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Recent years have seen explosive growth in the use of enterprise pricing software. Pricing software contributes to business returns by improving pricing decision-making, and by providing monitoring and process control for pricing processes across the corporation. This software has developed in a number of significant ways over the past decade and continues to evolve in terms of sophistication and ability to contribute to both top- and bottom-line growth. In this paper, the authors present a brief historical context for the role that pricing software fulfills in a typical B2B corporation, and a set of predictions of future capabilities based on emerging trends.

How do stock prices evolve over time? The standard assumption of geometric Brownian motion, questionable as it has been right along, is even more doubtful in light of the recent stock market crash and the subsequent prices of U.S. index options. With the development of rich and deep markets in these options, it is now possible to use options prices to make inferences about the risk-neutral stochastic process governing the underlying index. We compare the ability of models including Black–Scholes, naïve volatility smile predictions of traders, constant elasticity of variance, displaced diffusion, jump diffusion, stochastic volatility, and implied binomial trees to explain otherwise identical observed option prices that differ by strike prices, times-to-expiration, or times. The latter amounts to examining predictions of future implied volatilities.

Certain naïve predictive models used by traders seem to perform best, although some academic models are not far behind. We find that the better-performing models all incorporate the negative correlation between index level and volatility. Further improvements to the models seem to require predicting the future at-the-money implied volatility. However, an “efficient markets result” makes these forecasts difficult, and improvements to the option-pricing models might then be limited.

We examine the impact of the unique Japanese stock market microstructure on the pricing of stock index futures contracts. We use intraday transactions data for the Nikkei 225 Futures contracts in Osaka and the corresponding Nikkei 225 Index in Tokyo. Incorporating more realistic transaction-cost estimates and various institutional impediments in Japan, we find that the time-varying liquidity of some component shares of the index in Tokyo represents the most critical impediment to intraday arbitrage and often causes futures prices in Osaka to deviate significantly and persistently from their no-arbitrage boundary, especially for longer-lived contracts.

This chapter focuses on the common occurrence of wholesale electricity prices that fall below the cost of production. This “negative pricing” in effect represents payment to high-volume consumers for taking excess power off the grid, thus relieving overload. Occurrences of negative pricing have been observed since the wholesale electricity markets have been operating, and occur during periods of low demand, while generators are being kept in reserve for rapid engagement when demand increases (it is expensive and time-consuming to shut down generators and then restart them, so they are often kept in “spooling mode”). In such situations power production may temporarily exceed demand, potentially overloading the system. When the federal government began subsidizing the construction of wind generation projects, with regulations in place requiring transmission grids to accept all of the electricity produced by the wind generators, negative pricing became more frequent.

This chapter develops a no-arbitrage, futures equilibrium cost-of-carry model to demonstrate that the existence of cointegration between spot and futures prices in the New York Mercantile Exchange (NYMEX) crude oil market depends crucially on the time-series properties of the underlying model. In marked contrast to previous studies, the futures equilibrium model utilizes information contained in both the quality delivery option and convenience yield as a timing delivery option in the NYMEX contract. Econometric tests of the speculative efficiency hypothesis (also termed the “unbiasedness hypothesis”) are developed and common tests of this hypothesis examined. The empirical results overwhelming support the hypotheses that the NYMEX future price is an unbiased predictor of future spot prices and that no-arbitrage opportunities are available. The results also demonstrate why common tests of the speculative efficiency hypothesis and simple arbitrage models often reject one or both of these hypotheses.

Analysis of Information Options offers new tools for evaluating investments in research, mineral exploration, logistics, energy transmission, and other information operations. With Information Options, the underlying assets are information assets and the rules governing exercise are based on the realities of the information realm (infosphere). Information Options can be modeled as options to “purchase” information assets by paying the cost of the information operations involved. Information Options arise at several stages of value creation. The initial stage involves observation of physical phenomena with accompanying data capture. The next refinement is to organize the data into structured databases. Then bits of information are selected from storage and synthesized into an information product (such as a management report). Next, the information product is presented to the user via an efficient interface that does not require the user to be a field expert. Information Options are similar in concept to real options but substantially different in their details, since real options have physical objects as the underlying assets and the rules governing exercise are based on the realities of the physical world. Also, while exercising a financial option typically kills the option, Information Options may include multiple exercises. Information Options may involve high volatility or jump processes as well, further enhancing their value. This chapter extends several important real option applications into the information realm, including jump process models and models for valuing options to synthesize any of n information items into any of m output assets.

Option models have provided insight into the value of flexibility to switch from one state to another (such as switching a mine or refinery from operating to closed status). More complex flexible processes offer multiple possibilities for switching states. A fabrication facility, for example, may offer options to shift from the current status to any of several alternatives (reflecting reconfiguration of basic facilities to accommodate different operating processes with different outputs). New algorithms enable practical application of complex option pricing models to flexible facilities, improving analysts’ ability to draw sound conclusions about the effects of flexibility and innovativeness on share value. Such models also apply for options with information items as the underlying assets. Information organizations such as oil exploration and development companies may include options to shift from the current capability to any of several alternatives reflecting added abilities to handle new information sources or apply the organization’s talents in new ways. In the case of either physical or information processing, careful attention to estimating the matrix of correlations among the values of potential alternative states allows explicit integration of financial analysis and strategic analysis – especially the influence of substitutes and the anticipated reactions of competitors, suppliers, and potential new entrants.