Higher-order effects in asset-pricing models with long-run risks

W. Pohl, K. Schmedders, Ole Wilms

Research output: Contribution to journalArticleScientificpeer-review

Abstract

This paper shows that the latest generation of asset pricing models with long-run risk exhibits economically significant nonlinearities, and thus the ubiquitous Campbell--Shiller log-linearization can generate large numerical errors. These errors in turn translate to considerable errors in the model predictions, for example, for the magnitude of the equity premium or return predictability. We demonstrate that these nonlinearities arise from the presence of multiple very persistent processes, which cause the exogenous states to attain values far away from their long-run means with non-negligible probability. These extreme values have a significant impact on asset price dynamics.
Original languageEnglish
Pages (from-to)1061-1111
JournalJournal of Finance
Volume73
Issue number3
DOIs
Publication statusPublished - Jun 2018

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Asset pricing models
Order effects
Nonlinearity
Equity returns
Equity premium
Extreme values
Price dynamics
Asset prices
Return predictability
Linearization
Prediction model

Keywords

  • asset pricing
  • discretization
  • log-linearization
  • nonlinear dynamics
  • projection methods

Cite this

Pohl, W. ; Schmedders, K. ; Wilms, Ole. / Higher-order effects in asset-pricing models with long-run risks. In: Journal of Finance. 2018 ; Vol. 73, No. 3. pp. 1061-1111.
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Higher-order effects in asset-pricing models with long-run risks. / Pohl, W.; Schmedders, K.; Wilms, Ole.

In: Journal of Finance, Vol. 73, No. 3, 06.2018, p. 1061-1111.

Research output: Contribution to journalArticleScientificpeer-review

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