Gao, J. and Wang, L. (2023) Communication-Efficient Low-Dimensional Parameter Estimation and Inference for High-Dimensional L^p-Quantile Regression. Scandinavian Journal of Statistics, 51, 302-333.
https://doi.org/10.1111/sjos.12683

has been cited by the following article:

• TITLE: Evaluating Fund Performance Based on Lp Quantile Nonlinear Regression Model

  AUTHORS: Ying Sun, Fuming Lin

  KEYWORDS: Sharpe Ratio, Expectile, Lp Quantile, VaR

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.14 No.11, November 25, 2024

  ABSTRACT: There is a substantial body of empirical research that has found the fund return distributions to exhibit pronounced peakiness, heavy tails, and skewness, deviating from a normal distribution. Addressing the limitations of the traditional Sharpe ratio, which assumes a normal distribution of returns and uses standard deviation to measure investment risk, this paper primarily employs the Value at Risk (VaR) based on Lp quantile to adjust excess returns of funds. This method offers superior robustness, is capable of capturing asymmetry and heavy-tailed characteristics, and is more flexible, providing a better description of the tail risk in fund returns. Empirical studies have shown that using the Sharpe ratio corrected with the Lp quantile is feasible for evaluating and ranking the performance of open-end funds.