Moment Component Analysis: An Illustration With International Stock Markets

Eric Jondeau, Emmanuel Jurczenko and Michael Rockinger ()

Journal of Business & Economic Statistics, 2018, vol. 36, issue 4, 576-598

Abstract: 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.

Date: 2018
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Citations: View citations in EconPapers (12)

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Working Paper: Moment Component Analysis: An Illustration with International Stock Markets (2010)
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DOI: 10.1080/07350015.2016.1216851

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