Moment component analysis : an illustration with international stock markets

Jondeau, Eric (Swiss Finance Institute ; University of Lausanne, Switzerland) ; Jurczenko, Emmanuel (Ecole hôtelière de Lausanne, HES-SO // University of Applied Sciences Western Switzerland) ; Rockinger, Michael (Swiss Finance Institute ; University of Lausanne, Switzerland)

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.


Keywords:
Article Type:
scientifique
Faculty:
Economie et Services
School:
EHL
Institute:
Aucun institut
Subject(s):
Economie/gestion
Date:
2018-10
Pagination:
23 p.
Published in:
Journal of business & economic statistics
Numeration (vol. no.):
2018, vol. 36, no. 4, pp. 576-598
DOI:
ISSN:
0735-0015
Appears in Collection:

Note: The status of this file is: restricted


 Record created 2019-10-30, last modified 2019-11-28

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