WAVELET BETAS AT THE SECTOR LEVEL: A LENS TO CAPTURE RISK DYNAMICS THAT STANDARD BETAS IGNORE

Joan Nix ()
Additional contact information
Joan Nix: Department of Economics and BBA, Queens College of the City University of New York

Working Papers from Department of Economics, Queens College of the City University of New York

Abstract: The application of the CAPM to ten S&P sectors is investigated using wavelet analysis. Wavelet methods provide a unified framework for investigating the relationship among variables at different frequencies and the evolution of these relationships over time. Betas at different time and frequency scales using wavelet analysis are estimated for ten sectors and compared to the betas estimated from a simple regression of the market model. We find coherence and frequency differences across sectors that are not reflected in the estimation of standard beta. Given that there is no a priori reason to assume that the synchronization of sector returns with market returns has only a time varying dimension, wavelet analysis by allowing for multiple time-varying frequencies offers estimates of sector level betas that capture features of the underlying risk dynamics that would be missed using the standard estimate of beta.

Keywords: Wavelet Analysis; CAPM; Equity Betas; Standard & Poor Sectors (search for similar items in EconPapers)
Pages: 50 pages
Date: 2014-09, Revised 2014-09
New Economics Papers: this item is included in nep-rmg
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.qc-econ-bba.org/RePEc/pdf/0006.pdf Revised version, 2014 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:quc:wpaper:0006

Access Statistics for this paper

More papers in Working Papers from Department of Economics, Queens College of the City University of New York Contact information at EDIRC.
Bibliographic data for series maintained by Cara Marshall ().