Gain-loss based convex risk limits in discrete-time trading

dc.citation.epage321en_US
dc.citation.issueNumber3en_US
dc.citation.spage299en_US
dc.citation.volumeNumber8en_US
dc.contributor.authorPinar, M. C.en_US
dc.date.accessioned2015-07-28T11:59:51Z
dc.date.available2015-07-28T11:59:51Z
dc.date.issued2011-08en_US
dc.departmentDepartment of Industrial Engineeringen_US
dc.description.abstractWe present an approach for pricing and hedging in incomplete markets, which encompasses other recently introduced approaches for the same purpose. In a discrete time, finite space probability framework conducive to numerical computation we introduce a gain–loss ratio based restriction controlled by a loss aversion parameter, and characterize portfolio values which can be traded in discrete time to acceptability. The new risk measure specializes to a well-known risk measure (the Carr–Geman– Madan risk measure) for a specific choice of the risk aversion parameter, and to a robust version of the gain–loss measure (the Bernardo–Ledoit proposal) for a specific choice of thresholds. The result implies potentially tighter price bounds for contingent claims than the no-arbitrage price bounds. We illustrate the price bounds through numerical examples from option pricing.en_US
dc.description.provenanceMade available in DSpace on 2015-07-28T11:59:51Z (GMT). No. of bitstreams: 1 10.1007-s10287-010-0122-7.pdf: 323969 bytes, checksum: 46301cd0355fcb7d4eb803a1dde279e5 (MD5)en
dc.identifier.doi10.1007/s10287-010-0122-7en_US
dc.identifier.eissn1619-6988
dc.identifier.issn1619-697X
dc.identifier.urihttp://hdl.handle.net/11693/12051
dc.language.isoEnglishen_US
dc.publisherSpringer -Verlagen_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/s10287-010-0122-7en_US
dc.source.titleComputational Management Scienceen_US
dc.subjectIncomplete Marketsen_US
dc.subjectAcceptabilityen_US
dc.subjectMartingale Measureen_US
dc.subjectContingent Claimen_US
dc.subjectPricingen_US
dc.titleGain-loss based convex risk limits in discrete-time tradingen_US
dc.typeArticleen_US

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