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 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.
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.

Konference

KonferenceThe 43rd European Finance Association Annual Meeting (EFA 2016)
Nummer43
LokationBI Norwegian Business School
LandNorge
ByOslo
Periode17/08/201620/08/2016
Internetadresse

Citer dette

Skov Jensen, C., Lando, D., & Heje Pedersen, L. (2016). Generalized Recovery. Afhandling præsenteret på The 43rd European Finance Association Annual Meeting (EFA 2016), Oslo, Norge.
Skov Jensen, Christian ; Lando, David ; Heje Pedersen, Lasse. / Generalized Recovery. Afhandling præsenteret på The 43rd European Finance Association Annual Meeting (EFA 2016), Oslo, Norge.59 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 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 = "null ; 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, .

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

2016. Afhandling præsenteret på The 43rd European Finance Association Annual Meeting (EFA 2016), Oslo, Norge.

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 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 -

Skov Jensen C, Lando D, Heje Pedersen L. Generalized Recovery. 2016. Afhandling præsenteret på The 43rd European Finance Association Annual Meeting (EFA 2016), Oslo, Norge.