条件自回归expectile模型及其在基金业绩评价中的应用

New Insight into Application in Mutual Funds' Performance Evaluation on Conditional Auto Regressive Expectile Models

本文研究了日收益率之下开放式基金的业绩评价和检验问题,提出了改进的条件自回归expectile(CARE)模型并应用到基金业绩评价的问题研究中。首先运用非对称最小二乘法(ALS)对动态的CARE模型进行半参数估计,得到样本基金收益率序列的VaR值和ES值。其次,使用计算结果对样本基金的日收益率进行风险调整,得到基于VaR和ES修正的Sharpe比率。最后,在实证研究中,本文使用传统的Sharpe比率、基于VaR和ES的Sharpe比率对我国56只开放式基金在2005-2011年间的业绩进行了实证分析,结论显著证明了CARE模型在极端风险度量上更精确,在基金评价和检验中的应用中是可行的。

Performance measurement is one of the most important issues in the research of mutual funds. The problems of performance evaluation and tests in the open-end mutual funds are studied in this paper, using daily returns. Conditional AutoRegressive Expectile （CARE） models are creatively introduced into the problem of evaluation of mutual funds＇ performance. Firstly, asymmetric least squares （ALS） method is applied to estimate the parameters in those CARE Models, and then the results are used to create autoregressive VaR model and conditional ES model to calculate the values of VaR and ES of our sample funds. Secondly, the values of VaR and ES are used to conduct risk-adjustment on the standard deviation, and thus the amended Sharpe ratios are obtained, which are based on VaR and ES. Finally, in empirical study, 56 domestic open-end funds in China are selected as samples, from 2005 to 2011. Empirical analysis are made on the evaluation and ranking of three measures of performance, including the traditional Sharpe ratio, VaR-based Sharpe Ratio and ES-based Sharpe ratio. The results prove CARE models can measure extreme risk much more accurately and thus can be very feasible to the evaluation and test in mutual funds.