An IMF working paper this month contemplates the effectiveness of lending arrangements the institution makes to various countries around the world. The paper, “Assessing IMF Lending: A Model of Sample Selection,” seeks to identify and understand common factors that make programs effective – or not. Using Monte Carlo simulation to assess the impacts of various global shocks, the authors “suggest that higher external financing needs, larger exchange rate depreciation, lower GDP growth, as well as deteriorated global financial conditions, are associated with larger individual IMF arrangement sizes,” and that “the distribution of potential aggregate IMF lending exhibits a substantial right tail.” The analysis will serve as a basis for broader IMF lending policy discussions.
The paper touches upon a very common application of Monte Carlo simulation – that of stress testing. Stress testing, as defined by Investopedia, is: “…a computer simulation technique used to test the resilience of institutions and investment portfolios against possible future financial situations. Such testing is customarily used by the financial industry to help gauge investment risk and the adequacy of assets, as well as to help evaluate internal processes and controls. In recent years, regulators have also required financial institutions to carry out stress tests to ensure their capital holdings and other assets are adequate.”
In the IMF’s case, the authors are examining whether their lending programs are sufficient to enable recipient countries to withstand various financial shocks. After the global financial crisis, the Dodd-Frank Act mandated stress testing of large banks to ensure sufficient capital reserves in the case of another downturn.
Monte Carlo simulation is ideally suited to stress testing, because it enables you to see the probability of an institution (bank, insurance company, central bank) of meeting its obligations in event of certain specific events. Straightforward Monte Carlo simulation enables this by allowing you to define uncertain inputs using probability distributions – for example, a lognormal distribution to describe the Dow Jones Industrial Average. @RISK takes this one step further with its Stress Analysis feature, which lets you specify which parts of an input distribution to sample during a simulation. For instance, if you wanted to sample the bottom 5th percentile of a distribution in order to focus on a particularly negative scenario, @RISK’s Stress Analysis feature will do this automatically:
The Federal Home Loan Bank of Indianapolis has used @RISK for stress testing as part of its credit portfolio loss modeling. According to Brendan McGrath, Director of Credit Risk Analysis, Enterprise Risk Management at the FHLB, “The @RISK framework gives you robust tools to do sensitivity analysis and optimization, so our model gives us the ability to do a variety of stress testing and what-if analysis on the portfolio.” As a result of the analysis, the FHLB was able to identify key loss distributions, which allowed them to calculate Value-at-Risk. Importantly, they were also able to determine key drivers of default risk in order to reduce VaR going forward.
Interested in learning more? We can help you create your own stress testing models for a range of applications. Just contact our consulting and custom solutions team.