Generalized Recovery

Publikation: Bidrag til konferencePaperForskningpeer review

Resumé

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 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.
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 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.

Konference

Konference2016 Annual Meeting of the Society for Economic Dynamics
LandFrankrig
ByToulouse
Periode30/06/201602/07/2016
Internetadresse

Citer dette

Skov Jensen, C., Lando, D., & Heje Pedersen, L. (2016). Generalized Recovery. Afhandling præsenteret på 2016 Annual Meeting of the Society for Economic Dynamics , Toulouse, Frankrig.
Skov Jensen, Christian ; Lando, David ; Heje Pedersen, Lasse. / Generalized Recovery. Afhandling præsenteret på 2016 Annual Meeting of the Society for Economic Dynamics , Toulouse, Frankrig.41 s.
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title = "Generalized Recovery",
abstract = "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.",
author = "{Skov Jensen}, Christian and David Lando and {Heje Pedersen}, Lasse",
year = "2016",
language = "English",
note = "null ; Conference date: 30-06-2016 Through 02-07-2016",
url = "https://www.economicdynamics.org/2016-sed-meeting/",

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Skov Jensen, C, Lando, D & Heje Pedersen, L 2016, 'Generalized Recovery' Paper fremlagt ved 2016 Annual Meeting of the Society for Economic Dynamics , Toulouse, Frankrig, 30/06/2016 - 02/07/2016, .

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

2016. Afhandling præsenteret på 2016 Annual Meeting of the Society for Economic Dynamics , Toulouse, Frankrig.

Publikation: Bidrag til konferencePaperForskningpeer 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 -

Skov Jensen C, Lando D, Heje Pedersen L. Generalized Recovery. 2016. Afhandling præsenteret på 2016 Annual Meeting of the Society for Economic Dynamics , Toulouse, Frankrig.