Publication: Small Sample Inferences on the Sharpe Ratio
2
Issued Date
2016-04-02
Resource Type
ISSN
01966324
Other identifier(s)
2-s2.0-84960498281
Rights
Mahidol University
Rights Holder(s)
SCOPUS
Bibliographic Citation
American Journal of Mathematical and Management Sciences. Vol.35, No.2 (2016), 105-123
Suggested Citation
Suntaree Unhapipat, Jun Yu Chen, Nabendu Pal Small Sample Inferences on the Sharpe Ratio. American Journal of Mathematical and Management Sciences. Vol.35, No.2 (2016), 105-123. doi:10.1080/01966324.2015.1121847 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/43308
Research Projects
Organizational Units
Authors
Journal Issue
Thesis
Title
Small Sample Inferences on the Sharpe Ratio
Author(s)
Other Contributor(s)
Abstract
© 2016 Taylor & Francis Group, LLC. This work deals with statistical inferences on the "Sharpe Ratio" (SR) based on small samples. We have considered point estimation, interval estimation, as well as hypothesis testing, assuming that a random sample is available from a normal distribution. Further, we study the robustness of our inferential methods when the data is thought to have come from other nonnormal distributions but is mistakenly modeled by the normal distribution. Results from a comprehensive simulation study have been provided to justify our observations and recommendations. Among other things, we have proposed a new estimator of SR that performs much better than the commonly used maximum likelihood estimator. Finally, some mutual fund datasets have been used for demonstration purposes to estimate SR in order to assess their monthly return performances.