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Title: Direct local linear estimation for Sharpe ratio function in heteroscedastic regression models Authors:  Hongmei Lin - Shanghai University of International Business and Economics (China) [presenting]
Abstract: The heteroscedastic regression model has been widely used in financial econometrics. It allows us to deal with nonlinearity and heteroscedasticity in financial time series. As the ratio of the mean and volatility functions, the Sharpe ratio is one of the most widely used risk or return measures in finance. We propose a new nonparametric method to estimate the Sharpe ratio function directly using local linear regression. We establish the asymptotic normality for the proposed estimator. Monte Carlo simulation studies show the proposed estimator has excellent finite sample performance and outperform existing indirect method. We illustrate our method with a real data example.