We prove a version of the Fundamental Theorem of Asset Pricing, which applies to Kabanov’s modelling of foreign exchange markets under transaction costs. The financial market is described by a d × d matrix-valued stochastic process (Πt) T t=0 specifying the mutual bid and ask prices between d assets. We introduce the notion of “robust no arbitrage”, which is a version of the no arbitrage concept, robust with respect to small changes of the bid ask spreads of (Πt) T t=0. The main theorem states that the bid-ask process (Πt) T t=0 satisfies the robust no arbitrage condition iff it admits a strictly consistent pricing system. This result extends the theorems of Harrison-Pliska and Kabanov-Stricker pertaining to the case of finite Ω, as well as the theorem of Dalang-Morton-Willinger and Kabanov-Rásonyi-Stricker, pertaining to the case of general Ω. An example of a 5 × 5-dimensional process (Πt) 2 t=0 shows that, in this theorem, the robust no arbitrage condition cannot be replaced by the so-called strict no arbitrage condition, thus answering negatively a question raised by Kabanov, Rásonyi and Stricker.

Abstract not found

Consider the postage stamp. As title to a future good (or, in this case, service) with monetary value, this humble object is essentially the same as a security. Its value, 37 cents, can be identiÞed with the present value of the service (delivery of a letter) to which its owner is entitled.

California. The views expressed herein are those of the author and not necessarily those of the National

This concludes a two-part commentary on management science and e-business, the theme of this two-part special issue. After reviewing the topical clusters that give organization to both parts, we sketch the papers appearing in this second part from the perspective of two key questions concerning the impact of the emerging digital economy on management science research: What fundamentally new research questions arise, and what kind of research enables progress on them. We then offer summary comments on the second question based on the papers in both parts. The principal conclusions are that, in meeting the challenges posed by the digital economy, management science researchers are (a) making greater use of parts of economics and computer science/information technology, and (b) exploiting the improving productivity advantages of empirical and methodological work in comparison with theoretical work.

In this paper, I present a decision-making process that incorporates a Genetic Algorithm (GA) into a state dependent dynamic portfolio optimization system. A GA is a probabilistic search approach and thus can serve as a stochastic problem solving technique. A Genetic Algorithm solves the model by forward-looking and backward-induction, which incorporates both historical information and future uncertainty when estimating the asset returns. It significantly improves the accuracy of expected return estimation and thus improves the overall portfolio efficiency over the classical mean-variance method. In addition a GA could handle a large variety of future uncertainties, which overcome the computational difficulties in the traditional Bayesian approach.

and suggestions. Carlos Pereira provided able research assistance. The usual disclaimer applies.

Wen Li Cheng, and Marsha Yamshikova, but they are in no way responsible for any remaining errors or ambiguities. We are also grateful to Richard Laden for helpful preliminary discussions. A Child's Guide to Real Options Our consulting experiences indicate that many New Zealand businesses and their managers are increasingly aware of the shortcomings of conventional methods for evaluating capital investment projects, particularly in situations where there is considerable flexibility subsequent to the project's commencement. With the busy executive in mind, we offer a brief and intuitive introduction to recently developed methods of project evaluation that explicitly incorporate managerial flexibility. 1

In this paper, we present an easy-to-apply option-theoretical approach to quantifying liquidity spreads of bonds. We model illiquidity in the spirit of Longstaff (1995) who describes the value of liquidity as that of an exotic option. We extend this model in two directions: First, we introduce interest rate uncertainty of the extended Vasicek type to model the dynamics of zero bonds. Second, we allow for an arbitrary distribution of trading dates rather than one single non-trading period. This results in liquidity spreads arising from the values of both continuously and discretely monitored lookback options written on a zero bond. The liquidity spreads show several meaningful and plausible properties; they are humped-shaped functions of the maturity and increase with the interest rate volatility. Furthermore, the liquidity spreads are not only influenced by the number of possible trading dates, but also by their distribution over time. In contrast to the total value of illiquid zero bonds, the theoretical liquidity spreads are independent of the short rate level. When we regard German Jumbo Pfandbrief market data, we find several parallels

This paper argues that the introduction of a short-sale constraint in the Arrow-Radner framework invalidates standard definitions of complete and incomplete markets. In this constrained set-up, two threshold values with familiar properties arise. The case of a zero short-sale bound set on some security fulfills the standard definition of “incomplete ” financial markets. Beyond a particular level of the short-sale bound financial markets are “complete”, since the short-sale constraint is not active. For intermediate bounds the distinction between complete and incomplete financial markets is blurred. Although some technical definitions hold, agents can not fully transfer wealth among states. These intermediate cases, called “technically incomplete markets”, exhibit interesting welfare properties. For instance, the resulting equilibrium allocations may not be Pareto dominated by those of the non-restricted complete markets equilibrium.