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Stress testing structured finance transactions presents unique challenges due to large and diverse portfolios of underlying assets, limitations on data availability, and the idiosyncrasies and complexities of the structures and associated risks.

Stress testing European structured finance portfolios presents a unique challenge: nowhere is tail-risk analysis more critical yet more difficult to do properly. As we have witnessed over the past decade, structured finance transactions tend to carry myriad risks, therefore requiring complicated analyses. In response, banks tend to separate structured finance securities from less esoteric asset classes, both organisationally and analytically. However, when a bank conducts stress testing, it must consistently apply stresses to all its positions regardless of asset class. This article addresses some of the challenges banks face in stressing their structured finance positions within the context of a larger enterprise-wide stress testing exercise.

An inherently involved and complex process

Looking at a structured finance portfolio as a whole can yield useful generalisations around projected performance. For example, dropping home prices are on average going to negatively affect the credit risk of RMBS tranches. However, unlike corporate bonds, for example, it is not possible to know intuitively how a change in a given macroeconomic statistic will affect a single position. Depending on the deal structure, it is possible that severe economic scenarios could improve the relative performance of some tranches and cause significant losses to others. You cannot determine the impact on structured finance tranches without running the cash flows on the underlying properties and loans and then moving those cash flows through the deal’s waterfall. And yet, running the cash flows opens up a whole new set of problems, including challenges in maintaining quality data and building the underlying asset models.

Dealing with data

Using a consistent method to stress test across asset classes implies the ability to reliably convert forecasts on a potentially large set of macroeconomic factors into performance projections on each of a bank’s positions. In the world of structured finance, this ideally means crafting projections at the underlying loan-level. Loan-level data, especially in European deals, can be frustratingly scarce, which contributes to a dearth of granular structured finance asset models. Despite concerted efforts of both regulators and the market to increase loan-level data availability in Europe, the lack of history in these newly created datasets makes robust and predictive model building difficult.

Whilst lower coverage for loan-level data makes it hard, if not sometimes impossible, to develop reliable account-level models, the paucity of data also means that any successful stress testing model must simultaneously and consistently support alternate methodologies. For example, a bank with whole loan mortgages and RMBS on its books may stress whole loans through an account-level asset model, whereas the RMBS position can only be analyzed through an aggregation model on the underlying collateral. Despite using separate models, stressed results between the whole loan and RMBS books must be consistent. Most often, missing loan-level data forces a pool-level analysis where historical performance of a given pool, its comparables, and aggregate industry and national metrics inform the projections. Mechanisms should be in place to reconcile results from the loan-level and pool-level models.

Identifying and addressing hidden risks

Complexities in structured finance models in Europe are not limited to the underlying assets. Because of the preponderance of cross-currency transactions, currency swaps are common and swaps of any kind could introduce many risks, including counterparty risk. In so-called normal economic environments, counterparty risk can be overshadowed by credit risk and extension risk, as two examples, but it strongly came to the forefront during the credit crisis when protective swaps failed to deliver in times of need. Indeed, counterparty risk tends to become problematic in particularly difficult economic environments, or tail-risk scenarios, which are precisely what stress testing is designed to address. Properly tracking counterparty risk within the context of structured finance securities is especially challenging given the lack of unique identifiers and standard reporting templates for derivative transactions in securitizations. Investors need to scour performance reports and deal with documents carefully to understand their counterparty exposure.

Loan-level data, especially in European deals, can be frustratingly scarce, which contributes to a dearth of granular structured finance asset models.

Developing an industrial-strength, scalable platform

Even if a given bank has access to a model for stress testing that features consistent implementation of structured finance analysis, that bank cannot simply run the stress test once and move on. Stress testing is meant to be an ongoing process and, therefore, any competent stress testing solution must be streamlined and user-friendly. Furthermore, the platform must be extensible and diligently supported in order for the bank to keep up with the ever-changing regulatory environment. In cases where some banks hold thousands of structured finance positions, building an efficient technology infrastructure to run a variety of stress tests in a consistent and timely manner is a challenge that must be addressed.

Stress testing with a mixed portfolio that includes structured finance securities can be a daunting task. From complicated legal structures and non-standard reporting of underlying collateral to properly incorporating macroeconomic factors, some banks may struggle to convince regulators that their structured finance testing is up to the same standards as the stress testing on their more vanilla positions. This is why it is critical to leverage a platform that provides cohesion across asset classes, strong fundamental analysis, consistent assumptions, model design, and ongoing support. Consistency across all portfolio assets is an imperative to stress testing best practices.

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