@article{a563a322d20b49e899dbb00eb0086423,
title = "Direct local linear estimation for Sharpe ratio function",
abstract = "Nonparametric regression has been widely used to deal with nonlinearity and heteroscedasticity in financial time series. As the ratio of the mean and standard deviation functions, the Sharpe ratio function is one of the most commonly used risk/return measures in financial econometrics. Most existing methods take an indirect procedure, which first estimates the mean and variance functions and then applies these two functions to estimate the Sharpe ratio function. In practice, however, such an indirect procedure can often be less efficient. In this article, we propose a direct method to estimate the Sharpe ratio function by local linear regression. We further establish the asymptotic normality of the proposed estimator, apply Monte Carlo simulations to evaluate its finite sample performance, and compare it with the indirect method. The usefulness of our new method is also illustrated through a real data analysis.",
keywords = "Heteroscedasticity, local likelihood estimation, local linear regression, nonparametric regression, Sharpe ratio function",
author = "Hongmei Lin and Tiejun Tong and Yuedong Wang and Wenchao Xu and Riquan Zhang",
note = "Funding Information: We thank the editor, the associate editor and the two reviewers for their constructive comments, which led to a significant improvement of this article. Hongmei Lin's research was partially supported by the National Natural Science Foundation of China (11701360, 11971300), the Shanghai Natural Science Foundation (20ZR1421800, 19ZR1420900) and the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science (East China Normal University). Tiejun Tong's research was partially supported by the National Natural Science Foundation of China (11671338) and the General Research Fund (HKBU12303918). Yuedong Wang's research was partially supported by the National Science Foundation of the United States (DMS‐1507620). Wenchao Xu's research was partially supported by China Postdoctoral Science Foundation (2021M693340). Riquan Zhang's research was partially supported by the National Natural Science Foundation of China (11971171). Publisher Copyright: {\textcopyright} 2021 Statistical Society of Canada",
year = "2022",
month = mar,
doi = "10.1002/cjs.11658",
language = "English",
volume = "50",
pages = "36--58",
journal = "Canadian Journal of Statistics",
issn = "0319-5724",
publisher = "Wiley-Blackwell",
number = "1",
}