What are the best practices for using financial models to evaluate interest rate changes?

1 Model type

The first step in using financial models to evaluate interest rate changes is to choose the appropriate model type for the purpose and context of the analysis. There are different types of financial models that can handle interest rate changes, such as discounted cash flow (DCF) models, net present value (NPV) models, internal rate of return (IRR) models, bond valuation models, and option pricing models. Each model has its own advantages and limitations, and you should select the one that best suits your objectives, data availability, and complexity level. For example, if you want to estimate the value of a project or a company based on its future cash flows, you might use a DCF model. If you want to compare the returns of different investment alternatives, you might use an NPV or an IRR model. If you want to value a fixed-income security or a derivative contract, you might use a bond valuation or an option pricing model.

2 Uncertainty and sensitivity

The second step in using financial models to evaluate interest rate changes is to incorporate uncertainty and sensitivity analysis into the model. Uncertainty and sensitivity analysis are techniques that help you measure and understand how the model outputs vary depending on the changes in the model inputs, such as interest rates, growth rates, discount rates, and other assumptions. By performing uncertainty and sensitivity analysis, you can identify the key drivers of the model results, assess the range of possible outcomes, and test the robustness of the model under different scenarios. For example, you can use Monte Carlo simulation to generate a distribution of possible values for the model outputs based on random variations in the model inputs. You can also use data tables, charts, or graphs to show how the model outputs change with respect to one or more input variables.