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

KonferenceThe 77th Annual Meeting of American Finance Association. AFA 2017
Nummer77
LokationSheraton Grand Chicago
LandUSA
ByChicago
Periode06/01/201708/01/2017
Internetadresse

Citer dette

Jensen, C. S., Lando, D., & Pedersen, L. H. (2017). Generalized Recovery. Afhandling præsenteret på The 77th Annual Meeting of American Finance Association. AFA 2017, Chicago, USA.
Jensen, Christian Skov ; Lando, David ; Pedersen, Lasse Heje. / Generalized Recovery. Afhandling præsenteret på The 77th Annual Meeting of American Finance Association. AFA 2017, Chicago, USA.58 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 = "Jensen, {Christian Skov} and David Lando and Pedersen, {Lasse Heje}",
year = "2017",
language = "English",
note = "null ; Conference date: 06-01-2017 Through 08-01-2017",
url = "http://www.afajof.org/details/page/8672741/Paper-Submission-2017.html",

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Jensen, CS, Lando, D & Pedersen, LH 2017, 'Generalized Recovery' Paper fremlagt ved The 77th Annual Meeting of American Finance Association. AFA 2017, Chicago, USA, 06/01/2017 - 08/01/2017, .

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

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

Publikation: Bidrag til konferencePaperForskningpeer 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 -

Jensen CS, Lando D, Pedersen LH. Generalized Recovery. 2017. Afhandling præsenteret på The 77th Annual Meeting of American Finance Association. AFA 2017, Chicago, USA.