Stress Testing

Risk Integrated Releases Quarterly CCAR Model for Commercial Real Estate


Risk Integrated Releases Quarterly CCAR Model for Commercial Real Estate

New York / London – May 19, 2016 – Risk Integrated, the leading international risk solution provider for commercial real estate, today announced the release of a quarterly cashflow simulation model (CFM) specifically tailored to the requirements of the US regulatory Comprehensive Capital Analysis and Review (CCAR) stress tests. In addition to breaking the risk into granular, quarterly time steps, this new model requires less input data and is easier to use and maintain.

The CCAR CFM is built on the same foundation as the comprehensive fully detailed CFM that clients are currently using in the Specialized Finance System (SFS) for grading and deal structuring as well as Basel III and Solvency II regulatory compliance. The full model takes into account the many features, such as covenants, lease terms, etc., which can change the risk of a commercial real estate financing, and are especially useful when structuring new credits. By building on the same foundation, the new CCAR CFM model leverages the proven track record of the full model (see Validation of the Specialized Finance System). However, the CCAR CFM has been optimized and greatly simplified such that it only requires data which is available from the CCAR dataset (the FR Y-14Q data).

At a portfolio level, the new model gives very similar results to the full model. The graph below illustrates a portfolio's cumulative loss, quarter by quarter, for the CCAR stress, comparing the quarterly losses with the average losses from annual time steps.

At this time there is increased regulatory pressure for institutions to only use capital models that they control and understand in full detail - i.e., not to [...]

May 19th, 2016|

2015 CCAR Results for CRE

Risk Integrated, the leading provider of client-specific risk systems for CRE, has run its Specialized Finance System (SFS) with the 2015 CCAR scenarios published by the Federal Reserve Board (FRB). The analysis shows the base, adverse and severely adverse losses for a standard US bank’s portfolio of commercial real estate loans.

The key results are the quarterly expected losses for the portfolio as illustrated below:

Figure 1. Quarterly Expected Loss

Table 1. 9-Quarter Cumulative Loss Rates

Loss estimates are shown under four different sets of assumptions: the unconditional base case, the conditional base case, the conditional adverse scenario and the conditional severely adverse scenario. The unconditional base case takes the FRB’s base-case to be the central tendency for the economy, but still allows for variations in economic and market conditions. The conditional case takes the economic scenarios to be fixed, i.e., the conditional base-case gives the losses conditional on it being known that the overall economy will be benign.

The SFS estimates these results using a transparent detailed cashflow simulation model. The model projects the debt servicing cascade and collateral values for the individual deals in thousands of alternative economic and market scenarios conditioned by the central forecasts and the historical volatility structures. These market conditions interact with idiosyncratic risks such as tenant defaults and re-leasing to determine whether the loan will default and if so, what will be the subsequent loss (if any). The models are completely transparent and can by customized by each client to match their markets, deal structures, available data and experience. In this case the limited data set of the FRY-14Q is used, but the model uses more detailed information per asset if it is also being [...]

December 11th, 2014|

Allocating CRE Risk Statistics to Quarterly Time Steps

Exercises such as CCAR are asking for loss forecasts to be allocated to individual quarters. If the requirement is to fully characterize the quarterly risk for individual loans, it is necessary to use risk models that are built using quarterly time steps. However, by their nature, CRE loan losses respond more slowly to economic conditions than other types of loans and therefore at the aggregate portfolio-level the difference between quarterly and annual pictures is less marked than for other assets. This means that at the portfolio level, there are several effective approaches for allocating annual estimates of risk to individual quarters. This paper illustrates three example approaches.

As a base-line, consider the simple algorithm of dividing the annual loss by four and making the "flat" assumption that all the quarters within the year have the same loss. Figure 1 illustrates this for US Charge-offs from 1993 to 2013 and compares the actual quarterly data with the annual flat averages. By definition the annual averages track the overall trend, but there are some quarters where the loss spikes significantly away from the annual average. This difference is a combination of actual response to quarterly economic conditions, plus some idiosyncratic randomness (e.g., weather, a particular government statement, or the idiosyncratic risk of the individual loans).

Figure 1. US Quarterly CRE Charge-off rates compared with the Flat Annual Average

As alternatives to this flat allocation, three methods are illustrated in this paper:

Smoothed Allocation
Difference of Models
Model of Differences

Smoothed Allocation

Smoothed allocation takes the flat allocation for each quarter and then calculates the exponentially weighted running average to define a new quarterly loss. This has the effect of taking the "edges" off the flat [...]

October 28th, 2014|

Linking Market & Credit Stresses to Economic Stresses

This paper describes a mathematically rigorous approach for defining market and credit stresses given a set of economic stresses. This approach is directly applicable to regulatory stress-test reporting.
Recent regulations such as CCAR and Basel III have adapted the long-standing risk measurement approach of stress-testing and made it applicable to new regulatory purposes. The current regulations require institutions to estimate losses in a base-case and in a stressed condition in which the condition is defined by stresses on a selection of macro-economic variables such as GDP and interest rates. A very direct estimate of the risk can then be obtained by using risk models that include economic and market conditions, for example regressions on historical default data where market conditions are included in the regressors1, or cashflow simulation where the cashflows are conditional on factors such as vacancy and rental rates. However, these direct approaches require the estimation of the remaining market conditions or credit factors that are not already specified in the regulatory stress.
As an example, consider the task of assessing the increased risk of loans to commercial offices in Pittsburgh, conditional on a set of regulatory stresses. Intuitively we know that if macro factors such as GDP are depressed, then market factors such as rents and vacancy rates will also tend to be worse. However, for any given level of GDP, the exact change in the market factors will be uncertain, e.g., if GDP was down 4%, rents might fall 10% on some occasions or 25% on others. More formally, defining the overall economic conditions removes much of the systemic risk, but leaves idiosyncratic risk[2. Here [...]

  1. See Risk Integrated’s paper: “Linking Stress Tests to the Real Economy"
October 24th, 2013|

Stress Testing Risk Projections for Commercial Real Estate

All Publications >> Stress Testing

The largest U.S. CRE portfolio lenders are now gathering a standardized set of data for all significant CRE loans. The set of data items is somewhat limited but has the great advantages of standardization and availability. Risk Integrated can use the SFS to extract maximum value from this data set by providing rigorous portfolio risk projections. The document illustrates the risk results Risk Integrated can return to the bank within a very short time.

Please contact us for the full set of sample projections.

November 28th, 2011|

Detailed Review of Purpose and Procedures for Stress Testing CRE Portfolios

All Publications >> Stress Testing

In a full presentation, Risk Integrated's Dr. Chris Marrison explains in detail the objectives of stress testing the commercial real estate portfolio. He discusses the shortcomings of the current prevalent methods such as adapting C&I stress testing models for use with very different CRE loans. In conclusion, full cashflow simulation is shown to be the most flexible, durable approach.

Please contact us Risk Integrated for a full copy.

October 23rd, 2011|

Risk Integrated Launches New Stress Test Platform

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Risk Integrated released enhancements to its Specialized Finance System, which is a web-based risk measurement and reporting platform that allows financial institutions to run stress tests easily and securely. CTO Yusuf Jafry clarifies that the SFS will now better enable risk analysts to prototype their models in Excel, but control and audit them in a robust, scalable system.

February 11th, 2010|

Risk Integrated Helps Financial Institutions Implement Fast, Accurate Portfolio Stress Testing

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Improvements to a facet of Risk Integrated's Specialized Finance System were launched today by Risk Integrated to help risk and portfolio managers stress portfolios of highly varied asset types, including retail and corporate lending and mortgages, CRE, equity and securitizations.

December 7th, 2009|

Linking Stress Tests to the Real Economy

All Publications >> Stress Testing

In a whitepaper to appear in the financial press this autumn, Risk Integrated CEO, Chris Marrison, discusses a method for adding real world structural elements to risk models for almost any asset class. His approach is to adjust an asset's probability of default by using the ratio of expected net income in nominal and stressed cases.

September 25th, 2009|

Stress Testing for Risk Management in Commercial Real Estate

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Real Estate Weekly featured an article today by Risk Integrated CEO Chris Marrison who cautions against overcounting your risk in the commercial real estate portfolio. A rich data environment is discussed as the solution to the problem.

April 8th, 2009|