Browsing by Subject "Stochastic linear programming"

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    Expected gain-loss pricing and hedging of contingent claims in incomplete markets by linear programming
    (Elsevier, 2010) Pınar, M. Ç.; Salih, A.; Camcı, A.
    We analyze the problem of pricing and hedging contingent claims in the multi-period, discrete time, discrete state case using the concept of a "λ gain-loss ratio opportunity". Pricing results 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 multi-period setting, and have the appealing feature of being very simple to derive and to articulate even for the non-specialist. They also extend to markets with transaction costs.
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    Pricing American contingent claims by stochastic linear programming
    (Taylor & Francis, 2009) Camcı, A.; Pınar, M. Ç.
    We consider pricing of American contingent claims (ACC) as well as their special cases, in a multi-period, discrete time, discrete state space setting. Until now, determining the buyer's price for ACCs required solving an integer programme unlike European contingent claims for which solving a linear programme is sufficient. However, we show that a relaxation of the integer programming problem that is a linear programme, can be used to get the same lower bound for the price of the ACC.