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dc.contributor.authorErnst, Philip A.
Rogers, L.C.G.
Zhou, Quan
dc.date.accessioned 2017-12-19T15:24:10Z
dc.date.available 2017-12-19T15:24:10Z
dc.date.issued 2017
dc.identifier.citation Ernst, Philip A., Rogers, L.C.G. and Zhou, Quan. "The value of foresight." Stochastic Processes and their Applications, 127, no. 12 (2017) Elsevier: 3913-3927. https://doi.org/10.1016/j.spa.2017.03.019.
dc.identifier.urihttps://hdl.handle.net/1911/98899
dc.description.abstract Suppose you have one unit of stock, currently worth 1, which you must sell before time . The Optional Sampling Theorem tells us that whatever stopping time we choose to sell, the expected discounted value we get when we sell will be 1. Suppose however that we are able to see units of time into the future, and base our stopping rule on that; we should be able to do better than expected value 1. But how much better can we do? And how would we exploit the additional information? The optimal solution to this problem will never be found, but in this paper we establish remarkably close bounds on the value of the problem, and we derive a fairly simple exercise rule that manages to extract most of the value of foresight.
dc.language.iso eng
dc.publisher Elsevier
dc.rightsThis is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.title The value of foresight
dc.type Journal article
dc.citation.journalTitle Stochastic Processes and their Applications
dc.subject.keywordexplicit stopping rules
excursion processes
insider trading
Bermudan fixed-window lookback option
Brownian motion
dc.citation.volumeNumber 127
dc.citation.issueNumber 12
dc.identifier.digital value-of-foresight
dc.type.dcmi Text
dc.identifier.doihttps://doi.org/10.1016/j.spa.2017.03.019
dc.type.publication publisher version
dc.citation.firstpage 3913
dc.citation.lastpage 3927


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