### Resumé

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.

Sprog | Engelsk |
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Dato | 2017 |

Antal sider | 58 |

Status | Udgivet - 2017 |

Begivenhed | The 77th Annual Meeting of American Finance Association. AFA 2017 - Sheraton Grand Chicago, Chicago, USA Varighed: 6 jan. 2017 → 8 jan. 2017 Konferencens nummer: 77 http://www.afajof.org/details/page/8672741/Paper-Submission-2017.html |

### Konference

Konference | The 77th Annual Meeting of American Finance Association. AFA 2017 |
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Nummer | 77 |

Lokation | Sheraton Grand Chicago |

Land | USA |

By | Chicago |

Periode | 06/01/2017 → 08/01/2017 |

Internetadresse |

### Citer dette

*Generalized Recovery*. Afhandling præsenteret på The 77th Annual Meeting of American Finance Association. AFA 2017, Chicago, USA.

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

Publikation: Bidrag til konference › Paper › Forskning › peer review

TY - CONF

T1 - Generalized Recovery

AU - Jensen,Christian Skov

AU - Lando,David

AU - Pedersen,Lasse Heje

PY - 2017

Y1 - 2017

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 -