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On Stable Factor Structures in the Pricing of Risk

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  • Eric Ghysels

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Abstract

Much of the research describing the cross-sectional and time series behavior of asset returns can be characterized as a search for the relevant state variables and also a search for the relevant model specification. Ultimately the scope of such efforts is to find a satisfactory and stable asset pricing structure. In this paper we discuss various methods to accomplish this and appraise the success of two recently proposed classes of asset pricing models in tracking predictable patterns in risk and return trade-offs. The two classes are the conditional CAPM and the nonlinear APT. The parameters of both models are estimated via a set of moment conditions using the GMM estimator and the model fit is judged on the basis of the overidentifying restrictions. The fundamental problem is that overidentifying restrictions tests are not designed to diagnose whether a model, provides a stable relationship between the return series and risk factors. We use a set of recently developed tests for structural stability of parameter estimates for the GMM estimator to diagnose which factor structures appear stable through time in the context of the two aforementioned classes of models. In the course of trying to sort out whether there is systematic mispricing we shall also try to determine what type of model looks most promising for further development. In that regard we find the nonlinear APT more satisfactory than the conditional APT and CAPM. Dans cette étude nous réexaminons les modèles à facteurs qui ont été proposés récemment, c'est à dire le CAPM conditionnel et l'APT non-linéaire. Ces modèles ont été estimés par la méthode des moments généralisée. La diagnostique usuelle pour juger ces modèles est la statistique de suridentification. Le problème fondamental de cette statistique est qu'elle n'a pas de puissance par rapport à des alternatives caractérisés par des variations de paramètres. Évidement, ces variations entraînent des erreurs sur l'évaluation du risque. Nous proposons d'appliquer des tests de changement structurel pour les paramètres et analysons plusieurs modèles du type APT non-linéaire et CAPM conditionnel. Peu de modèles semblent être stable. Nous trouvons que la spécification du APT non-linéaire semble être quand même la plus satisfaisante.

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Bibliographic Info

Paper provided by CIRANO in its series CIRANO Working Papers with number 95s-16.

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Date of creation: 01 Mar 1995
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Handle: RePEc:cir:cirwor:95s-16

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Keywords: Structural change; Factor models; APT; Changement structurel ; Modèles à facteurs; APT;

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References

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  1. Huberman, Gur, 1982. "A simple approach to arbitrage pricing theory," Journal of Economic Theory, Elsevier, vol. 28(1), pages 183-191, October.
  2. Ghysels, E. & Guay, A. & Hall, A., 1995. "Predictive Tests for Structural Change with Unknown Breakpoint," Cahiers de recherche 9524, Universite de Montreal, Departement de sciences economiques.
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  4. Connor, Gregory, 1984. "A unified beta pricing theory," Journal of Economic Theory, Elsevier, vol. 34(1), pages 13-31, October.
  5. René Garcia & Eric Ghysels, 1996. "Structural Change and Asset Pricing in Emerging Markets," CIRANO Working Papers 96s-34, CIRANO.
  6. Ghysels, E & Hall, A., 1988. "A Test For Structural Stability Of Euler Conditions Parameters Estimated Via The Generalized Methods Of Moments Estimators," Cahiers de recherche 8837, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  7. Donald W.K. Andrews & Werner Ploberger, 1992. "Optimal Tests When a Nuisance Parameter Is Present Only Under the Alternative," Cowles Foundation Discussion Papers 1015, Cowles Foundation for Research in Economics, Yale University.
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Citations

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Cited by:
  1. BONOMO, Marco & GARCIA, René, 1997. "Tests of Conditional Asset Pricing Models in the Brazilian Stock Market," Cahiers de recherche 1997, Universite de Montreal, Departement de sciences economiques.
  2. Touhami, A. & Martens, A., 1996. "Macroemesures in Computable General Equilibrium Models: a Probabilistic Treatment with an Application to Morocco," Cahiers de recherche 9621, Universite de Montreal, Departement de sciences economiques.
  3. Elena Andreou & Eric Ghysels, 2004. "The Impact of Sampling Frequency and Volatility Estimators on Change-Point Tests," CIRANO Working Papers 2004s-25, CIRANO.
  4. Schrimpf, Andreas & Schröder, Michael & Stehle, Richard, 2006. "Evaluating conditional asset pricing models for the German stock market," ZEW Discussion Papers 06-43, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  5. Stefano D'Addona & Mattia Ciprian, 2007. "Time Varying Sensitivities On A Grid Architecture," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 10(02), pages 307-329.
  6. Ho-Chuan Huang & Wan-hsiu Cheng, 2005. "Tests of the CAPM under structural changes," International Economic Journal, Taylor & Francis Journals, vol. 19(4), pages 523-541.
  7. Wayne E. Ferson & Campbell R. Harvey, 1999. "Conditioning Variables and the Cross Section of Stock Returns," Journal of Finance, American Finance Association, vol. 54(4), pages 1325-1360, 08.
  8. Thierry Post & Haim Levy, 2002. "Does Risk Seeking drive Asset Prices?," Tinbergen Institute Discussion Papers 02-070/2, Tinbergen Institute.
  9. Bekaert, Geert & Wu, Guojun, 2000. "Asymmetric Volatility and Risk in Equity Markets," Review of Financial Studies, Society for Financial Studies, vol. 13(1), pages 1-42.
  10. Wayne E. Ferson & Andrew F. Siegel, 2006. "Testing Portfolio Efficiency with Conditioning Information," NBER Working Papers 12098, National Bureau of Economic Research, Inc.
  11. Martin Scheicher, 2000. "Time-varying risk in the German stock market," The European Journal of Finance, Taylor & Francis Journals, vol. 6(1), pages 70-91.
  12. Michael W. Brandt & David A. Chapman, 2006. "Linear Approximations and Tests of Conditional Pricing Models," NBER Working Papers 12513, National Bureau of Economic Research, Inc.

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