We offer stress testing to help financial institutions better understand balance sheet risk and more effectively deploy capital.
Why Choose Us
While capital stress testing is mandated for financial institutions with more than $10 billion of total assets, we consider many of the required elements to be best practice. We believe that modeling balance sheet risk by considering interest rate, liquidity and credit risk in a holistic way under varying macroeconomic conditions can lead to better allocations of capital.
We believe the balance sheet risks should be measured on an integrated basis because financial assets can contain limited risk in one area and substantial risk in another. For example, a 5-year subprime auto loan contains little interest rate risk because of its expected duration but contains significant credit risk. Conversely, a Federal Home Loan Bank long-term step up callable bond contains little credit risk but contains significant interest rate risk.
Our focus is on community financial institutions and our macroeconomic stress test inputs are based on the variables most closely related to their performance. We have analyzed and back tested all of the DFAST Supervisory Scenario inputs against our multi-billion dollar, multi-year, multi-organization database and have found that changes in the unemployment rate and housing prices have the most predictive power for community financial institutions.
We utilize discounted cash flow models which we run at a granular level under multiple macroeconomic forecasts. The results can be used to determine the real return of loans under different economic conditions, resulting in more effective loan pricing and profitability. In addition, by iteratively modeling changes in portfolio mix and macroeconomic conditions, we can help clients set rational, quantitative concentration limits and sub-limits.
We first perform data mining to identify concentrations in investments, loans, and deposits, recognizing that the risk can arise from different areas and can be interrelated. For example, a financial institution could have an indirect auto loan portfolio sourced from a limited number of dealers. Understanding the percentage of the portfolio arising from each dealer and their relative credit performance (FICO and delinquency) would be important in understanding and managing credit risk. As another example, a financial institution could have a concentration of residential real estate loans causing it to be vulnerable to a downturn in real estate prices in a specific geographic area. An example of interrelated risks would be an institution with a concentration of long-term residential real estate loans and a relatively large portfolio of agency mortgage backed securities. It would have credit risk from the loans, and heightened interest rate risk from the combination of the loans and the securities.
Based on the concentrations we work with our client to identify, we develop critical input assumptions. We believe the best analysis technique to be used depends on the type of loan. For example, a financial institution could analyze its commercial real estate loans by re-underwriting its largest loans based on its knowledge of the borrower and current and forecasted economic conditions. It could combine this with a historical migration analysis – how many loans with a risk rating of one migrated to lower ratings over time. Wilary Winn believes the sheer volume of residential real estate and consumer loans in a portfolio preclude loan-by-loan analyses and are best analyzed using statistical techniques. We begin with the contractual cash flows based on the attributes of the loan and adjust them for:
- Voluntary prepayments – which is called the conditional repayment rate – (“CRR”)
- Involuntary prepayments or defaults – which is called the conditional default rate – (“CDR”)
- Loss severity or loss given default – which is the loss that will be incurred – (“loss severity”)
A major advantage of this technique is that it relies on the use of the same credit indicators that financial institutions now use to underwrite loans and manage their loan portfolios, including FICO, loan term, and loan-to-value percentage.
The primary disadvantage of this technique is that most institutions have insufficient data to produce statistically valid loss estimates. We overcome this hurdle because we can combine industry-wide data with a financial institution’s specific performance. We do this by using a statistical theory called “creditability”. See our Implementing CECL white paper for more details.
Like many, we believe the most challenging part of capital stress testing is to incorporate forecasted changes in macroeconomic conditions in its estimate. We believe the use of discounted cash flow analyses and updated credit indicators results in the most reliable approach to meeting this challenge, because we can apply assumptions from the bottom up rather than being forced to use a top down assumption. For example, when we are modeling the performance of residential real estate loans, we begin with an updated combined LTV based on a recent AVM. We thus do not have to make an inference regarding the loan’s credit indicators as of the valuation date as would be required by other techniques such as vintage analysis – we know what they are. To include short-term changes in housing prices, we utilize forecasts by MSA. Longer term, we incorporate the forecasted change in national housing prices. In this way, we incorporate short-term changes with which we have more certainty with a national forecast that is driven by forecasted economic conditions and historic performance. We use these estimates to change our loss severity estimates. Our models also include a dynamic default vector that is tied to forecasted changes in housing prices. We change our rate of default based on changes to the estimated LTV given normal amortization, curtailments, and changes in housing prices. In this way, we are adjusting our loss estimates based on macroeconomic forecasts.