Is the Recursive Preference Asset Pricing Model More Flexible? A Monte Carlo Study

Zhonghui Zhang ()
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Zhonghui Zhang: Nanjing Audit University

Computational Economics, 2025, vol. 66, issue 4, No 30, 3605 pages

Abstract: Abstract This study investigates the challenge of accurately estimating the coefficient of relative risk aversion (CRRA) and the elasticity of intertemporal substitution (EIS) in the consumption-based asset pricing model with recursive preferences, as introduced by Epstein and Zin (Econometrica, 57, 937–969). Through Monte Carlo simulations, I find that the generalized method of moments exhibits finite-sample identification issues, complicating the distinction between the CRRA and EIS. The analysis reveals that the model’s objective function generates local minima along a straight line intersecting the true parameter values, but fails to produce a distinct pointwise minimum, making it challenging to separately estimate the CRRA and EIS over a broad range of EIS values. This indeterminacy underscores the necessity for new estimation techniques or model refinements to resolve significant empirical challenges in asset pricing, enhancing economic theory and policy making.

Keywords: Recursive preference; Monte Carlo simulation; CRRA and EIS estimation; C52; G12 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10614-024-10830-y

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