讲座人简介：林红梅，上海对外经贸大学副教授，硕士生导师，科研处副处长。2016年在华东师范大学获得统计学博士学位，主要从事非参半参回归分析、纵向数据分析、函数型数据分析以及分布式统计方法等相关内容的研究，在Journal of Multivariate Analysis、Computational Statistics and Data Analysis等国内外知名学术期刊已发表SCI论文二十余篇。主持国家自然科学基金面上项目、青年基金项目各1项，上海市自然科学基金面上项目1项，教育部重点实验室项目1项。

讲座简介：The 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 local maximum likelihood method to simultaneously estimate the Sharpe ratio function and the negative log-volatility function or their derivatives. We establish the joint limiting distribution of the proposed estimators, and we further extend the proposed method to estimate the multivariate Sharpe ratio function and establish its asymptotic normality. We evaluate the numerical performance of the proposed estimators through simulation studies, and compare them with existing methods. Finally, we apply the proposed method to analyze the three-month US Treasury bill interest rate datasets and capture a well-known covariate-dependent effect on the Sharpe ratio.