Financial Engineering

This article compares the performance of ordinary Monte Carlo and quasi Monte Carlo methods in valuing moderate- and high-dimensional options. The dimensionality of the problems arises either from the number of times steps along a single path or from the number of underlying assets. We compare ordinary Monte Carlo with and without antithetic variates against Sobol', Faure, and Generalized Faure sequences and three constructions of a discretely sampled Brownian path.

Hedging with Trees: Advances in Pricing and Risk Managing Derivatives is a useful compendium of 35+ articles encapsulating recent advances in financial derivatives, selected by two well-respected academics. It includes advances in exotic options, computational techniques, VAR and capital management.

The accelerated pace of technological change has led to rapid obsolescence of productive capacity in electronics and other industries. Managers must consider the impact of future technologies while making acquisition and replacement decisions in such environments. We consider a problem where a sequence of technological breakthroughs are anticipated but their magnitude and timing are uncertain. A firm, operating in such an environment, must decide how much capacity of the current technology to acquire to meet future demand growth.

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A guiding principle in the efficient estimation of rare-event probabilities by Monte Carlo is that importance sampling based on the change of measure suggested by a large deviations analysis can reduce variance by many orders of magnitude. In a variety of settings, this approach has led to estimators that are optimal in an asymptotic sense. We give examples, however, in which importance sampling estimators based on a large deviations change of measure have provably poor performance.

We analyze a multistage inventory system with limited production capacity facing stochastic demands. Each node follows a periodic-review base-stock policy for echelon inventory: in each period, each node attempts to produce enough material to restore cumulative down-stream inventory to a fixed target level. We develop approximations to the key measures of interest (average inventories, average backorders, and service levels) by simultaneously letting the mean demand approach the system's bottleneck capacity and letting the base-stock level for finished goods increase without bound.

We provide a framework in which we link the valuation and asset allocation policies of defined benefits plans with the lifetime marginal productivity schedule of the worker and the pension plan formula. In turn, we examine the retirement policies that are implied by the primitives of the model and the value of pension obligations. Our model provides an explicit valuation formula for a stylized defined benefits plan. The optimal asset allocation policies consist of the replicating portfolio of the pension liabilities and the growth optimum portfolio independent of the pension liabilities.

The Monte Carlo approach has proved to be a valuable and flexible computational tool in modern finance. This paper discusses some of the recent applications of the Monte Carlo method to security pricing problems, with emphasis on improvements in efficiency. We first review some variance reduction methods that have proved useful in finance. Then we describe the use of deterministic low-discrepancy sequences, also known as quasi-Monte Carlo methods, for the valuation of complex derivative securities.