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In Full-Information Estimates, Long-Run Risks Explain at Most a Quarter of P/D Variance, and Habit Explains Even Less

Andrew Y. Chen, Fabian Winkler () and Rebecca Wasyk

Critical Finance Review, 2021, vol. 10, issue 3, 329-381

Abstract: Many consumption-based models succeed in matching long lists of asset price moments. We propose an alternative, full-information Bayesian evaluation that decomposes the price-dividend ratio (p/d) into contributions from long-run risks, habit, and a residual. We find that long-run risks account for less than 25% of the variance of p/d and that habit’s contribution is negligible. This result is robust to the prior, including priors that assume long-run risks in consumption and highly persistent habit. However, the residual mostly tracks decades-long movements in p/d. At business cycle frequency, long-run risks explain about 70% of the movements of p/d while habit’s contribution stays negligible.

Keywords: Long run risks; Rare disasters; Habit; Bayesian estimation; Particle filter; Time-varying beliefs; Time-varying preferences; Excess volatility (search for similar items in EconPapers)
JEL-codes: C11 C15 E21 E30 E44 G10 G12 (search for similar items in EconPapers)
Date: 2021
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