Suchergebnisse

This paper proposes an approach for solving a multifactor real options problem by approximating the underlying stochastic process with an implied binomial tree. The implied binomial tree is constructed to be consistent with simulated market information. By simulating European option prices as artificial market information, we apply the implied binomial tree method for real options valuation when the options are contingent on the value of market uncertainties that are not traded assets. Compared to the discrete approximations suggested in the current literature, this method offers a more flexible distribution assumption for project values and therefore provides an alternative approach to estimating the value of high-dimensional real options. For risk managers, it serves as a capital budgeting method for projects with managerial flexibility.

AbstractWell‐established studies in behavioral finance confirm that investors are highly influenced by subjective reference points when making economic decisions. Although the literature examines reference point formation for individual stocks based on observed price sequences, the stocks are often treated without considering market context. We extend this literature by incorporating market price information as background to the behavior experiment, and we consider both prospect theory and anticipation/disappointment‐based utility functions in a combined framework to investigate the impact of market context on reference point formation. Our article provides new evidence and insights for investment practitioners and behavioral researchers.

Modeling the dependence between uncertainties in decision and risk analyses is an important part of the problem structuring process. We focus on situations where correlated uncertainties are discrete, and extend the concept of the copula‐based approach for modeling correlated continuous uncertainties to the representation of correlated discrete uncertainties. This approach reduces the required number of probability assessments significantly compared to approaches requiring direct estimates of conditional probabilities. It also allows the use of multiple dependence measures, including product moment correlation, rank order correlation and tail dependence, and parametric families of copulas such as normal copulas, t‐copulas, and Archimedean copulas. This approach can be extended to model the dependence between discrete and continuous uncertainties in the same event tree.