Constructing Scenarios—Use of Macroeconomic Models
Once the key questions or main vulnerabilities of interest have been identified, the next step is to construct a scenario that will form the basis of the stress test. This phase of the process involves an examination of the available data and models that can be used to understand the behavior of the system with respect to the main vulnerabilities. Using those data, one can construct a scenario in the context of some overall macroeconomic framework or model, depending on the complexity of the system and the availability of a suitable model.
The objective of using an explicit macroeconomic model is to link a particular set of shocks to key macrovariables and financial variables in a consistent and forward-looking framework. The use of a macroframework does not necessarily require a large research effort, but it can leverage existing expertise and research. The key reason for using this approach is to bring the discipline and consistency of an empirically based model and an explicit focus on the link between the macroeconomy and the main vulnerabilities.
Drawing on the main macroeconomic vulnerabilities, the analyst should arrive at a consensus for the key macrovariables and financial variables that are the most volatile, misaligned, or likely to have the greatest effect on the financial system. Typically, such misaligned variables are susceptible to major shocks or realignments and, thus, can form the basis of a realistic simulation scenario. Depending on the structure and features of the macromodel that is available, the simulation can produce a range of economic and financial variables as outputs.
Here are three illustrative examples of the process of developing a scenario:
• Example 1: Suppose that housing prices had risen sharply on the strength of rapid employment growth, rising household disposable incomes, and low interest rates, thereby fuelling a mortgage-lending boom. An analysis of bank balance sheets and income statements shows a strong dependence on mortgage lending both in the stock of assets and in the flow of income. A possible scenario could involve a rise in unemployment, a fall in disposable incomes, and a sharp rise in interest rates affecting the debt servicing capacity of households. The outputs from a macromodel could provide a range of information on employment, real incomes, prices, and interest rates, which could be used to formulate a specific stress test for bank balance sheets.
• Example 2: Suppose that the macrolevel analysis indicated an overvalued exchange rate caused by strong capital inflows with associated credit growth financing a surge in construction investment. An analysis of structural data on institutional balance sheets and income statements reveals a sharp increase in exposure to foreign-currency-denominated real estate loans, and microlevel indicators of FSIs. Individual balance-sheet information shows rising defaults on property loans. One scenario
might include a sudden reversal of capital flows and a rapid depreciation of the exchange rate. Macrosimulations of this scenario could produce a range of outputs, including real gross domestic product (GDP) growth, price level, interest rates, and exchange rate. Those outputs could then form the basis of a stress test of balance sheets for individual institutions.
• Example 3: Suppose that financial deregulation and low interest rates, together with strong wage and economic growth, have fuelled a sharp rise in consumer (nonmortgage) lending. An analysis of balance sheets and income statements reveals banks and nonbanks now earn more than a quarter of their income from this lending, with exposures (and credit extended to consumers) growing rapidly. Furthermore, nonbanks are funding their lending largely through commercial paper placements. Although FSIs show only modest rises in delinquency rates and nonperforming assets, there are concerns about credit quality going forward. One possible scenario might involve a sharp rise in interest rates, increasing banks’ funding costs and (temporarily) narrowing their margins, perhaps caused by a policy response to increased inflationary pressures or an external shock. The output of a macromodel could be used to analyze the possible effect on household incomes and the debt-servicing capacity.
Ideally, a macroeconometric or simulation model should form the basis of the stresstesting scenarios. One objective of system-focused stress tests is to understand the effect of major changes in the economic environment on the financial system. Using a macromodel provides a forward-looking and internally consistent framework for analyzing key linkages between the financial system and the real economy. The feasibility of this approach will vary according to the range of modeling expertise available, as well as the type of macromodel in place. Here are some of the considerations involved in using a macromodel:3
• What are the baseline assumptions? The baseline assumption could be either no change from the latest data, or the central forecast or most likely scenario from the most recent forecasting exercise.
• What policy responses are assumed? Depending on the model, different policy reaction functions may be imbedded in the model (such as a Taylor rule relating monetary policy instrument settings to deviations in inflation and output from their targets), or an assumption of no change in policies may be used. One can assume no policy response will typically imply a larger macroeconomic effect of any shock, but this conclusion will depend on the model and scenario.
• What is the time horizon of the simulations? If a quarterly model is available, it may be possible to produce forecasts over the next six to eight quarters. When one applies the scenarios to individual balance sheets, however, a shorter time horizon is desirable if no reaction by institutions to the specific shocks is assumed (i. e., if it is assumed that institutions do not adjust their balance sheets, then the results can be interpreted as a comparative static exercise).
• Which variables are assumed to be fixed, and which are shocked? Many macroeconomic models use a large number of exogenous variables. Implementing a particular scenario requires a judgment as to which variables are assumed to be constant.
Changing a large number of exogenous variables may make the scenario unnecessarily complex with little benefit in terms of realism and less acceptance of the results by participants.
• What size of shocks should be used? Shocks either can be calibrated on historical experience (e. g., largest change over the chosen time horizon seen in the past 10 years), or can be set on the basis of a hypothetical scenario (e. g., a 20 percent fall in the exchange rate). Historical experience may be more intuitive and easier to justify, but major structural changes may invalidate historical calibration (e. g., deregulation may change fundamental economic relations).
In the absence of a macromodel, it may be necessary to rely on more rudimentary approaches. Some authorities may not have a well-developed macromodel available. Even if a model is in place, there may be difficulties in using it to simulate relevant shocks. Some models may not be tractable for the type of economic shock that the analyst wishes to consider, whereas others may not incorporate a financial sector or may not allow for a policy reaction by authorities. Thus, it may not always be feasible to generate a macroscenario using a consistent macromodel. Even in those circumstances, it is still possible to frame the analysis in the context of an internally consistent, forward-looking macroeconomic scenario by using textbook macromodels, which are supplemented by existing empirical research, or by using models developed for another country that has a similar structure.