How will the future economic climate affect your portfolio?
It has become increasingly clear over the past months that the global economic recession is deeper and more severe than anyone had anticipated, illustrated in the graph below.
While the length and depth of the recession or severe economic slowdown will vary from country to country, the portfolios of all lenders will be affected by the implications of the downturn. What has become clear is that economic factors do play a significant role in explaining both defaults and portfolio losses, both for consumer portfolios and commercial loan portfolios.
There are a large number of economic factors which will influence the probability of default and the potential magnitude of the loss. These factors are all linked to the ability to pay, so economic variables would include at the broadest level: unemployment, disposable income, savings rates, interest rates and inflation. Of course, the particularly useful thing about these factors is that they are the outputs of standard economic forecast models, so a robust framework exists not only for producing forecasts but also consistent forecasts of these variables. An additional asset of using economic models is that it is easy to run alternative economic scenarios, which allows for more pessimistic (or optimistic) outturns to be considered.
Against this backdrop, some of the key questions that risk managers are asking are:
- “How bad will it get?”
- “Where are we in the cycle?”
- “What does the worse case scenario look like?”
- “How will my portfolio be affected?”
- “Where are the ticking bombs in my book?”
How can economics help better inform your risk management strategies during this period? Each portfolio is made up of different customers, each of which is being affected by the current economic scenario in a different way. By understanding how the economic outturn impacts these different consumer groups, risk managers will better understand how these impact their overall portfolio performance, which in turn allows them to maximise opportunity and minimise risk.
Different client segments will be affected in different ways by the economic downturn. Clearly, customers in a portfolio will have different net asset positions, with some being net savers while others are net borrowers. Each will have different sources of income, some with an unearned element and others with just earned income, while others will have social security payments. These income flows will be affected differently by the economic situation. On top of that, customers do not all have the same employment profile, as different jobs will be associated with different rates of pay increase and also different sensitivities to the economic climate.
The following schema (Figure 2) summarises an approach that has helped a number of Experian clients in estimating the impact of the economic downturn on their portfolios. The idea is that a macroeconomic model produces baseline forecasts (or alternative scenarios) for the economy in question. Changes in the historical economic data are then correlated with the changes in the probability of default (or the portfolio factor in question) to produce a model linking the two. Given that forecasts have been produced for the economic factors, these can then be used in the model to produce forecasts of probabilities of default. While this can be done at an aggregate portfolio level, a more robust approach would be to do this for each of the customer segments that make it up. The same approach can be used to model the impact of economic factors on recovery rates, or indeed on the loss given default.
It is important to validate that changes in economic factors explain changes in loss / default data. The following illustration is from a piece of work done for an Asian client, who wished to forecast the effect that the macroeconomic environment had on their total losses on their credit card portfolio. The validation step involved identifying those economic and “portfolio” factors that best explained the loss profile of the credit card account. The economic factors that were found to be important were: interest rates, household interest payment burden and the unemployment rate.
Figure 3 above indicates how well the identified relationship between the economic factors and the actual portfolio loss data correlated with the actual portfolio data itself. Forecasts of the interest rate, unemployment rate and the household interest rate burden were then used to project the portfolio loss profile forward.
Using a model based approach allows risk managers to perform portfolio stress testing using alternative economic scenarios. One particular stress test that springs to mind is the ad-hoc macroeconomic (1 in 25) stress test that is required under Basel II (Figure 4). Its aim is to examine the effects of exceptional but plausible events, which are termed to be 1 in 25 year events. Of course we are living a 1 in 75 year plus event right now, but this just illustrates the need to perform such stress tests as part of on-going good business practice above all else. These can be performed by “shocking” the macroeconomic model with a carefully constructed extreme case scenario. This then produces alternative extreme values for the economic variables in the default / loss models that have been created. These will provide extreme values for the default probabilities and the losses, as illustrated in the graph below.
The key message in all of this is that economic factors are some of the key drivers of household demand for credit, as well as the probabilities of default and loss given default. Since robust economic theory and forecasting methodologies exist for economic forecasting, these credit market, default and loss drivers can be projected into the future. Approaching the problem from a segmented portfolio level gives you a better handle of the sources of risk, and 
indicates ways to contain or manage that risk. Given the approach is based on a set of models linking economic forecasts to customer portfolio data, it is possible to change the economic assumptions i.e. Scenario analysis. The adoption of such an approach is required by the regulators as part of Basel II compliance. However, the benefits of adopting macroeconomic modelling goes well beyond this, as it enables institutions to implement proactive manage credit risk measures more effectively in adverse economic scenarios.
William Thomson
Director of Economics
Business Strategies
Experian
The article has been developed from the presentation given by William Thomson at the Decision Analytics Forum, Paris - November 2008.
| .Want to find out more about The DA Forum? |
| Visit the DA Forum event website » |
| .Related links |
| www.business-strategies.co.uk » |
Contact us for further discussions about this article