Quantile Hedging in a semi-static market with model uncertainty
Erhan Bayraktar and
Gu Wang ()
Additional contact information
Gu Wang: Worcester Polytechnic Institute
Mathematical Methods of Operations Research, 2018, vol. 87, issue 2, 197-227
Abstract With model uncertainty characterized by a convex, possibly non-dominated set of probability measures, the agent minimizes the cost of hedging a path dependent contingent claim with given expected success ratio, in a discrete-time, semi-static market of stocks and options. Based on duality results which link quantile hedging to a randomized composite hypothesis test, an arbitrage-free discretization of the market is proposed as an approximation. The discretized market has a dominating measure, which guarantees the existence of the optimal hedging strategy and helps numerical calculation of the quantile hedging price. As the discretization becomes finer, the approximate quantile hedging price converges and the hedging strategy is asymptotically optimal in the original market.
Keywords: Quantile hedging; Model uncertainty; Semi-static hedging; Neyman–Pearson Lemma (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
http://link.springer.com/10.1007/s00186-017-0616-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Working Paper: Quantile Hedging in a Semi-Static Market with Model Uncertainty (2017)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:spr:mathme:v:87:y:2018:i:2:d:10.1007_s00186-017-0616-y
Ordering information: This journal article can be ordered from
Access Statistics for this article
Mathematical Methods of Operations Research is currently edited by Oliver Stein
More articles in Mathematical Methods of Operations Research from Springer, Gesellschaft für Operations Research (GOR), Nederlands Genootschap voor Besliskunde (NGB)
Bibliographic data for series maintained by Sonal Shukla ().