Give me strong moments and time: combining GMM and SMM to estimate long-run risk asset pricing models

  • The long-run consumption risk (LRR) model is a promising approach to resolve prominent asset pricing puzzles. The simulated method of moments (SMM) provides a natural framework to estimate its deep parameters, but caveats concern model solubility and weak identification. We propose a two-step estimation strategy that combines GMM and SMM, and for which we elicit informative macroeconomic and financial moment matches from the LRR model structure. In particular, we exploit the persistent serial correlation of consumption and dividend growth and the equilibrium conditions for market return and risk-free rate, as well as the model-implied predictability of the risk-free rate. We match analytical moments when possible and simulated moments when necessary and determine the crucial factors required for both identification and reasonable estimation precision. A simulation study – the first in the context of long-run risk modeling – delineates the pitfalls associated with SMM estimation of a non-linear dynamic asset pricing model. Our study provides a blueprint for successful estimation of the LRR model.

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Metadaten
Author:Joachim Grammig, Eva-Maria Schaub
URN:urn:nbn:de:hebis:30:3-351112
URL:http://ssrn.com/abstract=2508125
DOI:https://doi.org/10.2139/ssrn.2508125
Parent Title (English):Center for Financial Studies (Frankfurt am Main): CFS working paper series ; No. 479
Series (Serial Number):CFS working paper series (479)
Publisher:Center for Financial Studies
Place of publication:Frankfurt, M.
Document Type:Working Paper
Language:English
Year of Completion:2014
Year of first Publication:2014
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2014/10/20
Tag:asset pricing; long-run risk; simulated method of moments
Issue:July 22, 2014
Page Number:60
HeBIS-PPN:351157727
Institutes:Wirtschaftswissenschaften / Wirtschaftswissenschaften
Wissenschaftliche Zentren und koordinierte Programme / Center for Financial Studies (CFS)
Dewey Decimal Classification:3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft
Sammlungen:Universitätspublikationen
Licence (German):License LogoDeutsches Urheberrecht