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Testing Conditional Factor Models

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  • Andrew Ang
  • Dennis Kristensen

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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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 17561.

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Date of creation: Nov 2011
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Handle: RePEc:nbr:nberwo:17561

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Citations

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Cited by:
  1. Ang, Andrew & Kristensen, Dennis, 2012. "Testing conditional factor models," Journal of Financial Economics, Elsevier, vol. 106(1), pages 132-156.
  2. Fernando M. Duarte, 2013. "Inflation risk and the cross section of stock returns," Staff Reports 621, Federal Reserve Bank of New York.
  3. Andrew Ang & Marie Brière & Ombretta Signori, 2012. "Inflation and Individual Equities," NBER Working Papers 17798, National Bureau of Economic Research, Inc.
  4. Londono Yarce, J.M., 2011. "Essays on asset pricing," Open Access publications from Tilburg University urn:nbn:nl:ui:12-5146522, Tilburg University.
  5. Zárraga Alonso, Ainhoa & Nieto Domenech, Belén & Orbe Mandaluniz, Susan, 2011. "Time-Varying Beta Estimators in the Mexican Emerging Market," BILTOKI 2011-06, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
  6. Kang, Hankil & Kang, Jangkoo & Lee, Changjun, 2013. "Do the production-based factors capture the time-varying patterns in stock returns?," Emerging Markets Review, Elsevier, vol. 15(C), pages 122-135.
  7. Cosemans, M. & Frehen, R.G.P. & Schotman, P.C. & Bauer, R.M.M.J., 2009. "Efficient Estimation of Firm-Specific Betas and its Benefits for Asset Pricing Tests and Portfolio Choice," MPRA Paper 23557, University Library of Munich, Germany.
  8. Roussanov, Nikolai, 2014. "Composition of wealth, conditioning information, and the cross-section of stock returns," Journal of Financial Economics, Elsevier, vol. 111(2), pages 352-380.
  9. Baele, Lieven & Londono, Juan M., 2013. "Understanding industry betas," Journal of Empirical Finance, Elsevier, vol. 22(C), pages 30-51.
  10. Boguth, Oliver & Carlson, Murray & Fisher, Adlai & Simutin, Mikhail, 2011. "Conditional risk and performance evaluation: Volatility timing, overconditioning, and new estimates of momentum alphas," Journal of Financial Economics, Elsevier, vol. 102(2), pages 363-389.
  11. Markus Reiß & Viktor Todorov & George Tauchen, 2014. "Nonparametric Test for a Constant Beta over a Fixed Time Interval," SFB 649 Discussion Papers SFB649DP2014-022, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  12. Dennis Kristensen, 2009. "Semiparametric Modelling and Estimation: A Selective Overview," CREATES Research Papers 2009-44, School of Economics and Management, University of Aarhus.
  13. Arnold, Marc & Wagner, Alexander F. & Westermann, Ramona, 2013. "Growth options, macroeconomic conditions, and the cross section of credit risk," Journal of Financial Economics, Elsevier, vol. 107(2), pages 350-385.
  14. Corradi, Valentina & Distaso, Walter & Fernandes, Marcelo, 2013. "Conditional alphas and realized betas," Textos para discussão 341, Escola de Economia de São Paulo, Getulio Vargas Foundation (Brazil).

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