Monte Carlo Research Paper

Monte Carlo Simulation Research paper

Mara Davis

Professor Victor Allen

Lawrence Technological University

October 1, 2017

Overview

The term Monte Carlo is typically associated with the process of modeling and simulating a system affected by randomness: Several random scenarios are generated, and relevant statistics are gathered in order to assess, e.g., the performance of a decision policy or the value of an asset. (Brandimarte, pg3) The Monte Carlo simulation is also known as the probability simulation. This simulation is a computerized mathematical technique that allows people to account for risk in quantitative analysis and decision making. (What is Monte Carlo Simulation, nd).

To understand the impact of risk and uncertainty in financial, project management, cost, and other forecasting models. The technique is used by professionals in such widely disparate fields as finance, project management, energy, manufacturing, engineering, research and development, insurance, oil & gas, transportation, and the environment. (Monte Carlo Simulation, nd)

The Monte Carlo simulation furnishes the decision-maker with a range of possible outcomes and the probabilities they will occur for any choice of action. In a Monte Carlo simulation, a random value is selected for each of the tasks, based on the range of estimates. The model is calculated based on this random value. The result of the model is recorded, and the process is repeated. This is usually done a few times with randomly selected values so that the results will be more realistic in the real world. This causes us to have many results from the model, each based on the random input values. (What is Monte Carlo Simulation, nd).

Advantages

Some advantages of the Monte Carlo Simulation include probabilistic results which are results that show not only what could happen, but how likely each outcome is. Graphical Results, because of the data a Monte Carlo simulation generates, it’s easy to create graphs of different outcomes and their chances of occurrence.  This is important for communicating findings to other stakeholders. Sensitivity Analysis, because with just a few cases, deterministic analysis makes it difficult to see which variables impact the outcome the most.  In Monte Carlo simulation, it’s easy to see which inputs had the biggest effect on bottom-line results. Scenario Analysis, because in deterministic models, it’s very difficult to model different combinations of values for different inputs to see the effects of truly different scenarios.  Using Monte Carlo simulation, analysts can see exactly which inputs had which values together when certain outcomes occurred. And lastly correlation of Inputs, because in Monte Carlo simulation, it’s possible to model interdependent relationships between input variables.  It’s important for accuracy to represent how, in reality, when some factors go up, others go up or down accordingly. (Monte Carlo Simulation, nd).

        In most Monte Carlo tools, the returns and inflation are treated as random, and they vary based on an assumed mean, standard deviation and correlation. Those inputs are defined by the user and have a considerable impact on the results of any simulation.  (Blanchett, D.,2014)

Concerns/Issues

         Monte Carlo simulation has received a lot of criticism, though not always for valid reasons. Monte Carlo simulation also has important limitations, which have restrained EPA from accepting it as a preferred risk assessment tool. The available software cannot distinguish between variability and uncertainty. Some factors, such as body weight and tap water ingestion, show well-described differences among individuals. These differences are called "variability". Other factors, such as frequency and duration of trespassing, are simply unknown. This lack of knowledge is called "uncertainty". Current Monte Carlo software treats uncertainty as if it were variability, which may produce misleading results.

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