Generalized Recovery

Christian Skov Jensen; David Lando; Lasse Heje Pedersen

Generalized Recovery

Christian Skov Jensen, David Lando, Lasse Heje Pedersen

Department of Finance

Research output: Contribution to conference › Paper › Research › 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.

Original language	English
Publication date	2016
Number of pages	59
Publication status	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/Territory	Norway
City	Oslo
Period	17/08/2016 → 20/08/2016
Internet address	http://www.efa2016.org/

Access to Document

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=finalLicence: Unspecified

Handle.net

Persistent link

Cite this

Skov Jensen, C., Lando, D., & Heje Pedersen, L. (2016). Generalized Recovery. Paper presented at The 43rd European Finance Association Annual Meeting (EFA 2016), Oslo, Norway. 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

@conference{587bf6726da04c47be4aaa922e667375,

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 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.",

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

year = "2016",

language = "English",

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

url = "http://www.efa2016.org/",

}

Skov Jensen, C, Lando, D & Heje Pedersen, L 2016, 'Generalized Recovery', Paper presented at The 43rd European Finance Association Annual Meeting (EFA 2016), Oslo, Norway, 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 -