Moment Component Analysis: An Illustration With International Stock Markets
提出矩成分分析（MCA）方法，将主成分分析扩展到高阶协矩（如协偏度和协峰度），并用44个国际股票市场周度数据（1994-2014）证明少数因子即可概括协偏度和协峰度结构，且对市场收益、系统性风险测量和投资组合分配有额外信息价值。
We describe a statistical technique, which we call Moment Component Analysis (MCA), that extends Principal Component Analysis (PCA) to higher co-moments such as co-skewness and co-kurtosis. This method allows us to identify the factors that drive co-skewness and co-kurtosis structures across a large set of series. We illustrate MCA using 44 international stock markets sampled at weekly frequency from 1994 to 2014. We find that both the co-skewness and the co-kurtosis structures can be summarized with a small number of factors. Using a rolling window approach, we show that these co-moments convey useful information about market returns, for systemic risk measurement and portfolio allocation, complementary to the information extracted from a standard PCA or from an Independent Component Analysis.