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Semiparametric inference in a GARCH-in-mean model

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  • Christensen, Bent Jesper
  • Dahl, Christian M.
  • Iglesias, Emma M.

Abstract

A new semiparametric estimator for an empirical asset pricing model with general nonparametric risk-return tradeoff and GARCH-type underlying volatility is introduced. Based on the profile likelihood approach, it does not rely on any initial parametric estimator of the conditional mean function, and it is under stated conditions consistent, asymptotically normal, and efficient, i.e., it achieves the semiparametric lower bound. A sampling experiment provides finite sample comparisons with the parametric approach and the iterative semiparametric approach with parametric initial estimate of Conrad and Mammen (2008). An application to daily stock market returns suggests that the risk-return relation is indeed nonlinear.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 167 (2012)
Issue (Month): 2 ()
Pages: 458-472

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Handle: RePEc:eee:econom:v:167:y:2012:i:2:p:458-472

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Web page: http://www.elsevier.com/locate/jeconom

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Keywords: Efficiency bound; GARCH-M model; Profile likelihood; Risk-return relation; Semiparametric inference;

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References

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Cited by:
  1. Christian Conrad & Enno Mammen, 2008. "Nonparametric Regression on Latent Covariates with an Application to Semiparametric GARCH-in-Mean Models," Working Papers 0473, University of Heidelberg, Department of Economics, revised Jul 2008.

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