講座主題🧏🏽：A New Method for EstimatingSharpe Ratio Function via Local Maximum Likelihood

報告人：林紅梅（上海對外經貿大學科研處副處長）

時間🖱：2022年11月16日上午09🧑‍🦲：00-11🎐：30

線上會議：騰訊會議號：180-246-291

報告人簡介🤽🏽‍♂️：林紅梅，上海對外經貿大學副教授，碩士生導師，科研處副處長🕵🏻‍♂️。2016年在華東師範大學獲得統計學博士學位，主要從事非參半參回歸分析、縱向數據分析、函數型數據分析以及分布式統計方法等相關內容的研究👨🏼‍🏭🎒，在<Journal ofMultivariate Analysis>👨‍✈️、<Computational Statisticsand Data Analysis>等國內外知名學術期刊已發表SCI檢索論文二十余篇。主持國家自然科學基金面上項目、青年基金項目各1項🦺，上海市自然科學基金面上項目1項，教育部重點實驗室項目1項。

內容摘要🫅🏽：The Sharpe ratio function isa commonly used risk/return measure in financial econometrics. To estimate thisfunction, most existing methods take a two-step procedure that first estimatesthe mean and volatility functions separately and then applies the plug-inmethod. In this paper, we propose a direct local maximum likelihood method tosimultaneously estimate the Sharpe ratio function and the negative log-volatilityfunction or their derivatives. We establish the joint limiting distribution ofthe proposed estimators, and we further extend the proposed method to estimatethe multivariate Sharpe ratio function and establish its asymptotic normality.We evaluate the numerical performance of the proposed estimators throughsimulation studies, and compare them with existing methods. Finally, we applythe proposed method to analyze the three-month US Treasury bill interest ratedatasets and capture a well-known covariate-dependent effect on the Sharperatio.