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Testing Conditional Factor Models


Andrew Ang


Columbia Business School - Finance and Economics; National Bureau of Economic Research (NBER)

Dennis Kristensen


University College London; University of Aarhus - CREATES; Cemmap (Centre for Microdata Methods and Practice)

November 2011

NBER Working Paper No. w17561

Abstract:     
Using nonparametric techniques, we develop a methodology for estimating conditional alphas and betas and long-run alphas and betas, which are the averages of conditional alphas and betas, respectively, across time. The tests can be performed for a single asset or jointly across portfolios. The traditional Gibbons, Ross, and Shanken (1989) test arises as a special case of no time variation in the alphas and factor loadings and homoskedasticity. As applications of the methodology, we estimate conditional CAPM and multifactor models on book-to-market and momentum decile portfolios. We reject the null that long-run alphas are equal to zero even though there is substantial variation in the conditional factor loadings of these portfolios.

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Number of Pages in PDF File: 59

working papers series


Date posted: November 4, 2011  

Suggested Citation

Ang, Andrew and Kristensen, Dennis, Testing Conditional Factor Models (November 2011). NBER Working Paper No. w17561. Available at SSRN: http://ssrn.com/abstract=1954488

Contact Information

Andrew Ang (Contact Author)
Columbia Business School - Finance and Economics ( email )
3022 Broadway
New York, NY 10027
United States

National Bureau of Economic Research (NBER)
1050 Massachusetts Avenue
Cambridge, MA 02138
United States
Dennis Kristensen
University College London ( email )
Gower Street
London WC1E 6BT, WC1E 6BT
United Kingdom
+44 (0)20 7679 5846 (Phone)
HOME PAGE: http://www.ucl.ac.uk/economics/
University of Aarhus - CREATES
School of Economics and Management
Building 1322, Bartholins Alle 10
DK-8000 Aarhus C
Denmark
Cemmap (Centre for Microdata Methods and Practice) ( email )
7 Ridgmount Street
London WC1E 7AE, WC1E 7 AE
United Kingdom
Feedback to SSRN (Beta)


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