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Projection pursuit based generalized betas accounting for higher order co-moment effects in financial market analysis

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  • Sven Serneels

Abstract

Betas are possibly the most frequently applied tool to analyze how securities relate to the market. While in very widespread use, betas only express dynamics derived from second moment statistics. Financial returns data often deviate from normal assumptions in the sense that they have significant third and fourth order moments and contain outliers. This paper targets to introduce a way to calculate generalized betas that also account for higher order moment effects, while maintaining the conceptual simplicity and interpretability of betas. Thereunto, the co-moment analysis projection index (CAPI) is introduced. When applied as a projection index in the projection pursuit (PP) framework, generalized betas are obtained as the directions optimizing the CAPI objective. A version of CAPI based on trimmed means is introduced as well, which is more stable in the presence of outliers. Simulation results underpin the statistical properties of all projections and a small, yet highly illustrative example is presented.

Suggested Citation

  • Sven Serneels, 2019. "Projection pursuit based generalized betas accounting for higher order co-moment effects in financial market analysis," Papers 1908.00141, arXiv.org.
    • Handle: RePEc:arx:papers:1908.00141
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    1. Wenliang Pan & Xueqin Wang & Weinan Xiao & Hongtu Zhu, 2019. "A Generic Sure Independence Screening Procedure," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 928-937, April.
    2. Serneels, Sven & Verdonck, Tim, 2009. "Principal component regression for data containing outliers and missing elements," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3855-3863, September.
    3. Lionel Martellini & Volker Ziemann, 2010. "Improved Estimates of Higher-Order Comoments and Implications for Portfolio Selection," The Review of Financial Studies, Society for Financial Studies, vol. 23(4), pages 1467-1502, April.
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    6. Todorov, Valentin & Filzmoser, Peter, 2009. "An Object-Oriented Framework for Robust Multivariate Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 32(i03).
    7. Serneels, Sven & Verdonck, Tim, 2008. "Principal component analysis for data containing outliers and missing elements," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1712-1727, January.
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