Stochastic Discounting and Simulation a Capital Budgeting Model Applied in the Greek Banking Industry
Author | : Panayiotis G. Artikis |
Publisher | : |
Total Pages | : 353 |
Release | : 1995 |
Genre | : |
ISBN | : |
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The main purpose of the present research is to develop a capital budgeting stochastic simulation model for quantifying the risk and uncertainty inherent in the establishment of a new branch in Greece by a larqe multinational bank. In examining critically the existing investment evaluation methods the risk simulation approach proved a valid tool for risk and uncertainty analysis, mainly for its strength in quantifying the risk and uncertainty and its applicability to real life situations. The investigation of the Greek banking system, the environment the sample bank operates in, showed the emergence of an increasing number of profitable opportunities over the last years associated, however, with a larger degree of risk. The development of the research model, expresses the key variables of the investment project in the form of mathematical equations showing both all kind of relationships and interdependencies that exist among certain variables, and the way each variable affects the profitability criterion that is used to evaluate the investment project. Subjective probability distributions are used as a means of data inputs. The computer simulation program performs a repeatedly discounted cash flow computation with the values of the random variables being modified between iterations in accordance with their associated subjective probability distributions. The output of the simulation program is a probability distribution of the NPV and the IRR associating each possible outcome with the probability of its occurrence. The statistical analysis of the output of the simulation allows the management of the sample bank to discriminate among measures of expected return based on their probability of occurrence. Moreover, it provides a measure of the maximum risk they would be willing to accept. Finally, the superiority of the information obtained from the risk simulation approach is illustrated numerically, by comparing the output of the computer simulation program with the results produced by the method the sample bank is currently employinq to evaluate investment proposals.