Practical Portfolio Simulation: Determining the Precision of a Probability Distribution of a Large Asset Portfolio When the Underlying Project Economics are Captured by a Small Number of Discrete Scenarios

Abstract

Abstract One of the great successes of modern financial risk modelling is the application of computing technology to simulate complex continuous probability distributions associated with the value metrics of real-life assets. The Monte Carlo simulation technique is the best known example and is widely applied by economists in the E&P industry. However a Monte Carlo simulation in which project economics are aggregated into a large asset portfolio is rarely undertaken. The main reason is that present computing systems cannot handle the vast amounts of data generated in a Monte Carlo simulation of a large asset portfolio. A pragmatic solution for this issue might be to approximate the continuous distribution of feasible project outcomes using a small number of probability-weighted discrete scenarios. In a corporate portfolio simulation, a project sample would be drawn from these discrete distributions as opposed to the original continuous distributions. If either a global assumption needs revision or a single asset requires recalculating, the economics of a relatively small number of scenarios would be computed. Thus, there is no need to store or recalculate a large number of outcomes (typically more then 2,000) for each project, as would be required in a conventional Monte Carlo simulation. A prerequisite for this approach is that the frequently skewed and complex continuous probability distributions of each of the assets can precisely be described by a small number of scenarios. This study documents the level of precision that can be achieved using Swanson's rule and variations thereof. Our analyses suggest that if the P50 is at least 33% greater than the P10, the simulated portfolio mean and standard deviation are within 5% and 15% of the actual values, respectively. The approximation of the lower end of the distribution, i.e., the downside risk of a portfolio, is within 4% even for an extremely asymmetric lognormal distribution. The proposed portfolio simulation methodology addresses the largely unmet need of corporate managers to improve their understanding of key risk and value drivers and their impact on the performance of a corporate asset portfolio.