TY - JOUR
T1 - Estimating Sharpe ratio function in the presence of measurement error
AU - Lin, Hongmei
AU - Zhang, Shaodong
AU - Xu, Wenchao
AU - Tong, Tiejun
AU - Zhang, Riquan
N1 - Hongmei Lin’s research was partially supported by the National Natural Science Foundation of China (12171310,12371272) and the Shanghai “Project Dawn 2022” (22SG52). Wenchao Xu’s research was partially supported by National Natural Science Foundation of China (12101591). Tiejun Tong’s research was partially supported by the General Research Fund (HKBU12300123 and HKBU12303421), the Initiation Grant for Faculty Niche Research Areas (RC-FNRA-IG/23-24/SCI/03) of Hong Kong Baptist University, and the National Natural Science Foundation of China (12071305). Riquan Zhang’s research was partially supported by the National Natural Science Foundation of China (12371272) and the Basic Research Project of Shanghai Science and Technology Commission (22JC1400800).
Publisher Copyright:
© 2025 Taylor & Francis Group, LLC.
PY - 2025/1/9
Y1 - 2025/1/9
N2 - In financial econometrics, the Sharpe ratio function serves as a gold standard to measure the return-to-risk ratio for comparing different assets or trading strategies. In the recent literature, several methods have been developed to directly or indirectly estimate the Sharpe ratio function, yet none of them apply to the scenario where the covariates are measured with error. To handle this problem, we propose a new method by incorporating the local polynomial smoothing and SIMEX to simultaneously estimate the Sharpe ratio function and the negative log-volatility function in the presence of measurement error. The asymptotic bias and variance of the proposed estimators are also derived under some regularity conditions. We further conduct Monte Carlo simulations to evaluate the finite sample performance, and apply two real data examples to illustrate the usefulness of our new method.
AB - In financial econometrics, the Sharpe ratio function serves as a gold standard to measure the return-to-risk ratio for comparing different assets or trading strategies. In the recent literature, several methods have been developed to directly or indirectly estimate the Sharpe ratio function, yet none of them apply to the scenario where the covariates are measured with error. To handle this problem, we propose a new method by incorporating the local polynomial smoothing and SIMEX to simultaneously estimate the Sharpe ratio function and the negative log-volatility function in the presence of measurement error. The asymptotic bias and variance of the proposed estimators are also derived under some regularity conditions. We further conduct Monte Carlo simulations to evaluate the finite sample performance, and apply two real data examples to illustrate the usefulness of our new method.
KW - local polynomial smoothing
KW - measurement error
KW - Sharpe ratio function
KW - SIMEX
UR - http://www.scopus.com/inward/record.url?scp=85214707210&partnerID=8YFLogxK
UR - https://www.tandfonline.com/doi/full/10.1080/03610926.2024.2444501
U2 - 10.1080/03610926.2024.2444501
DO - 10.1080/03610926.2024.2444501
M3 - Journal article
AN - SCOPUS:85214707210
SN - 0361-0926
JO - Communications in Statistics - Theory and Methods
JF - Communications in Statistics - Theory and Methods
ER -