Contingent claims whose values depend on multiple sources of uncertainty arise in many financial contracts and in the analysis of real projects. Unfortunately closed form solutions for these options are rare and numerical methods can be computationally expensive. This article extends the literature on multinomial approximating models. Specifically, new multinomial models are presented that include as special cases existing models. The more general models are shown to be computationally more efficient.contingent claims, option pricing, geometric Wiener processes, multinomial lattice
This paper develops an operational risk management model for evaluating production efforts in manufacturing and mining industries where the resource to be exploited is nonhomogenous. Using a contingent claims methodology now commonly encountered in financial applications, we formulate a production control model in an environment characterized by market and process uncertainty. In our analysis, market risk is depicted by the output price while process uncertainty is captured by the random variability inherent in the output's yield. In this light, adjustments to the rate of production are viewed as a sequence of (nested) real options affording operating flexibility. We account for an optimal sequence of production adjustments, over a preestablished production horizon, by taking the production rate as an adapted positive real-valued process. Accordingly, techniques of stochastic control theory and contingent claims analysis (CCA) are employed to ensure value maximizing production policies are rendered in a manner consistent with an equilibrium price structure.
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