Uncertainty in transportation capacity and cost poses a significant challenge for both shippers and carriers in the trucking industry. In the practice of adopting lean and demand-responsive logistics systems, orders are required to be delivered rapidly, accurately and reliably, even under demand uncertainty. These tougher demands on the industry motivate the need to introduce new instruments to manage transportation service contracts. One way to hedge these uncertainties is to use concepts from the theory of Real Options to craft derivative contracts, which we call truckload options in this dissertation. In its simplest form, a truckload call (put) option gives its holder the right to buy (sell) truckload services on a specific route, at a predetermined price on a predetermined date. The holder decides if a truckload option should be exercised depending on information available when the option expires.;Truckload options are not yet available, however, so the purpose of this dissertation is to develop a truckload options pricing model and to show the usefulness of truckload options to both shippers and carriers. Since the price of a truckload option depends on the spot price of a truckload move, we first model the dynamics of spot rates using a common stochastic process. Unlike financial markets where high frequency data are available, spot prices for trucking services are not public and we can only observe some monthly statistics. This complicates somewhat the estimation of necessary parameters, which we obtain via two independent methods (variogram analysis and maximum likelihood), before developing a truckload options pricing formula. Finally, a numerical illustration based on real data shows that truckload options would be quite valuable to the trucking industry.;This dissertation develops a method to create value through more flexible procurement contracts, which could benefit the trucking industry as a whole---particularly in an uncertain business environment. Truckload rates and options prices are rigorously investigated and modeled. In addition, parameter estimation for a continuous stochastic model is explored using discrete statistics. Finally, numerical examples are illustrated and a picture of truckload option trading is presented. Results suggest that truckload options have the potential of significantly benefiting the trucking and logistics industries.
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