How much will your retirement savings be worth when you turn 65? A simple spreadsheet model may provide a number, but how accurate is that number? The model will likely be driven by the growth rate assumptions used for equities, bonds, real estate, and cash. And don’t forget to factor in the effects of inflation on your savings. How do you accurately predict these key economic indices and thereby accurately forecast your nest egg?
Actuaries face similar challenges every day when calculating requirements for pension funds, life insurance, or long-term care. A recent research study by three noted authorities in actuarial practice provides a model for projecting economic indices such as interest rates, equity price levels, inflation rates, unemployment rates, and real estate price levels. That model is now available on two actuarial society websites. The researchers’ goal was not simply to create a model, but to help bring practicing actuaries up to date on current thinking and techniques for economic modeling and to lay the foundation for future advances.
Combining their expertise in actuarial science, finance, and mathematics, Kevin Ahlgrim, Illinois State University, and Stephen D’Arcy and Richard Gorvett, both of University of Illinois Champaign-Urbana collaborated on the research and the model, which were sponsored by the Society of Actuaries and the Casualty Actuary Society. First, they made a broad sweep through the academic literature on the relationships among economic variables, particularly relating to interest rates, inflation and equity returns, and provided summaries of that information on the societies’ web sites. Next they surveyed the state of the art of modeling in the insurance industry to identify best practices and ways in which they might advance those practices.
The model provides an integrated framework for sampling future financial scenarios that represent a reasonable approximation of historical values (see graph). It produces output values for interest rates, inflation, stock and real estate returns, dividends, and unemployment. The model was created using Microsoft Excel and Palisade’s @RISK. @RISK’s built-in probability distribution functions, correlation matrices, and simulation results were essential to the study. The model will prove useful for a variety of actuarial applications, including dynamic financial analysis (DFA), dynamic financial condition analysis, pricing embedded options in insurance contracts, solvency testing, and operational planning. According to Stephen D’Arcy, the insurance industry “has been using DFA for a number of years now to come up with aggregate projections of financial results combining the underwriting and investment sides of the business. Many actuarial firms have developed their own, proprietary DFA models. The difference between the existing models and the one we developed for the societies is that our model is publicly accessible.”
In spinning out these scenarios, the @RISK model is able to correlate such variables as the performance of stocks and bonds, the housing market, and natural disasters with interest rates, inflation, and unemployment. Capturing the interplay among these variables creates a far more accurate model. D’Arcy says the insurance industry benefits since “unforeseen events can create havoc with insurance rates, and better modeling tools will result in better prepared insurance companies and more consistent pricing for insurance buyers.”
The report, entitled Modeling of Economic Series Coordinated with Interest Rate Scenarios, explains why the team decided to use @RISK. In its overview of the software, the report says, “@RISK leverages the simplicity of spreadsheets and integrates powerful analysis tools that help to randomly generate future scenarios to allow the examination of risk in a stochastic environment.” The report continues, “@RISK allows input variables to have explicit, user defined distributions and can easily capture the correlation between dependent variables. Additionally, a significant benefit of @RISK is the ability to capture, study, and report simulation results.”
Is a Monte Carlo simulation tool like @RISK required for the projection of future economic scenarios such as insurance policy pricing, retirement planning, and investment asset allocation? The team believed that “leveraging an existing and widely available simulation spreadsheet package is the most effective approach.” States Ahlgrim: “From our perspective, @RISK saved time and resources, especially during the model’s development. The interface through Excel allowed us to use a tool with which actuaries are very comfortable and introduce stochastic modeling procedures without resorting to programming. Writing code to simulate financial processes, incorporate correlations, and capture output would have been a formidable task. @RISK allowed us to develop these features quickly and easily.” The report continues, “We sincerely believe that any organization truly interested in generating economic and financial scenarios should commit the resources to purchasing and understanding such a package.”
The report, supporting presentations, and model are available from the Casualty Actuary Society website or the Society of Actuaries website. The model is free and can be used by any interested party. The Society of Actuaries is an educational, research, and professional organization dedicated to serving the public and SOA members. The SOA is made up of over 16,000 actuaries working in life insurance, retirement systems, health benefit systems, and financial and investment management. The Casualty Actuarial Society (CAS) is a professional society with the purpose of advancing the body of knowledge of actuarial science applied to property, casualty, and similar risk exposures. The CAS has over 4,000 members.
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» Society of Actuaries
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Coordinated with Interest Rate Scenarios
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