### Abstract

Language | English |
---|---|

Date | 2016 |

Number of pages | 59 |

State | Published - 2016 |

Event | The 43rd European Finance Association Annual Meeting (EFA 2016) - BI Norwegian Business School, Oslo, Norway Duration: 17 Aug 2016 → 20 Aug 2016 Conference number: 43 http://www.efa2016.org/ |

### Conference

Conference | The 43rd European Finance Association Annual Meeting (EFA 2016) |
---|---|

Number | 43 |

Location | BI Norwegian Business School |

Country | Norway |

City | Oslo |

Period | 17/08/2016 → 20/08/2016 |

Internet address |

### Cite this

*Generalized Recovery*. Paper presented at The 43rd European Finance Association Annual Meeting (EFA 2016), Oslo, Norway.

}

**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 relation between the number of time periods and 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 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 relation between the number of time periods and 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 and other recovered statistics.

M3 - Paper

ER -