### Abstract

between the number of time periods on the number of states. When recovery is feasible, our model is easy to implement, allowing a closed-form linearized solution. We implement our model empirically, testing the predictive power of the recovered expected return, crash risk, and other recovered statistics.

between the number of time periods on the number of states. When recovery is feasible, our model is easy to implement, allowing a closed-form linearized solution. We implement our model empirically, testing the predictive power of the recovered expected return, crash risk, and other recovered statistics.

Language | English |
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Date | 2016 |

Number of pages | 41 |

State | Published - 2016 |

Event | 2016 Annual Meeting of the Society for Economic Dynamics - Toulouse, France Duration: 30 Jun 2016 → 2 Jul 2016 https://www.economicdynamics.org/2016-sed-meeting/ |

### Conference

Conference | 2016 Annual Meeting of the Society for Economic Dynamics |
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Country | France |

City | Toulouse |

Period | 30/06/2016 → 02/07/2016 |

Internet address |

### Cite this

*Generalized Recovery*. Paper presented at 2016 Annual Meeting of the Society for Economic Dynamics , Toulouse, France.

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**Generalized Recovery.** / Skov Jensen, Christian; Lando, David; Heje Pedersen, Lasse.

Research output: Contribution to conference › Paper › Research › peer-review

TY - CONF

T1 - Generalized Recovery

AU - Skov Jensen,Christian

AU - Lando,David

AU - Heje Pedersen,Lasse

PY - 2016

Y1 - 2016

N2 - We characterize when physical probabilities, marginal utilities, and the discount rate can be recovered from observed state prices for several future time periods. Our characterization makes no assumptions of the probability distribution, thus generalizing the time-homogeneous stationary model of Ross (2015). Our characterization is simple and intuitive, linking recovery to the relationbetween the number of time periods on the number of states. When recovery is feasible, our model is easy to implement, allowing a closed-form linearized solution. We implement our model empirically, testing the predictive power of the recovered expected return, crash risk, and other recovered statistics.

AB - We characterize when physical probabilities, marginal utilities, and the discount rate can be recovered from observed state prices for several future time periods. Our characterization makes no assumptions of the probability distribution, thus generalizing the time-homogeneous stationary model of Ross (2015). Our characterization is simple and intuitive, linking recovery to the relationbetween the number of time periods on the number of states. When recovery is feasible, our model is easy to implement, allowing a closed-form linearized solution. We implement our model empirically, testing the predictive power of the recovered expected return, crash risk, and other recovered statistics.

M3 - Paper

ER -