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Learning and Earning : Optimal Stopping and Partial Information in Real Options Valuation

In this thesis, we consider an optimal stopping problem interpreted as the task of valuating two so called real options written on an underlying asset following the dynamics of an observable geometric Brownian motion with non-observable drift; we have incomplete information. After exercising the first real option, however, the value of the underlying asset becomes observable with reduced noise; we obtain partial information. We then state some theoretical properties of the value function such as convexity and monotonicity. Furthermore, numerical solutions for the value functions are obtained by stating and solving a linear complementary problem. This is done in a Python implementation using the 2nd order backward differentiation formula and summation-by-parts operators for finite differences combined with an operator splitting method.

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-531219
Date January 2024
CreatorsSätherblom, Eric Marco Raymond
PublisherUppsala universitet, Sannolikhetsteori och kombinatorik
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeStudent thesis, info:eu-repo/semantics/bachelorThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess
RelationU.U.D.M. project report ; 2024:6

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