Simulation models of bank risk management

Abstract

Quantifying the impact of various economic events is essential for risk management in community banks. Interest rate shocks of either rapidly increasing or decreasing rates, in magnitudes of at least 200 basis points, is one of the more common risks modeled. Liquidity crises that impact deposits or loan demand can arise from either local or national economic events is another risk factor that regulators are requiring banks to quantify and plan for. Excel spreadsheets can be used to develop models to measure and quantify these risks. Simulation tools and what-if analysis using data table and scenario manager identify possible outcomes for differing interest rate scenarios, interest rate shocks and liquidity stresses. Data table was used for simulation of a stochastic model to produce a cumulative distribution function of two hundred results each on three different interest rate environments. Scenario manager was used to narrow the simulation to a certain set of expectations affecting the balance sheet of the bank and another set of expectations from an interest rate shock. Changes in the bank’s balance sheet resulting from three different commodity price expectations were modeled. An interest rate shock of four hundred basis points over a two year period was also modeled. These models are simple and cost effective. Once data are captured, the time required to develop and generate scenarios is manageable. The model can be used for a wide range of what-if alternatives as an individual bank may see fit. These models are adequate to meet present regulatory requirements for a community bank of smaller size that is not complex and does not possess a high risk profile.