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A Comparison of Risk-Premium Forecasts implied by Parametric versus Nonparametric Conditional Mean Estimators

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  • Thomas H. McCurdy
  • Thansis Stengos
Abstract
This paper computes parametric estimates of a time-varying risk premium model and compares the one-step-ahead forecasts implied by that model with those given by a nonparametric kernel estimator of the conditional mean function. The conditioning information used for the nonparametric analysis is that implied by the theoretical model of time-varying risk. Thus, the kernel estimator is used, in conjunction with a nonparametric diagnostic test for in-sample residual nonlinear structure, to assess the adequacy of the parametric model in capturing any structure in the excess returns. Our results support the parametric specification of an asset pricing model in which the conditional beta is the ratio of the relevant components of the conditional covariance matrix of returns modeled as a bivariate generalized ARCH process. Although the predictable component of the conditional moments is relatively small, the parametric estimator of the risk premia has somewhat more out-of-sample forecasting ability than does the kernel estimator. Hence, the superior in-sample performance of the latter may be attributed to overfitting.

Suggested Citation

  • Thomas H. McCurdy & Thansis Stengos, 1991. "A Comparison of Risk-Premium Forecasts implied by Parametric versus Nonparametric Conditional Mean Estimators," Working Paper 843, Economics Department, Queen's University.
    • Handle: RePEc:qed:wpaper:843
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    File URL: http://qed.econ.queensu.ca/working_papers/papers/qed_wp_843.pdf
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    Cited by:

    1. Paul D. McNelis & G.C. Lim, 1998. "Parameterizing Currency Risk in the EMS: The Irish Pound and Spanish Peseta against the German Mark," International Finance 9805001, University Library of Munich, Germany.
    2. Bong-Chan, Kho, 1996. "Time-varying risk premia, volatility, and technical trading rule profits: Evidence from foreign currency futures markets," Journal of Financial Economics, Elsevier, vol. 41(2), pages 249-290, June.
    3. 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.
    4. Jiayuan Zhou & Feiyu Jiang & Ke Zhu & Wai Keung Li, 2019. "Time series models for realized covariance matrices based on the matrix-F distribution," Papers 1903.12077, arXiv.org, revised Jul 2020.
    5. Ten-Der Jane & Cherng Ding, 2009. "On the multivariate EGARCH model," Applied Economics Letters, Taylor & Francis Journals, vol. 16(17), pages 1757-1761.
    6. Bollerslev, Tim & Engle, Robert F. & Nelson, Daniel B., 1986. "Arch models," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 49, pages 2959-3038, Elsevier.
    7. De Arce Borda, R., 2004. "20 años de modelos ARCH: una visión de conjunto de las distintas variantes de la familia/20 Years of Arch Modelling: a Survey of Different Models in the Family," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 22, pages 1-27, Abril.
    8. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    9. Robert Engle, 2002. "New frontiers for arch models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 425-446.

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