Pricing and hedging of contingent claims in incomplete markets
Pınar, Mustafa Ç.
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In this thesis, we analyze the problem of pricing and hedging contingent claims in the multi-period, discrete time, discrete state case. We work on both European and American type contingent claims. For European contingent claims, we analyze the problem using the concept of a “λ gain-loss ratio opportunity”. Pricing results which are somewhat different from, but reminiscent of, the arbitrage pricing theorems of mathematical finance are obtained. Our analysis provides tighter price bounds on the contingent claim in an incomplete market, which may converge to a unique price for a specific value of a gain-loss preference parameter imposed by the market while the hedging policies may be different for different sides of the same trade. The results are obtained in the simpler framework of stochastic linear programming in a multiperiod setting. They also extend to markets with transaction costs. Until now, determining the buyer’s price for American contingent claims (ACC) required solving an integer program unlike European contingent claims for which solving a linear program is sufficient. We show that a relaxation of the integer programming problem which is a linear program, can be used to get the buyer’s price for an ACC. We also study the problem of computing the lower hedging price of an American contingent claim in a market where proportional transaction costs exist. We derive a new mixed-integer linear programming formulation for calculating the lower hedging price. We also present and discuss an alternative, aggregate formulation with similar properties. Our results imply that it might be optimal for the holder of several identical American claims to exercise portions of the portfolio at different time points in the presence of proportional transaction costs while this incentive disappears in their absence. We also exhibit some counterexamples for some new ideas based on our work. We believe that these counterexamples are important in determining the direction of research on the subject.
Mixed Integer Programming
Stochastic Linear Programming