Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/258748 
Year of Publication: 
2022
Citation: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 15 [Issue:] 1 [Article No.:] 24 [Publisher:] MDPI [Place:] Basel [Year:] 2022 [Pages:] 1-22
Publisher: 
MDPI, Basel
Abstract: 
This study proposes a wavelet procedure for estimating partial correlation coefficients between stock market returns over different time scales. The estimated partial correlations are subsequently used in a cluster analysis to identify, for each time scale, groups of stocks that exhibit distinct market movement characteristics and are therefore useful for portfolio diversification. The proposed procedure is demonstrated using all the major S&P 500 sector indices as well as precious metals and energy sector futures returns during the last decade. The results suggest cluster formations that vary by time scale, which entails different stock selection strategies for investors differing in terms of their investment horizon orientation.
Subjects: 
stock market returns
wavelets
partial correlation coefficient
cluster analysis
portfolio diversification
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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