dc.contributor.author Wang, Xuyan dc.date.accessioned 2007-05-22 18:22:08 (GMT) dc.date.available 2007-05-22 18:22:08 (GMT) dc.date.issued 2007-05-22T18:22:08Z dc.date.submitted 2007 dc.identifier.uri http://hdl.handle.net/10012/3075 dc.description.abstract Lookback option is a well-known path-dependent option where its en payoff depends on the historical extremum prices. The thesis focuses on the binomial pricing of the American floating strike lookback put options with payoff at time \$t\$ (if exercise) characterized by \[ \max_{k=0, \ldots, t} S_k - S_t, \] where \$S_t\$ denotes the price of the underlying stock at time \$t\$. Build upon the idea of \hyperlink{RBCV}{Reiner Babbs Cheuk and Vorst} (RBCV, 1992) who proposed a transformed binomial lattice model for efficient pricing of this class of option, this thesis extends and enhances their binomial recursive algorithm by exploiting the additional combinatorial properties of the lattice structure. The proposed algorithm is not only computational efficient but it also significantly reduces the memory constraint. As a result, the proposed algorithm is more than 1000 times faster than the original RBCV algorithm and it can compute a binomial lattice with one million time steps in less than two seconds. This algorithm enables us to extrapolate the limiting (American) option value up to 4 or 5 decimal accuracy in real time. dc.format.extent 593753 bytes dc.format.mimetype application/pdf dc.language.iso en en dc.publisher University of Waterloo en dc.subject American Lookback Put Option en dc.subject Binomial Lattice Model en dc.subject uniformity en dc.subject exercise barrier en dc.subject monotonicity en dc.subject exercise propagation en dc.title Efficient Procedure for Valuing American Lookback Put Options en dc.type Master Thesis en dc.pending false en dc.subject.program Actuarial Science en uws-etd.degree.department Statistics and Actuarial Science en uws-etd.degree Master of Mathematics en uws.typeOfResource Text en uws.peerReviewStatus Unreviewed en uws.scholarLevel Graduate en
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