic results of the WCQR estimators and asymptotic distribution of the proposed test statistics. The finite sample performance of the proposed WCQR estimation and the test statistic is shown to be acceptable and promising using simulation studies. We use two real datasets derived from the Shanghai and Shenzhen Composite Indexes to illustrate the methodology.
KeywordDTARCH model quantile weighted composite quantile regression modified likelihood ratio test restricted WCQR estimators unrestricted WCQR estimators
DOI10.1007/s11425-016-9321-x
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[11471277] ; MOE (Ministry of Education in China) Project of Humanities and Social Sciences[15YJC910004] ; Research Grant Council of the Hong Kong Special Administration Region[GRF 14305014] ; State Key Program of National Natural Science Foundation of China[71331006] ; Major Research Plan of National Natural Science Foundation of China[91546202]
WOS Research AreaMathematics
WOS SubjectMathematics, Applied ; Mathematics
WOS IDWOS:000509102200010
PublisherSCIENCE PRESS
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/50555
Collection中国科学院数学与系统科学研究院
Corresponding AuthorLiu, Xiaoqian
Affiliation1.Shanghai Int Studies Univ, Sch Econ & Finance, Shanghai 200083, Peoples R China
2.Chinese Univ Hong Kong, Dept Stat, Hong Kong, Peoples R China
3.East China Normal Univ, Fac Econ & Management, Acad Stat & Interdisciplinary Sci, Shanghai 200062, Peoples R China
4.East China Normal Univ, Fac Econ & Management, Sch Stat, Shanghai 200062, Peoples R China
5.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Liu, Xiaoqian,Song, Xinyuan,Zhou, Yong. Likelihood ratio-type tests in weighted composite quantile regression of DTARCH models[J]. SCIENCE CHINA-MATHEMATICS,2019,62(12):2571-2590.
APA Liu, Xiaoqian,Song, Xinyuan,&Zhou, Yong.(2019).Likelihood ratio-type tests in weighted composite quantile regression of DTARCH models.SCIENCE CHINA-MATHEMATICS,62(12),2571-2590.
MLA Liu, Xiaoqian,et al."Likelihood ratio-type tests in weighted composite quantile regression of DTARCH models".SCIENCE CHINA-MATHEMATICS 62.12(2019):2571-2590.
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