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This research applies a static hedging portfolio method derived from Derman, Ergener, and Kani (1995) (henceforth Derman's SHP method) and a new SHP method with European cash-or-nothing binary options developed by Chung, Shih, and Tsai (2013) to price European continuous double barrier (ECDB) options and the rebates of the ECDB options. Our numerical results indicate that the new SHP method outperforms Derman's SHP method in terms of efficiency and effectiveness under all circumstances.

This chapter discusses the formulation of an agent-based model to simulate day-to-day dynamics in activity-travel patterns, based on short and long-term adaptations to exogenous and exogenous changes.

The model is based on theoretical considerations of bounded rationality. Agents are able to explore the area, adapt their aspirations and develop habitual behaviour. If they experience dissatisfaction, stress emerges and this may lead to short or long-term adaptations of an agent’s activity-travel patterns. Both cognitive and affective responses are taken into account, when agents evaluate available options. Moreover, memory-activation and forgetting processes play a significant role in the development of habitual behaviour.

Results of numerical simulations show the effect of memory-activation and emotion-related parameters on habit formation, on the decision-making process and on overall model behaviour. Effects of specific aspects of bounded rationality on the evolution of dynamics in the activity-travel patterns of an individual are illustrated. Effects seem realistic, behaviourally rich and, therefore, more sensitive to a larger spectrum of policies.

The model is unique in its kind. It is one of the first attempts to formulate a dynamic model of activity-travel behaviour, based on principle of bounded rationality, which includes both cognitive and affective mechanism of adaptation.

In this chapter, an air cargo shipment planning problem is considered by including individual risk factors of any sub-contracted agents. Due to competitive market conditions, air cargo forwarders are advised to remain flexible in operations. A mixed integer linear programming formulation including the potential for divisible activities is developed to model the shipment planning problem. To solve this complicated problem, we employ an ant colony optimization (ACO) methodology. Numerical examples are generated using data from both the extant literature and from a global air cargo company, allowing investigation of the viability of the novel methodology. We find that the algorithm/methodology provides effective solutions for small problem sizes.

A futures contract may rely upon physical delivery or cash settlement to liquidate open positions at the maturity date. Contract settlement specification has direct impacts on the behavior of the futures price, leading to different effects of liquidity risk on futures hedging. This chapter compares such effects under alternative settlement specifications with a simple analytical model of daily price change. Numerical simulation results demonstrate that capital constraint reduces hedging effectiveness and tends to produce a lower optimal hedge ratio. As the futures contract proceeds toward the maturity date, hedgers will take larger hedge position in order to achieve better hedging effectiveness. Finally, optimal hedge ratios are higher (resp. lower) under cash settlement for the bivariate normal (resp. lognormal) assumptions, whereas hedging effectiveness is almost always greater under cash settlement.

The estimation of queue length and delays in queues that are oversaturated for some part of a study period is of substantial importance in a range of traffic engineering applications. Whiting’s co-ordinate transformation has provided the basis for several approaches to this. We analyse this approach and present an explicit form for the derivative of queue length with respect to time, which we then use to establish various properties. We also report the results of numerical comparisons with exact formulae for certain special cases and show that these offer little or no advantage over the co-ordinate transformation approximations and can be computationally impractical in study periods of moderate duration.

We propose a new model for the dynamics of the aggregate credit portfolio loss. The model is Markovian in two dimensions with the state variables being the total accumulated loss Lt and the stochastic default intensity λt. The dynamics of the default intensity are governed by the equation dλt=κ(ρ(Lt,t)−λt)dt+σλtdWt. The function ρ depends both on time t and accumulated loss Lt, providing sufficient freedom to calibrate the model to a generic distribution of loss. We develop a computationally efficient method for model calibration to the market of synthetic single tranche collateralized debt obligations (CDOs). The method is based on the Markovian projection technique which reduces the full model to a one-step Markov chain having the same marginal distributions of loss. We show that once the intensity function of the effective Markov chain consistent with the loss distribution implied by the tranches is found, the function ρ can be recovered with a very moderate computational effort. Because our model is Markovian and has low dimensionality, it offers a convenient framework for the pricing of dynamic credit instruments, such as options on indices and tranches, by backward induction. We calibrate the model to a set of recent market quotes on CDX index tranches and apply it to the pricing of tranche options.

The mathematical models used to describe the dynamical behaviour of a group of closely-spaced road vehicles travelling in a single lane without overtaking are known as car-following models. This paper presents a novel car-following model, which differs from the traditional models by having an equilibrium solution that corresponds to consecutive vehicles having not only zero relative velocity, but also travelling at a certain desired distance apart. This new model is investigated using both numerical and analytical techniques. For many parameter values the equilibrium solution is stable to a periodic perturbation but, for certain parameter values, chaotic motion results. This shows that in congested traffic, even drivers attempting to follow a safe driving strategy, may find themselves driving in an unpredictable fashion.

Disassembly takes place in remanufacturing, recycling, and disposal, with a line being the best choice for automation. The disassembly line balancing problem seeks a sequence that is feasible, minimizes the number of workstations, and ensures similar idle times, as well as other end-of-life specific concerns. Finding the optimal balance is computationally intensive due to exponential growth. Combinatorial optimization methods hold promise for providing solutions to the problem, which is proven here to be NP-hard. Stochastic (genetic algorithm) and deterministic (greedy/hill-climbing hybrid heuristic) methods are presented and compared. Numerical results are obtained using a recent electronic product case study.