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American Option Pricing under Markovian Regime Switching Model
Department of Informatics, University of Electro-communications, Tokyo, Japan.
Department of Informatics, University of Electro-communications, Tokyo, Japan.
Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics. (MAM)ORCID iD: 0000-0002-0835-7536
2019 (English)In: Proceedings of 18th Applied Stochastic Models and Data Analysis International Conference with the Demographics 2019 Workshop, Florence, Italy: 11-14 June, 2019 / [ed] Christos H. Skiadas, ISAST: International Society for the Advancement of Science and Technology , 2019, p. 515-522Conference paper, Published paper (Refereed)
Abstract [en]

In this research, we consider the pricing of American options when the price dynamics of the underlying risky assets is governed by Markovian regime switching process. We assume that the price dynamics depends on the economy, the state of which transits based on a discrete-time Markov chain. The underlying economy cannot be known directly but can be partially observed by receiving a signal stochastically related to the real state of economy. The pricing procedure and optimal stopping problem are formulated using partially observable Markov decision process, and some structural properties of the resulting optimal expected payoff functions are derived under certain assumptions. These properties establish the existence of a monotonic policy with respect to the holding time, asset price, and economic conditions.

Place, publisher, year, edition, pages
ISAST: International Society for the Advancement of Science and Technology , 2019. p. 515-522
Keywords [en]
Decision policy, Hidden Markov chain, Optimal strategy, Partially observable Markov decision process, Totally positive of order 2
National Category
Probability Theory and Statistics
Research subject
Mathematics/Applied Mathematics
Identifiers
URN: urn:nbn:se:mdh:diva-47130OAI: oai:DiVA.org:mdh-47130DiVA, id: diva2:1395097
Conference
ASMDA2019, 18th Applied Stochastic Models and Data Analysis International Conference
Available from: 2020-02-20 Created: 2020-02-20 Last updated: 2020-02-24Bibliographically approved

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Ni, Ying

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