*Assistant Professor, S. K. Patel Institute of Management and Computer Studies Gandhinagar, Gujarat, India
**Professor and Director, Marwadi Education Foundation Group of Institutions, Rajkot, Gauridad – 360003, Rajkot, Gujarat, India
Online published on 7 September, 2012.
One of the main tasks facing managers is resource allocation. The allocation task is both a strategic and a financial task; neglecting the financial aspects of the decision might divert the manager from his ultimate goal of creating wealth. Financial practice includes several tools for allocating capital to investments.
Estimating underlying asset volatility is one of the most important problems faced by practitioners wanting to use real options models. Sometimes, the only significant source of uncertainty for the project is the price of a commodity and, in such cases, market data can be used to estimate volatility. However, most projects contain multiple sources of uncertainty, and historical data do not exist for some significant sources of volatility. For such projects, it may be useful to estimate the volatility for the project without options, and use the project without options as the underlying asset for the analysis.
Copeland and Antikarov (2001) have proposed a Monte Carlo method for estimating project volatility. Based on this method, this paper looks into the issues involved in estimating volatility of projects and their impact on the final expected outcome. This analysis will lead to the understanding of the importance to be given to simulation for assessing future business conditions and generating an outcome, according to the expectations of the project manager and his team.
Monte Carlo Simulation, Project Volatility, Resource Allocation, Expected Outcome
