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A new method for estimating Sharpe ratio function via local maximum likelihood
Xu, Wenchao1; Lin, Hongmei2; Tong, Tiejun3; Zhang, Riquan2,4,5
2022-08-30
Source PublicationJOURNAL OF APPLIED STATISTICS
ISSN0266-4763
Pages19
AbstractThe Sharpe ratio function is a commonly used risk/return measure in financial econometrics. To estimate this function, most existing methods take a two-step procedure that first estimates the mean and volatility functions separately and then applies the plug-in method. In this paper, we propose a direct method via local maximum likelihood to simultaneously estimate the Sharpe ratio function and the negative log-volatility function as well as their derivatives. We establish the joint limiting distribution of the proposed estimators, and moreover extend the proposed method to estimate the multivariate Sharpe ratio function. We also evaluate the numerical performance of the proposed estimators through simulation studies, and compare them with existing methods. Finally, we apply the proposed method to the three-month US Treasury bill data and that captures a well-known covariate-dependent effect on the Sharpe ratio.
KeywordDirect method heteroscedastic non-parametric regression joint limiting distribution local polynomial smoothing Sharpe ratio function
DOI10.1080/02664763.2022.2114431
Indexed BySCI
Language英语
Funding ProjectChina Postdoctoral Science Foundation[2021M693340] ; National Natural Science Foundation of China[1207010822] ; Shanghai Natural Science Foundation[20ZR1421800] ; General Research Fund[HKBU12303421] ; General Research Fund[HKBU12303918] ; Initiation Grant for Faculty Niche Research Areas of Hong Kong Baptist University[RC-FNRA-IG/20-21/SCI/03] ; National Science Foundation of China[1207010822] ; National Science Foundation of China[11971171] ; National Science Foundation of China[11831008] ; Basic Research Project of Shanghai Science and Technology Commission[22JC1400800]
WOS Research AreaMathematics
WOS SubjectStatistics & Probability
WOS IDWOS:000865661200001
PublisherTAYLOR & FRANCIS LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/60851
Collection中国科学院数学与系统科学研究院
Corresponding AuthorLin, Hongmei
Affiliation1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
2.Shanghai Univ Int Business & Econ, Sch Stat & Informat, Shanghai, Peoples R China
3.Hong Kong Baptist Univ, Dept Math, Hong Kong, Peoples R China
4.East China Normal Univ, MOE, Key Lab Adv Theory & Applicat Stat & Data Sci, Shanghai, Peoples R China
5.East China Normal Univ, Sch Stat, Shanghai, Peoples R China
Recommended Citation
GB/T 7714
Xu, Wenchao,Lin, Hongmei,Tong, Tiejun,et al. A new method for estimating Sharpe ratio function via local maximum likelihood[J]. JOURNAL OF APPLIED STATISTICS,2022:19.
APA Xu, Wenchao,Lin, Hongmei,Tong, Tiejun,&Zhang, Riquan.(2022).A new method for estimating Sharpe ratio function via local maximum likelihood.JOURNAL OF APPLIED STATISTICS,19.
MLA Xu, Wenchao,et al."A new method for estimating Sharpe ratio function via local maximum likelihood".JOURNAL OF APPLIED STATISTICS (2022):19.
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