Author(s)

Ying Sun, Fuming Lin^*

College of Mathematical and Statistics, Sichuan University of Science and Engineering, Zigong, China.

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.

Sharpe Ratio, Expectile, Lp Quantile, VaR

Sun, Y. and Lin, F. (2024) Evaluating Fund Performance Based on Lp Quantile Nonlinear Regression Model. Open Journal of Applied Sciences, 14, 3202-3215. doi: 10.4236/ojapps.2024.1411211.