IDEAS home Printed from https://ideas.repec.org/p/cir/cirwor/2003s-34.html
   My bibliography  Save this paper

Finite-Sample Diagnostics for Multivariate Regressions with Applications to Linear Asset Pricing Models

Author

Listed:
  • Jean-Marie Dufour
  • Lynda Khalaf
  • Marie-Claude Beaulieu

Abstract

In this paper, we propose several finite-sample specification tests for multivariate linear regressions (MLR) with applications to asset pricing models. We focus on departures from the assumption of i.i.d. errors assumption, at univariate and multivariate levels, with Gaussian and non-Gaussian (including Student t) errors. The univariate tests studied extend existing exact procedures by allowing for unspecified parameters in the error distributions (e.g., the degrees of freedom in the case of the Student t distribution). The multivariate tests are based on properly standardized multivariate residuals to ensure invariance to MLR coefficients and error covariances. We consider tests for serial correlation, tests for multivariate GARCH and sign-type tests against general dependencies and asymmetries. The procedures proposed provide exact versions of those applied in Shanken (1990) which consist in combining univariate specification tests. Specifically, we combine tests across equations using the MC test procedure to avoid Bonferroni-type bounds. Since non-Gaussian based tests are not pivotal, we apply the "maximized MC"" (MMC) test method [Dufour (2002)], where the MC p-value for the tested hypothesis (which depends on nuisance parameters) is maximized (with respect to these nuisance parameters) to control the tests significance level. The tests proposed are applied to an asset pricing model with observable risk-free rates, using monthly returns on New York Stock Exchange (NYSE) portfolios over five-year subperiods from 1926-1995. Our empirical results reveal the following. Whereas univariate exact tests indicate significant serial correlation, asymmetries and GARCH in some equations, such effects are much less prevalent once error crossequation covariances are accounted for. In addition, significant departures from the i.i.d. hypothesis are less evident once we allow for non-Gaussian errors." Dans cet article, nous proposons plusieurs tests de spécification valides pour des échantillons finis dans le cadre de régression linéaires multivariées (RLM), avec des applications à des modèles d'évaluation d'actifs. Nous nous concentrons sur les déviations par rapport à l'hypothèse d'erreurs i.i.d. univariée ou multivariée, pour des distributions d'erreurs gaussiennes et non gaussiennes. Les tests univariés étudiés prolongent les procédures exactes existantes en permettant des paramètres non spécifiés dans la distribution des erreurs (e.g., le nombre de degrés de liberté dans le cas de la distribution de Student). Les tests multivariés sont basés sur des résidus standardisés multivariés qui assurent l'invariance par rapport aux coefficients RLM et à ceux de la matrice de covariance des erreurs. Nous considérons des tests contre la dépendance sérielle, contre la présence d'effets GARCH multivariés et des tests de signes contre l'asymétrie. Les procédures proposées sont des versions exactes des tests de Shanken (1990) qui consistent à combiner des tests de spécification univariés. Spécifiquement, nous combinons des tests entre équations en utilisant une approche de test de Monte Carlo (MC), ce qui permet d'éviter des bornes de type Bonferroni. Étant donné que les tests dans un contexte non gaussien ne sont pas pivotaux, nous appliquons une approche de test de Monte Carlo maximisé [Dufour (2002)] où la valeur p simulée pour l'hypothèse testée (qui dépend de paramètres de nuisance) est maximisée (par rapport aux dits paramètres de nuisance) dans le but de contrôler le niveau des tests. Nous appliquons les tests proposés à un modèle d'évaluation d'actifs qui comprend un taux d'intérêt sans risque observable et utilise les rendements de portefeuilles mensuels de titres inscrits à la bourse de New York, sur des sous-périodes de cinq ans allant de janvier 1926 à décembre 1995. Nos résultats révèlent que les tests univariés exacts présentent des problèmes de dépendance sérielle, d'asymétrie et d'effets GARCH statistiquement significatifs dans certaines équations. Cependant ces problèmes s'avèrent moins importants, lorsque l'on tient compte de la dépendance entre équations. De plus, les écarts importants par rapport à l'hypothèse i.i.d. sont moins évidents une fois que l'on considère des erreurs non gaussiennes.

Suggested Citation

  • Jean-Marie Dufour & Lynda Khalaf & Marie-Claude Beaulieu, 2003. "Finite-Sample Diagnostics for Multivariate Regressions with Applications to Linear Asset Pricing Models," CIRANO Working Papers 2003s-34, CIRANO.
  • Handle: RePEc:cir:cirwor:2003s-34
    as

    Download full text from publisher

    File URL: http://www.cirano.qc.ca/files/publications/2003s-34.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Dufour, Jean-Marie & Kiviet, Jan F., 1996. "Exact tests for structural change in first-order dynamic models," Journal of Econometrics, Elsevier, vol. 70(1), pages 39-68, January.
    2. Dufour, Jean-Marie & Khalaf, Lynda, 2002. "Exact tests for contemporaneous correlation of disturbances in seemingly unrelated regressions," Journal of Econometrics, Elsevier, vol. 106(1), pages 143-170, January.
    3. Kroner, Kenneth F & Ng, Victor K, 1998. "Modeling Asymmetric Comovements of Asset Returns," Review of Financial Studies, Society for Financial Studies, vol. 11(4), pages 817-844.
    4. Shanken, Jay, 1990. "Intertemporal asset pricing : An Empirical Investigation," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 99-120.
    5. Dufour, Jean-Marie & Khalaf, Lynda & Bernard, Jean-Thomas & Genest, Ian, 2004. "Simulation-based finite-sample tests for heteroskedasticity and ARCH effects," Journal of Econometrics, Elsevier, vol. 122(2), pages 317-347, October.
    6. Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 41-66.
    7. Jean-Marie Dufour & Lynda Khalaf & Marie-Claude Beaulieu, 2003. "Exact Skewness-Kurtosis Tests for Multivariate Normality and Goodness-of-Fit in Multivariate Regressions with Application to Asset Pricing Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 891-906, December.
    8. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(01), pages 122-150, February.
    9. Lee, John H. H., 1991. "A Lagrange multiplier test for GARCH models," Economics Letters, Elsevier, vol. 37(3), pages 265-271, November.
    10. Marie-Claude BEAULIEU & Jean-Marie DUFOUR & Lynda KHALAF, 2002. "Testing Mean-Variance Efficiency In Capm With Possibly Non-Gaussian Errors : An Exact Simulation-Based Approach," Cahiers de recherche 17-2002, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    11. Raymond Kan & Guofu Zhou, 2012. "Tests of Mean-Variance Spanning," Annals of Economics and Finance, Society for AEF, vol. 13(1), pages 139-187, May.
    12. Kenneth Stewart, 1997. "Exact testing in multivariate regression," Econometric Reviews, Taylor & Francis Journals, vol. 16(3), pages 321-352.
    13. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    14. Harvey, Andrew C & Phillips, Garry D A, 1982. "Testing for Contemporaneous Correlation of Disturbances in Systems of Regression Equations," Bulletin of Economic Research, Wiley Blackwell, vol. 34(2), pages 79-91, November.
    15. Jean-Marie Dufour & Jan F. Kiviet, 1998. "Exact Inference Methods for First-Order Autoregressive Distributed Lag Models," Econometrica, Econometric Society, vol. 66(1), pages 79-104, January.
    16. Dufour, Jean-Marie & Khalaf, Lynda, 2002. "Simulation based finite and large sample tests in multivariate regressions," Journal of Econometrics, Elsevier, vol. 111(2), pages 303-322, December.
    17. Richardson, Matthew & Smith, Tom, 1993. "A Test for Multivariate Normality in Stock Returns," The Journal of Business, University of Chicago Press, vol. 66(2), pages 295-321, April.
    18. Gibbons, Michael R & Ross, Stephen A & Shanken, Jay, 1989. "A Test of the Efficiency of a Given Portfolio," Econometrica, Econometric Society, vol. 57(5), pages 1121-1152, September.
    19. Gibbons, Michael R., 1982. "Multivariate tests of financial models : A new approach," Journal of Financial Economics, Elsevier, vol. 10(1), pages 3-27, March.
    20. Dufour, Jean-Marie, 2006. "Monte Carlo tests with nuisance parameters: A general approach to finite-sample inference and nonstandard asymptotics," Journal of Econometrics, Elsevier, vol. 133(2), pages 443-477, August.
    21. Shanken, Jay, 1986. " Testing Portfolio Efficiency When the Zero-Beta Rate Is Unknown: A Note," Journal of Finance, American Finance Association, vol. 41(1), pages 269-276, March.
    22. Godfrey, Leslie G., 1996. "Some results on the Glejser and Koenker tests for heteroskedasticity," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 275-299.
    23. Affleck-Graves, John & McDonald, Bill, 1989. " Nonnormalities and Tests of Asset Pricing Theories," Journal of Finance, American Finance Association, vol. 44(4), pages 889-908, September.
    24. Zhou, Guofu, 1995. "Small sample rank tests with applications to asset pricing," Journal of Empirical Finance, Elsevier, vol. 2(1), pages 71-93, March.
    25. Philippe J. Deschamps, 1996. "Monte Carlo Methodology for LM and LR Autocorrelation Tests in Multivariate Regression," Annals of Economics and Statistics, GENES, issue 43, pages 149-169.
    26. repec:bla:joares:v:23:y:1985:i:1:p:408-415 is not listed on IDEAS
    27. Zhou, Guofu, 1991. "Small sample tests of portfolio efficiency," Journal of Financial Economics, Elsevier, vol. 30(1), pages 165-191, November.
    28. Jobson, J. D. & Korkie, Bob, 1989. "A Performance Interpretation of Multivariate Tests of Asset Set Intersection, Spanning, and Mean-Variance Efficiency," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 24(02), pages 185-204, June.
    29. Jean-Marie Dufour & Abdeljelil Farhat & Lucien Gardiol & Lynda Khalaf, 1998. "Simulation-based finite sample normality tests in linear regressions," Econometrics Journal, Royal Economic Society, vol. 1(Conferenc), pages 154-173.
    30. Dufour, Jean-Marie, 1990. "Exact Tests and Confidence Sets in Linear Regressions with Autocorrelated Errors," Econometrica, Econometric Society, vol. 58(2), pages 475-494, March.
    31. Chou, Pin-Huang, 2000. "Alternative Tests of the Zero-Beta CAPM," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 23(4), pages 469-493, Winter.
    32. repec:cup:etheor:v:11:y:1995:i:1:p:122-50 is not listed on IDEAS
    33. Jobson, J. D. & Korkie, Bob, 1982. "Potential performance and tests of portfolio efficiency," Journal of Financial Economics, Elsevier, vol. 10(4), pages 433-466, December.
    34. Fama, Eugene F & French, Kenneth R, 1995. " Size and Book-to-Market Factors in Earnings and Returns," Journal of Finance, American Finance Association, vol. 50(1), pages 131-155, March.
    35. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sébastien Laurent & Luc Bauwens & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109.

    More about this item

    Keywords

    capital asset pricing model; CAPM; mean-variance efficiency; non-normality; multivariate linear regression; uniform linear hypothesis; exact test; Monte Carlo test; bootstrap; nuisance parameters; specification test; diagnostics; GARCH; variance ratio test; modèle d'évaluation d'actifs financiers; CAPM; efficacité moyenne-variance; nonnormalité; modèle de régression multivarié; hypothèse uniforme linéaire; test de Monte Carlo; bootstrap; paramètre de nuisance; test de spécification; diagnostics; GARCH; test du ratio des variances.;

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • G1 - Financial Economics - - General Financial Markets
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cir:cirwor:2003s-34. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Webmaster). General contact details of provider: http://edirc.repec.org/data/ciranca.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.