Generalized Recovery

Christian Skov Jensen; David Lando; Lasse Heje Pedersen

Generalized Recovery

Christian Skov Jensen, David Lando, Lasse Heje Pedersen

Institut for Finansiering

Publikation: Konferencebidrag › Paper › Forskning › peer review

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 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.

Originalsprog	Engelsk
Publikationsdato	2016
Antal sider	59
Status	Udgivet - 2016
Begivenhed	The 43rd European Finance Association Annual Meeting (EFA 2016) - BI Norwegian Business School, Oslo, Norge Varighed: 17 aug. 2016 → 20 aug. 2016 Konferencens nummer: 43 http://www.efa2016.org/

Konference

Konference	The 43rd European Finance Association Annual Meeting (EFA 2016)
Nummer	43
Lokation	BI Norwegian Business School
Land/Område	Norge
By	Oslo
Periode	17/08/2016 → 20/08/2016
Internetadresse	http://www.efa2016.org/

Adgang til dokumentet

https://www.conftool.com/efa2016/index.php/EFA2016-1080-APT-01-Jensen-Generalized_Recovery.pdf?page=downloadPaper&filename=EFA2016-1080-APT-01-Jensen-Generalized_Recovery.pdf&form_id=1080&form_version=finalLicens: Ikke-specificeret

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Citationsformater

Skov Jensen, C., Lando, D., & Heje Pedersen, L. (2016). Generalized Recovery. Paper præsenteret på The 43rd European Finance Association Annual Meeting (EFA 2016), Oslo, Norge. https://www.conftool.com/efa2016/index.php/EFA2016-1080-APT-01-Jensen-Generalized_Recovery.pdf?page=downloadPaper&filename=EFA2016-1080-APT-01-Jensen-Generalized_Recovery.pdf&form_id=1080&form_version=final

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author = "{Skov Jensen}, Christian and David Lando and {Heje Pedersen}, Lasse",

year = "2016",

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note = "The 43rd European Finance Association Annual Meeting (EFA 2016) ; Conference date: 17-08-2016 Through 20-08-2016",

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Skov Jensen, C, Lando, D & Heje Pedersen, L 2016, 'Generalized Recovery', Paper fremlagt ved The 43rd European Finance Association Annual Meeting (EFA 2016), Oslo, Norge, 17/08/2016 - 20/08/2016. <https://www.conftool.com/efa2016/index.php/EFA2016-1080-APT-01-Jensen-Generalized_Recovery.pdf?page=downloadPaper&filename=EFA2016-1080-APT-01-Jensen-Generalized_Recovery.pdf&form_id=1080&form_version=final>

TY - CONF

T1 - Generalized Recovery

AU - Skov Jensen, Christian

AU - Lando, David

AU - Heje Pedersen, Lasse

N1 - Conference code: 43

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

T2 - The 43rd European Finance Association Annual Meeting (EFA 2016)

Y2 - 17 August 2016 through 20 August 2016

ER -