Uncertainties in transportation capacity and cost pose 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, 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 slightly the
estimation of necessary parameters, which we obtain via two independent
methods (variogram analysis and maximum likelihood), before developing a
truckload options pricing formula. 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 rate and truckload options price are solidly investigated
and modeled. In addition, parameter estimation for a continuous
stochastic model is practically explored using discrete statistics.
Finally, numerical trading examples are illustrated and a picture of
truckload option trading becoming reality presented. The complicated
results indicate that truckload options have the potential of
stimulating the entire trucking and logistics industries.
