Heavy-tail statistical monitoring charts of the active managers' performance
Many performance measurement algorithms can only evaluate measure active managers' performance after a period of operating time. However, most investors are interested in monitoring the active managers' performances at any time, especially, when the performance is going down. So that the investors can adjust the targets and contents of their portfolios to reduce their risks. Yashchin,Thomas and David (1997) proposed to use a statistical quality control (SQC) procedure to monitor active managers' performances. In particular, they established the IR (Information Ratio) control charts under normality assumption to monitor the dynamic performances of active managers.
However, the distributions of IR statistic usually possess fat tail property. Since the underlying distribution of IR is an important hypothesis in building up the control chart, we consider the heavy tail distributions, such as mixture normal and generalized error distribution to fit the IR data. Based on the fitted distribution, the IR control charts are rebuilt. By simulations and empirical studies, the remedial control charts are found to detect the shifts of active managers' performances more sensitively.
Advisor:Mei-Hui Guo; Mong-Na Lo Huang; Yueh-hsia Chen
School:National Sun Yat-Sen University
School Location:China - Taiwan
Source Type:Master's Thesis
Keywords:generalized error distribution information ratio mixture normal cusum control chart fat tail performance measurement acceptance rejection sampling method
Date of Publication:08/03/2006