A real-world way to manage real options

Each corporate growth project is an option, in the sense that managers face choices--push ahead or pull back--along the way. Yet many companies hesitate to apply options theory to initiatives such as R&D and geographic expansion, partly because these "real" options are highly complex. In this article, the authors make the case that the complexity of real options can be eased through the use of a binomial valuation model. Many of the problems with real-options analysis stem from the use of the Black-Scholes-Merton model, which isn't suited to real options. Binomial models, by contrast, are simpler mathematically, and you can tinker with a binomial model until it closely reflects the project you wish to value. Suppose your company is considering investing in a new plant. To use the binomial model, you must create an "event tree" to figure out the full range of possible values for the plant during the project's lifetime--next year, at the end of the design phase, upon completion. Then you work backward from the value at completion, factoring in the various investments, to determine the value of the project today. These calculations provide you with numbers for all the possible future values of the option at the various points where a decision needs to be made on whether to continue with the project. The authors also address another criticism of real options: that gaps often arise between theoretical and realized values of options of all types. Such gaps may be largely the result of managers exercising options at the wrong time. To improve the way it manages its real options, a company can look out for the decision trigger points that correspond to the nodes on a binomial decision tree. The trigger points should not only tell managers when they need to decide on exercise but also specify rules governing the exercise decisions.     

Simulation Monte Carlo methods in extended stochastic volatility models

A new technique for nonlinear state and parameter estimation of discrete time stochastic volatility models is developed. Algorithms of Gibbs sampler and simulation filters are used to construct a simulation tool that reflects both inherent model variability and parameter uncertainty. The proposed chain converges to equilibrium enabling the estimation of unobserved volatilities and unknown model parameter distributions. The estimation algorithm is illustrated using numerical examples. Copyright © 2002 John Wiley & Sons, Ltd.     

The importance of focus for generating customer value

A carefully chosen customer value proposition (CVP) is essential to create customer value. Both value creation from the customer and the corporate viewpoint gain from consistent and deliberate focus on key market segments and core competences. The result is a mutual exchange of value, thus stabilising and strengthening the competitive position in the market. Conversely, failure to maintain a consistent focus puts value creation at risk, and potentially causes one's competitive position to break down. System Theory offers a useful framework to integrate value creation from both the customer and the corporate perspective.     

AN ANALYSIS OF THE FINANCIAL MANAGEMENT TECHNIQUES CURRENTLY EMPLOYED BY LARGE U.S. CORPORATIONS

Economic conditions have placed increased importance upon rigorous financial analysis. In order to determine which analytical techniques are currently emp loyed by management, a questionnaire was sent to each fm on the May 1980, FORTUNE 500 list. The researchsought to establish a profile of the respondents' organizational structure and to identify the primary procedures used in risk assessment, working capital management, capital budgeting, and operations research modeling. The results do suggest a basic profile of the more active employers of analytical techniques. Relatively sophisticated capital budgeting procedures appear to be accepted across most industries, and many firms support their decision making with a "package" of formal tools.