Risk analysis is the systematic study of uncertainties and risks we encounter in business, engineering, public policy, and many other areas. Risk analysts seek to identify the risks faced by an institution or business unit, understand how and when they arise, and estimate the impact (financial or otherwise) of adverse outcomes. Risk managers start with risk analysis, then seek to take actions that will mitigate or hedge these risks.
Some institutions, such as banks and investment management firms, are in the business of taking risks every day. Risk analysis and management is clearly crucial for these institutions. One of the roles of risk management in these firms is to quantify the financial risks involved in each investment, trading, or other business activity, and allocate a risk budget across these activities. Banks in particular are required by their regulators to identify and quantify their risks, often computing measures such as Value at Risk (VaR), and ensure that they have adequate capital to maintain solvency should the worst (or near-worst) outcomes occur.
After you've read on this page brief overviews of quantitative risk analysis, models and simulation, Monte Carlo simulation, and simulation optimization, we invite you to start the Risk Analysis Tutorial.
Quantitative risk analysis is the practice of creating a mathematical model of a project or process that explicitly includes uncertain parameters that we cannot control, and also decision variables or parameters that we can control. A quantitative risk model calculates the impact of the uncertain parameters and the decisions we make on outcomes that we care about -- such as profit and loss, investment returns, environmental consequences, and the like. Such a model can help business decision makers and public policy makers understand the impact of uncertainty and the consequences of different decisions.
One way to learn how to deal with uncertainty is to perform an experiment. But often, it is too dangerous or expensive to perform an experiment in the "real world" -- so we resort to a model, such as a scale model of an airplane in a wind tunnel. With a model, we can simulate what would happen in the real world, and perform many experiments -- for example, subjecting our model airplane to various air currents and forces -- and learn how it behaves. We can introduce uncertainty into our experiments using devices such as a coin toss, dice roll, or roulette wheel. A single experiment that involves a coin toss may not tell us very much, but if we perform a simulation that consists of many experiments or trials, and collect statistics about the results, we can learn quite a lot.
If we have the skills and software tools needed to create a mathematical model of a project or process on a computer, we can perform a simulation with many trials in a very short time, and at very low cost. With such advantages over experiments in the real world, it's no wonder that computer-based simulation has become so popular. For business models, Microsoft Excel is an ideal tool for creating such a model -- and simulation software such as Frontline Systems' Analytic Solver Simulation can be used to get maximum insight from the model.
Named after the city in Monaco famed for its casinos and games of chance -- is a powerful mathematical method for conducting quantitative risk analysis. Monte Carlo methods rely on random sampling -- the computer-based equivalent of a coin toss, dice roll, or roulette wheel. The numbers from random sampling are "plugged into" a mathematical model and used to calculate outcomes of interest. This process is repeated many -- typically thousands of -- times. With the aid of software, we can obtain statistics and view charts and graphs of the results. After you have gone through this short risk analysis tutorial, we recommend you go through our Monte Carlo simulation tutorial.
Monte Carlo simulation is especially helpful when there are several different sources of uncertainty that interact to produce an outcome. For example, if we're dealing with uncertain market demand, competitors' pricing, and variable production and raw materials costs at the same time, it can be very difficult to estimate the impacts of these factors -- in combination -- on Net Profit. Monte Carlo simulation can quickly analyze thousands of 'what-if' scenarios, often yielding surprising insights into what can go right, what can go wrong, and what we can do about it.
Simulation Optimization goes one step further than just helping us understand risk to allow us to make better decisions taking into account that risk. We do this by building a model where for each decision choice we run a Monte Carlo simulation, record the results and then continue to test additional decisions until we reach an optimal solution. Once you become familiar with simulation and Monte Carlo simulation, you'll most likely want to learn more about simulation optimization.