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Impact of Mr. Cooper’s Cyber Security Incident on Agency Prepayment Reporting

Amid the fallout of the cyberattack against Mr. Cooper on October 31st was an inability on the large servicer’s part to report prepayment activity to investors.

According to Freddie Mac, the incident “resulted in [Mr. Cooper’s] shutting down certain systems as a precautionary measure. As a result, Freddie Mac did not receive loan activity reporting, which includes loan payoffs and payment corrections, from Mr. Cooper during the last few days of the reporting period related to October loan activity.”

Owing to Mr. Cooper’s size, were curious to measure what (if any) impact its missing days of reporting might have on overall agency speeds.

Not a whole lot, it turns out.

This came as little surprise given the very low prepayment environment in which we find ourselves, but we wanted to run the numbers to be sure. Here is what we found.

We do not know precisely how much reporting was missed and assumed “the last few days of the reporting period” to mean 3 days.

Assuming 3 days means that Mr. Cooper’s reported speeds of 4.5 CPR to Freddie and 4.6 CPR to Fannie likely should have been 5.2 CPR and 5.4 CPR, respectively. While these differences are relatively small for to Mr. Cooper’s portfolio (less than 1 CPR) the impact on overall Agency speeds is downright trivial — less than 0.05 CPR.

Fannie MBSFreddie MBS
Sch. Bal.195,221,550,383168,711,346,228
CPR (reported)4.64.5
CPR (estimated*)5.45.2
*assumes three days of unreported loan activity and constant daily prepayments for the month

Fannie Mae and Freddie Mac will distribute scheduled principal and interest when servicers do not report the loan activity. Prepayments that were not reported “will be distributed to MBS certificateholders on the first distribution date that follows our receipt and reconciliation of the required prepayment information from Mr. Cooper.”

Validating Vendor Models

RiskSpan validates a diverse range of models, including many that have been developed by third-party vendors. Vendor models present unique implications when it comes to model risk management (MRM). In this article, we seek to describe how we align our approach to validating these models with existing regulatory guidance and provide an explanation of what financial institutions should expect when it comes time to validate their vendor models.  

Our clients use third-party vendor models that touch on virtually every risk function. The most common ones include: 

  • Anti-money laundering (AML) solutions for Suspicious Activity Monitoring (SAM) and Customer Due Diligence (CDD). 
  • Asset-Liability Management models that simulate the whole balance sheet under different interest rate scenarios to provide analytics for interest rate risk monitoring. 
  • Structured assets and mortgage loan analytics platforms (similar to RiskSpan’s Edge Platform). 
  • Mortgage pipeline management platforms, including loan pricing, best execution determination, analytics, and trade tracking. 
  • Climate risk models that quantify the risk associated with the future effects of climate change on assets at different locations. 
  • Artificial intelligence (AI) platforms that help model developers optimize the machine learning (ML) algorithm, feature selection, and hyperparameter tuning automatically with the target performance metric. 

Vendor Models and MRM Considerations

Regardless of whether a model is fully home grown or a “black box” purchased from a third-party vendor, the same basic MRM principles apply. Banks are expected to validate their own use of vendor products [OCC 2011-12, p.15] and thus institutions should understand the specifics of vendor models that pose model risk and require considerations for validation. The following table outlines specific risks that vendor models pose, along with mitigating considerations and strategies model risk managers should consider. 

SpecificsDescriptionMRM and Validation Implications
Complexity Some vendor models offer many functionalities and sub-models dedicated to different tasks. These various models are often highly integrated into the client’s internal systems and databases. Well-crafted model documentation is important to make the validation efficient. Validation requires more time since all model functionalities and components must be mapped.
Specialized ExpertiseVendor models are often developed based on accumulated know-how of a specific field of study. Validation requires professionals with specific field of study experience and who understand the model in relation to industry standards.
Regulatory Requirements and Compliance Many models need to comply with existing regulations (ex: fair lending in credit scoring) or are implemented to ensure compliance (BSA/AML and the PATRIOT Act).Validation requires expertise in specific regulatory compliance.
Opaque design, assumptions, and imitations Vendors usually do not provide code for review and some aspects of the model may be based on proprietary research or data. Banks should require the vendor to provide developmental evidence explaining the product components, design, and intended use, to determine whether the model is appropriate for the bank’s products, exposures, and risks. They should also clearly indicate the model’s limitations and assumptions and where the product’s use may be problematic. [OCC 2011-12, pp. 15-16].
Vague or incomplete documentation from the Vendor Often in the name of protecting IP, model documentation provided by the vendor may be vague or incomplete.Banks should ensure that appropriate documentation of the third-party approach is available so that the model can be appropriately validated [OCC 2011-12, p.21].

Institutions must also develop their own internal documentation that describes the intended use of the model, lists all inputs and outputs, lists model assumptions and limitations, and summarizes all relevant information about the model provided by the vendor such as model design, methodology, etc.
Limited Model Testing Model Testing is critical in assessing whether a model is performing as intended.

However, vendors may not provide detailed results of their thorough testing of model performance, outcomes, sensitivity, assumptions appropriateness, or the results of ongoing monitoring.

Moreover, there are usually limited possibilities to perform testing by the client or the validator since many parts of the model are proprietary.
Vendors should provide appropriate testing results demonstrating that the model works as expected. Banks should expect vendors to conduct ongoing performance monitoring and outcomes analysis [OCC 2011-12, pp. 15-16]. A bank also should conduct ongoing monitoring and outcomes analysis of vendor model performance using the bank’s own outcomes [OCC 2011-12, pp. 15-16].

Validation should consist of a review of the testing results provided by the vendor and of any additional testing that is feasible and practical. This usually includes analysis of outcomes and benchmarking, sometimes also manual replication, sensitivity analysis, or stress testing. Benchmarking may, however, be limited due to the uniqueness or complexity of the model, or because proprietary data were used for development.
CustomizationOut-of-the-box solutions often need to be customized to meet the internal systems, policies, and specific intended use of a particular institution.

A bank’s customization choices should be documented and justified as part of the validation [OCC 2011-12, p.15].
External DataVendor models often rely on external input data or external data used for its development. An important part of any validation is to determine all input data sources and assess the quality, completeness, and appropriateness of the data.

OCC 2011-12, p. 16, states that banks should obtain information regarding the data used to develop the model and assess the extent to which that data is representative of the bank’s situation.

OCC 2011-12, p.6, stresses that a rigorous review is particularly important for external data and information (from a vendor or outside party), especially as they relate to new products, instruments, or activities.

Moreover, AB-2022-03, p.3, states that regulated entities should map their external dependencies to significant internal systems and processes to determine their systemic dependencies and interconnections. In particular, the regulated entities should have an inventory of key dependencies on externally sourced models, data, software, and cloud providers. This inventory should be regularly updated and reviewed by senior management and presented to the board of directors, as deemed appropriate.
Reliance on Vendor’s Support Since the access to the code and implementation details is limited for vendor models, ongoing servicing and support is necessary.Roles and responsibilities around the model should be defined and the bank’s point of contact with their vendor should not rely solely on one person. It is also critical that the bank has in-house knowledge, in case the vendor or the bank terminates the contract for any reason, or if the vendor goes out of business or otherwise ceases to support the model [OCC 2011-12, p. 16].

Validation Approach

Validation of vendor models follows the same general principles as validation of any other model. These principles are laid out in regulatory guidance. This guidance, along with general MRM principles, provides details specifically about model risk management related to vendor models and specifically addresses vendor and other third-party products. Based on these guidelines and our experience validating numerous vendor models, RiskSpan’s approach includes the following:

  • Request documents and access to:
    • internal model documentation,
    • vendor documentation and user manual,
    • implementation documentation with a description of any customizations to the model (see Customization point in the section above), 
    • performance testing conducted by the model owner or vendor,
    • vendor certifications,
    • the model interface, if applicable, to conduct independent testing. 
  • Documentation review: We review both the internal documentation and vendor documentation and assess its thoroughness and completeness. According to OCC 11-12, p.21, documentation should be sufficiently detailed so that parties unfamiliar with a model can understand how the model operates, its limitations, and its key assumptions. For internal documentation, we focus on the statement of model purpose, list of inputs and their sources, documentation of assumptions and limitation, description of outputs and their use, controls and governance, and any testing conducted internally. We also review the documentation of the customizations made to the vendor model. 
  • Conceptual soundness review: Combining information from both the internal and vendor documentation, information from the model owner, and the industry expertise of our SMEs, we assess whether the model meets the stated model purpose, as well as whether the design, underlying theory, and logic are justifiable and supportable by existing research and industry standards. We also critically assess all known model assumptions and limitations and possibly identify additional assumptions that might be hidden or limitations that were not documented.  
  • Data review: We aim to identify all data inputs, their sources, and controls related to gathering, loading, and quality of data. We also assess the quality of data by performing exploratory data analysis. Assessing development data is often not possible as the data are proprietary to the vendor. 
  • Independent testing: To supplement, update, or verify the testing performed by the vendor, we perform internal testing where applicable. Typically, different models allow different testing methods but permission to access model interfaces is often needed for validators. This is also acknowledged in OCC 11-12, p.15: External models may not allow full access to computer coding and implementation details, so the bank may have to rely more on sensitivity analysis and benchmarking. The following are the testing methods we often use to devise effective challenges for specific models in our practice:
    • AML systems for transaction monitoring and customer due diligence: manual replication for a sample of customers/alerts, exploratory data analysis, outcomes analysis  
    • Asset-Liability Management models: outcomes analysis and review of reporting, sensitivity analysis and stress testing 
    • Loan pricing models: manual replication, outcomes analysis, sensitivity analysis, stress testing, benchmarking to RS Edge 
    • Climate risk models that quantify the risk associated with the future effects of climate change on assets at different locations: Outcomes analysis, benchmarking to online services with open access such as National Risk Index, ClimateCheck, and Risk Factor. 
    • ML AI system: outcome analysis based on the performance metrics, manual replication of the final model in Python, benchmarking with the alternative algorithm. 
  • Ongoing monitoring review: As explained in the previous section, vendors are expected to conduct ongoing monitoring of their models, but banks should monitor their own outcome as well. Our review thus consists of an assessment of the client’s ongoing monitoring plan as well as the results of both the client’s and vendor’s monitoring results. When the validated model does not produce predictions or estimations such as AML models, the ongoing monitoring typically consists of periodical revalidations and data quality monitoring. 
  • Governance review: We review the client’s policies, roles, and responsibilities defined for the model. We also investigate whether a contingency plan is in place for instances when the vendor is no longer supporting the model. We also typically investigate and assess controls around the model’s access and use. 
  • Compliance review: If a model is implemented to make the institution compliant to certain regulations (BSA/AML, PATRIOT Act) or the model itself must comply to regulations, we conduct a compliance review with the assistance of subject matter experts (SMEs) who possess industry experience. This review is conducted to verify that the model and its implementation align with the regulatory requirements and standards set forth by the relevant authorities. The expertise of the SMEs helps ensure that the model effectively addresses compliance concerns and operates within the legal and ethical boundaries of the industry. 

Project Management Considerations

In order for validation projects to be successful, a strong project management discipline must be followed to ensure it is completed on schedule, within budget and meets all key stakeholder objectives. In addition to adapting our validation approach, we thus also take our project management approach into consideration. For vendor model validation projects, we specifically follow these principles: 

  • Schedule a periodical status meeting: We typically hold weekly meetings with the client’s MRM to communicate the status of the validation, align client’s expectation, discuss observations, and address any concerns. Since vendor models are often complex, these meetings also serve as a place to discuss any road blockers such as access to the model’s UI, shared folders, database, etc. 
  • Schedule a model walkthrough session with the model owner: Vendor models are often complex and the client may use only specific components/functionalities. The most efficient way to understand the big picture and the particular way the model is used proved to be a live (typically remote) session with the model owner. Asking targeted questions right at the beginning of the engagement helps us to quickly get grasp of the critical areas to focus on during the validation. 
  • Establish a communication channel with the model owner: Be it direct messages or emails sent to and forwarded by the client’s MRM, it is important to be in touch with the model owner as not every detail may be documented. 


Vendor models pose unique risks and challenges for MRM and validation. Taking additional steps to mitigate these risks is vital to ensuring a well-functioning MRM program. An effective model validation approach takes these unique considerations into account and directly applies guidelines related specifically to validation of vendor models outlined in SR 11-7 (OCC 11-12). Effectively carrying out this type of testing often requires making adjustments to the management of vendor model validation projects. 


OCC 2011-12, p.6: The data and other information used to develop a model are of critical importance; there should be rigorous assessment of data quality and relevance, and appropriate documentation. Developers should be able to demonstrate that such data and information are suitable for the model and that they are consistent with the theory behind the approach and with the chosen methodology. If data proxies are used, they should be carefully identified, justified, and documented. If data and information are not representative of the bank’s portfolio or other characteristics, or if assumptions are made to adjust the data and information, these factors should be properly tracked and analyzed so that users are aware of potential limitations. This is particularly important for external data and information (from a vendor or outside party), especially as they relate to new products, instruments, or activities. 

OCC 2011-12, p.9: All model components, including input, processing, and reporting, should be subject to validation; this applies equally to models developed in-house and to those purchased from or developed by vendors or consultants. The rigor and sophistication of validation should be commensurate with the bank’s overall use of models, the complexity and materiality of its models, and the size and complexity of the bank’s operations. 

OCC 2011-12, p.12: Many of the tests employed as part of model development should be included in ongoing monitoring and be conducted on a regular basis to incorporate additional information as it becomes available. New empirical evidence or theoretical research may suggest the need to modify or even replace original methods. Analysis of the integrity and applicability of internal and external information sources, including information provided by third-party vendors, should be performed regularly. 

A whole section in OCC 2011-12 dedicated to validation of vendor models on pp. 15-16: 

Validation of Vendor and Other Third-Party Products  

The widespread use of vendor and other third-party products—including data, parameter values, and complete models—poses unique challenges for validation and other model risk management activities because the modeling expertise is external to the user and because some components are considered proprietary. Vendor products should nevertheless be incorporated into a bank’s broader model risk management framework following the same principles as applied to in-house models, although the process may be somewhat modified. 

As a first step, banks should ensure that there are appropriate processes in place for selecting vendor models. Banks should require the vendor to provide developmental evidence explaining the product components, design, and intended use, to determine whether the model is appropriate for the bank’s products, exposures, and risks. Vendors should provide appropriate testing results that show their product works as expected. They should also clearly indicate the model’s limitations and assumptions and where use of the product may be problematic. Banks should expect vendors to conduct ongoing performance monitoring and outcomes analysis, with disclosure to their clients, and to make appropriate modifications and updates over time. Banks are expected to validate their own use of vendor products. External models may not allow full access to computer coding and implementation details, so the bank may have to rely more on sensitivity analysis and benchmarking. Vendor models are often designed to provide a range of capabilities and so may need to be customized by a bank for its particular circumstances. A bank’s customization choices should be documented and justified as part of validation. If vendors provide input data or assumptions, or use them to build models, their relevance to the bank’s situation should be investigated. Banks should obtain information regarding the data used to develop the model and assess the extent to which that data is representative of the bank’s situation. The bank also should conduct ongoing monitoring and outcomes analysis of vendor model performance using the bank’s own outcomes. Systematic procedures for validation help the bank to understand the vendor product and its capabilities, applicability, and limitations. Such detailed knowledge is necessary for basic controls of bank operations. It is also very important for the bank to have as much knowledge in-house as possible, in case the vendor or the bank terminates the contract for any reason, or the vendor is no longer in business. Banks should have contingency plans for instances when the vendor model is no longer available or cannot be supported by the vendor. 

OCC 2011-12, p.17: Policies should emphasize testing and analysis, and promote the development of targets for model accuracy, standards for acceptable levels of discrepancies, and procedures for review of and response to unacceptable discrepancies. They should include a description of the processes used to select and retain vendor models, including the people who should be involved in such decisions. 

OCC 2011-12, p.21, Documentation: For cases in which a bank uses models from a vendor or other third party, it should ensure that appropriate documentation of the third-party approach is available so that the model can be appropriately validated. 

AB 2022-03, p.3: Since the publication of AB 2013-07, FHFA has observed a wider adoption of technologies in the mortgage industry. Many of these technologies reside externally to the regulated entities and are largely outside of the regulated entities’ control. Examples of such technologies are cloud servers, vendor models, and external data used by the regulated entities as inputs for their models. Although FHFA has published guidance related to externally sourced technologies, such as AB 2018-04: Cloud Computing Risk Management (Aug. 14, 2018) and AB 2018-08: Oversight of Third-Party Provider Relationships (Sept. 28, 2018), FHFA expects the regulated entities to take a macro-prudential view of the risks posed by externally sourced data and technologies. The regulated entities should map their external dependencies to significant internal systems and processes to determine their systemic dependencies and interconnections. In particular, the regulated entities should have an inventory of key dependencies on externally sourced models, data, software, and cloud providers. This inventory should be regularly updated and reviewed by senior management and presented to the board of directors, as deemed appropriate. 

AB-2022-03, p.3: The regulated entities should map their external dependencies to significant internal systems and processes to determine their systemic dependencies and interconnections. In particular, the regulated entities should have an inventory of key dependencies on externally sourced models, data, software, and cloud providers. This inventory should be regularly updated and reviewed by senior management and presented to the board of directors, as deemed appropriate. 

AB 2022-03, p.5: When using an external vendor to complete an independent model validation, the regulated entity’s model validation group is accountable for the quality, recommendations, and opinions of any third-party review. When evaluating a third-party model validation, a regulated entity should implement model risk management policies and practices that align the vendor-completed specific standards for an independent validation with the specific standards included in AB 2013-07. 

What Do 2023 Origination Trends Mean for MSRs?

When it comes to forecasting MSR performance and valuations, much is made of the interest rate environment, and rightly so. But other loan characteristics also play a role, particularly when it comes to predicting involuntary prepayments.

So let’s take a look at what 2023 mortgage originations might be telling us.

Average credit scores, which were markedly higher than normal during the pandemic years, have returned during the first part of 2023 to averages observed during the latter half of the 2010s.

The most credible explanation for this most recent reversion to the mean is the fact that the Covid years were accompanied by an historically strong refinance market. Refis traditionally have higher FICO scores than purchase mortgages, and this is apparent in the recent trend.

Purchase markets are also associated with higher average LTV ratios than are refi markets, which accounts for their sharp rise during the same period

Consequently, in 2023, with high home prices persisting despite extremely high interest rates, new first-time homebuyers with good credit continue to be approved for loans, but with higher LTV and DTI ratios.

Between rates and home prices,​​borrowers simply need to borrow more now than they would have just a few years ago to buy a comparable house. This is reflected not just in the average DTI and LTV, but also the average loan size (below) which, unsurprisingly, is trending higher as well.

Recent large increases to the conforming loan limit are clearly also contributing to the higher average loan size.

What, then, do these origination trends mean for the MSR market?

The very high rates associated with newer originations clearly translate to higher risk of prepayments. We have seen significant spikes in actual speeds when rates have taken a leg down — even though the loans are still very new. FICO/LTV/DTI trends also potentially portend higher delinquencies down the line, which would negatively impact MSR valuations.

Nevertheless, today’s MSR trading market remains healthy, and demand is starting to catch up with the high supply as more money is being raised and put to work by investors in this space. Supply remains high due to the need for mortgage originators to monetize the value of MSR to balance out the impact from declining originations.

However, the nature of the MSR trade has evolved from the investor’s perspective. When rates were at historic lows for an extended period, the MSR trade was relatively straightforward as there was a broader secular rate play in motion. Now, however, bidders are scrutinizing available deals more closely — evaluating how speeds may differ from historical trends or from what the models would typically forecast.

These more granular reviews are necessarily beginning to focus on how much lower today’s already very low turnover speeds can actually go and the extent of lock-in effects for out-of-the-money loans at differing levels of negative refi incentive. Investors’ differing views on prepays across various pools in the market will often be the determining factor on who wins the bid.

Investor preference may also be driven by the diversity of an investor’s other holdings. Some investors are looking for steady yield on low-WAC MSRs that have very small prepayment risk while other investors are seeking the higher negative convexity risk of higher-WAC MSRs — for example, if their broader portfolio has very limited negative convexity risk.

In sum, investors have remained patient and selective — seeking opportunities that best fit their needs and preferences.

So what else do MSR holders need to focus on that may may impact MSR valuations going forward? 

The impact from changes in HPI is one key area of focus.

While year-over-year HPI remains positive nationally, servicers and other investors really need to look at housing values region by region. The real risk comes in the tails of local home price moves that are often divorced from national trends. 

For example, HPIs in Phoenix, Austin, and Boise (to name three particularly volatile MSAs) behaved quite differently from the nation as a whole as HPIs in these three areas in particular first got a boost from mass in-migration during the pandemic and have since come down to earth.

Geographic concentrations within MSR books will be a key driver of credit events. To that end, we are seeing clients beginning to examine their portfolio concentration as granularly as zipcode level. 

Declining home values will impact most MSR valuation models in two offsetting ways: slower refi speeds will result in higher MSR values, while the increase in defaults will push MSRs back downward. Of these two factors, the slower speeds typically take precedence. In today’s environment of slow speeds driven primarily by turnover, however, lower home prices are going to blunt the impact of speeds, leaving MSR values more exposed to the impact of higher defaults.

Edge: Zombie Banks

At the market highs, banks gorged themselves on assets, lending and loading their balance sheets in an era of cheap money and robust valuations. As asset prices drop, these same companies find their balance sheets functionally impaired and in some cases insolvent. They are able to stay alive with substantial help from the central bank but require ongoing support. This support and an unhealthy balance sheet preclude them from fulfilling their role in the economy.

We are describing, of course, the situation in Japan in the late 1980s and early 1990s, when banks lent freely, and companies purchased both real estate and equity at the market highs. When the central bank tightened monetary policy and the stock market tanked, many firms became distressed and had to rely on support from the central bank to stay afloat. But with sclerotic balance sheets, they were unable to thrive, leading to the “lost decade” (or two or three) of anemic growth.

While there are substantial parallels between the U.S. today and Japan of three decades ago, there are differences as well. Firstly, the U.S. has a dynamic non-bank sector that can fill typical roles of lending and financial intermediation. And second, much of the bank impairment comes from Agency MBS, which slowly, but surely, will prepay and relieve pressure on their HTM assets.

Source: The Wall Street Journal

How fast will these passthroughs pay off? It will vary greatly from bank to bank and depends on their mix of passthroughs and their loan rates relative to current market rates, what MBS traders call “refi incentive” or “moniness.” It is helpful to remember that incentive also matters to housing turnover, which is a form of mortgage prepayment. For example, a borrower with a note rate that is 100bp below prevailing rates is much more likely to move to a new house than a borrower with a note rate that is 200bp out of the money, a trait that mortgage practitioners call “lock-in”.

Source: RiskSpan’s Edge Platform

As a proxy for the aggregate bank’s balance sheet, we look at the universe of conventional and GNMA passthroughs and remove the MBS held by the Federal Reserve.1 The Fed’s most substantial purchases flowed from their balance sheet expansion during COVID, when mortgage rates were at all-time lows. Consequently, the Fed owns a skew of the MBS market. Two-thirds of the Fed’s position of 30yr MBS have a note rate of 3.25% or lower. In contrast, the market ex Fed has just under 50% of the same note rates.

Source: RiskSpan’s Edge Platform

From here, we can estimate prepayments on the remaining universe. Prepay estimates from dealers and analytics providers like RiskSpan vary, but generally fall in the 4 to 6 CPR range for out-of-the-money coupons. This, coupled with scheduled principal amortization of roughly 2-3% per annum means that for this level in rates, runoff in HTM MBS should occur around 8% per annum — slow, but not zero. After five years, approximately 1/3 of the MBS should pay off. Naturally, the pace of runoff can change as both mortgage rates and home sales change.

While the current crisis contains echoes of the Japanese zombie bank crisis of the 1990s, there are notable differences. U.S. banks may be hamstrung over the next few years, with reduced capacity to make new loans as MBS in their HTM balance sheets run off over the next few years. But they will run off — slowly but surely.

Cracking the Code for a Gender-Equal Future:  Strategies for addressing unconscious gender bias

Bias is real and we all have it – both men and women. It’s often hard to recognize, as bias is the result of cultural and societal norms that have existed for decades or more. Thus, the challenge for changing subtle behaviors requires intentional action. Earlier this week, RiskSpan co-hosted with the Structured Finance Association a lively panel discussion focused on taking intentional action towards achieving a gender-equal future.

The heart of the discussion focused on unconscious gender bias in the workplace and strategies to effect change. The group discussed the importance of intentionality to drive the mission forward and the required participation of men. Part of the dialogue addressed how women and men interact in the workplace (having lunch is completely appropriate), and the need to sometimes get comfortable being uncomfortable. The panel included a sole male representative, but the room was filled with roughly 25% men. One important take-away was that the goal of gender equality is simply unattainable without the commitment and sustained effort from both women and men, and a continued dialogue on the subject is essential. 

Further, to effect social change, economic incentives have to be sustainable and aligned with the social mission. With women graduating from college at higher rates than ever before, we are beginning to unleash half of our country’s brainpower into many fields, including business, finance and tech, that continue to be dominated by men. Women in these fields continue to face obstacles stemming from gender bias. These biases need to be addressed head-on with intentional strategies for change.

Common Gender Biases and Strategies for Change

Myth No. 1: There are plenty of women in the C-suite, so what’s the problem?

Although there has been progress with better representation of women in the C-suite, the pay gap continues to exist and is significant. Part of this relates to the fact that there are more women in C-level jobs that are considered “less risky.” These less-risky jobs include legal, compliance, or accounting, as opposed to jobs such as Chief Investment Officer, Head of Capital Markets or CEO.  

Earning potential increases not only with experience and qualification, but with a bold ambition that tends to be encouraged more often among men than women (think competitive sports). This may contribute to a more heightened fear of failure among women — the business world is no exception to that.   

Strategy for change:
Assure all members of your team, regardless of gender, that mistakes are inevitable and recoverable. What’s important is how you react to a mistake or challenge. Further, encourage women to challenge themselves – invite women to lead the next challenging project and client pitch. A favorite quote of mine serves as a reminder of this:

“I always go back to my grandmother’s advice the first time I fell and hurt myself. She said, ‘Honey, at least falling on your face is a forward movement.’ You have to be willing to be brave enough to risk falling on your face, to risk failing… Everything we do is about taking risks.”

Pat Mitchell

Myth No. 2: The workplace is (solely) a meritocracy

The fact is advancement happens not only via hard work and merit, but via personal relationships and connections. This is not to say that qualifications don’t matter – of course they do. But it is human nature to consider the people you know best, have met face-to-face, or have worked with. Others may be qualified, but if you don’t know them, you’re simply not going to consider them. This is particularly difficult for women who often expect to be recognized and rewarded for their great work. However, they are missing a big part of the equation – networking and relationship building and sponsorship.  

Strategy for change:
Find a sponsor that will advocate for you when you’re not in the room. For managers, reach out to the next generation of women and make the needed introduction to help expand her network (and invite her to coffee – it’s totally appropriate). Include women in regular networking events and create a networking practice that is naturally welcoming for women to participate in.

Myth No. 3: It is inappropriate for a man and woman to have an unchaperoned business lunch

A 2017 New York Times article reported that most people (women as well as men) thought it inappropriate for a person to have a drink with someone of the opposite sex other than their spouse.

Let’s face it — It can be awkward for a man to ask a female colleague (particularly a subordinate) to join him for coffee, lunch, or a drink. However, this significant social barrier disadvantages women and hinders their opportunity for advancement. It is virtually impossible to level the playing field if women and men can’t develop a professional relationship that includes socializing over coffee or a meal.

Business communities incorporate professional socializing to foster relationships and partnerships. This extends to advancement opportunities. You typically select the people to promote from a short list of people you know well and feel comfortable with. The people you are having lunch with are the ones who are most likely to make the list. 

Strategy for change:
Start with coffee – invite her for a 1:1 conversation. The only way to break with this social norm may be to get comfortable being uncomfortable. This means it’s ok for a woman to ask a man to coffee or lunch or visa-versa (particularly between a senior and a subordinate). Treat everyone with respect and professionalism, and never withhold an invitation simply on the basis of gender.

Myth No. 4: Women with children don’t want to travel

Managers sometimes make this assumption and withhold assignments from women that require travel – be it for a conference or a critical client engagement. Although the intention might be noble, the impact is detrimental. Doing so deprives women of the same opportunities as men to engage with important clients and others in the industry. It undermines her ability to make decisions and may adversely impact how she is viewed and valued by her peers. 

Strategy for change:
Always offer travel opportunities equally among team members regardless of gender, marital status, or motherhood/fatherhood and allow the choice to be theirs. Resist reinforcing stereotypes that consequentially keep mothers at home. 

Quantifying Mortgage Risk — Best Practices in the Wake of SVB

Much has been made of the Silicon Valley Bank saga, from the need for basic risk management (was there any, other than a trivial nod?) to the possibility of re-extending the Dodd-Frank rules to cover all banks. Rather than adding our voice to that noise, this post makes a pitch for best practices in MBS and whole loan risk, regardless of whether existing regulation covers your institution.

“Best practices” in mortgage risk is a broad term meaning different things to different people. For our purposes, it refers to using sophisticated risk management tools to quantify both first- and second-order risk of various factors. It also refers to using scenario analysis to capture projected P/L under combinations of risks, for example twists in the interest rate curve combined with spread changes and changes in implied volatility.

Before these risks can be offset using rate and option hedges, our first step is quantifying what the risks are.

In the simplest case, good risk management analysis should quantify projected P/L of a rate-sensitive mortgage or MBS position for shifts in the rate curve — not just local rate shifts of 25 to 50bp, but much larger shifts in rates. It’s helpful to remember that MBS and their underlying mortgages have embedded calls, which lead to significant changes in both projected durations and projected convexity as rates move. Running scenarios with large rate shifts can help highlight the sizable second-order risks in MBS, which are typically negative but turn positive under large enough shifts. In turn, this extended analysis highlights a non-trivial third-order rate effect in MBS.

In the following chart, we show P/L on a position of TBA passthroughs, securities similar to SVB’s held-to-maturity portfolio. We project price movements under parallel rate shifts as of January 3, 2022, which roughly corresponds to the start of the tightening cycle. For this analysis, we use RiskSpan’s prepayment and interest-rate models, which are available in the Edge interface or via overnight batch.1

In this analysis, the model projected prices of FNCL 2.0 to 3.0 within 2.5% of actual observed prices on March 8, 2023, shown by the diamonds on the chart, the Wednesday before the SVB crisis began to unfold. While not exact, this analysis illustrates the power of a straightforward rate curve to help a bank’s risk management team project actual, realized prices over very large rate moves.

In the next chart, we show a P/L chart that is duration-neutral at outset. This chart shows the losses from negative convexity,2 driven by the homeowner’s option to refinance moving from at-the-money to significantly out-of-the-money. As rates continue to rise (moving right on the chart), underperformance from convexity continues to increase, but only to a point. This is where the homeowner’s call option is offset by the natural, positive convexity of discounting. Beyond that point, MBS become mildly positively convex as the call options become less relevant.

What does this change in convexity look like? In the final chart, we show convexity at various rate shifts for a par-priced passthrough.3 This highlights convexity changes over large moves (and a non-trivial third derivative with respect to changes in rates) and underscores the importance of a quantitative approach to risk management for MBS.

From these straightforward scenarios, banks and other institutions can overlay combinations of other risk shocks, for example curve flatteners and steepeners, OAS changes, and changes in implied volatility. These mixed scenarios can quantify risk from cross-partial derivatives and inform potential hedges under multiple changes in inputs. All these simple and more complex user-defined scenarios are available in RiskSpan’s Edge platform, giving small and mid-sized banks the ability to quantify risk on high-quality MBS, which is the first fundamental in a rigorous risk management framework. Recent events have highlighted the tradeoff between cost savings generated by taking a light approach to rate risk management and the existential risk of insolvency. Yes, small and mid-sized banks can save costs while remaining within the current regulatory framework. But, as SVB has taught us, to do so can be tantamount to unwittingly betting the entire enterprise. Laying out a few basis points to ensure you’ve quantified the interest rate risk properly has never looked like a more worthwhile investment.

Duration Risk: Daily Interest Rate Risk Management and Hedging Now Indispensable

The rapid decline of Silicon Valley Bank and Signature Bank affirms the strong need for daily interest rate risk measurement and hedging. All financial institutions should have well documented management and board limits on these exposures.

Measuring risk on complex mortgage-backed securities and loan portfolios that have embedded prepayment and credit risk is challenging. RiskSpan has a one-stop risk measurement solution for all mortgage-backed securities, structured product, loan and other related assets including data management, proprietary models and risk reporting.

Our bank clients enjoy the benefit of daily risk measurement to ensure they are well-hedged in this volatile market environment.

For a limited time, under full non-disclosure, RiskSpan will offer a one-time analysis on your securities portfolio.

Please reach out if we can help your institution more fully understand the market risk in your portfolios.

There are many lessons to learn through the SVB failure. While technology (the internet) enabled the fastest run on a bank in US history, technology can also be the solution. As we just saw US Government securities are risk-free for credit but not interest rate movements. When rates rose, security prices on the balance sheet of SVB declined in lock-step. All financial institutions (of all sizes) need to act now and deploy modern tech to manage modern risks – this means managing duration risk on a daily basis. It’s no longer acceptable for banks to review this risk monthly or weekly. Solutions exists that are practical, reliable and affordable.

Are Recast Loans Skewing Agency Speeds?

In a previous blog, we highlighted large curtailments on loans, behavior that was driving a prepayment spike on some new-issue pools. Any large curtailment should also result in shortening the remaining term of the loan because the mortgage payment is nearly always “level-pay” for loans in a conventional pool. And we see that behavior for all mortgages experiencing large curtailments.

However, we noted that nearly half of these loans showed a subsequent extension of their remaining term back to where it would have been without the curtailment.1 This extension occurred anywhere between zero and sixteen months after the curtailment, with a median of one month after the large payment. We presume these maturity extensions are a loan “recast,” which is explained well in a recent FAQ from Rocket Mortgage. In summary, a recast allows the borrower to lower their monthly payment after making a curtailment above some threshold, typically at least $10,000 extra principal.

Some investors may not be aware that a recast loan may remain in the trust, especially since the terms of the loan are being changed without a buyout.2 Further, since the extension lowers the monthly payment, the trust will receive principal more slowly ex curtailment than under the original terms of the loan. This could possibly affect buyers of the pool after the curtailment and before the recast.

While the number of recast loans is small, we found it interesting that the loan terms are changed without removing the loans from the pool. We identified nearly 7,800 loans that were issued between 2021 Q4 and 2022 Q1 and had both a curtailment greater than $10,000 and a subsequent re-extension of loan term.

Of these loans, the typical time to term-recast is zero to two months, with 1% of the loans recasting a year or more after the curtailment.

Some of these loans reported multiple curtailments and recasts, with loan 9991188863 in FR QD1252 extending on three separate occasions after three large curtailments. It seems the door is always open to extension.

For loans that recast their maturities after a curtailment, 85% had extensions between 10 and 25 years.

Large curtailments are uncommon and term-recasts comprise roughly half of loans in our sample with large curtailments, so term recasts will typically have only a small effect on pool cash flows, extending the time of principal receipt ex curtailment and possibly changing borrower behavior.3 For large pools, any effect will be typically exceeded by prepayments due to turnover.

However, for some smaller pools the WAM extension due to recast is noticeable. We identified dozens of pools whose WAM extended after a recast of underlying loan(s). The table below shows just a few examples. All of these pools are comparatively small, which is to be expected since just one or two individual loan recasts can have an outsized effect on a small pool’s statistics.

Pool IDFactor DateCurrent FaceExtension (months)
FR QD76177/202220,070,7376
FR QD00061/202215,682,7755
FN CB336711/202214,839,9195
FR QD57367/202210,916,9596
FN BU05814/202210,164,0006
FR QD44926/20223,113,53216
FN BV20765/20223,165,50918
FR QD60137/20223,079,25022

Takeaways from SFVegas 2023

The most highly attended conference in recent years brought together leaders from government, capital markets, and tech institutions to discuss the current state and future of the securitization markets.

SFVegas remains the optimal environment for fostering healthy dialogue aimed at making markets more efficient and transparent by creating innovative, new solutions.  RiskSpan is delighted to be engaged in this dialogue.  

Here are our key takeaways from the conference.

Loan Innovation

Sticky inflation and high interest rates are creating a macroeconomic environment that is particularly conducive to bringing new residential mortgage products to market. Market demand for HELOCs and other second-lien products is driving innovation around these offerings and accelerating their acceptance. ARM production is growing rapidly and is at some of the highest levels in over a decade.

Product Innovation is moving forward with both consumers and investors in mind. Consumers are in search of access to better financing while investors seek new ways to participate in these markets.

Technology-Accelerated (R)evolution

Data is driving the dialogue. New scoring tools (FICO10T and Vantage Score 4.0), new ESG-related data and better disclosures are creating a much more transparent investment process

Cloud-native applications continue to make analytics processing cheaper and differentiate how investors and their counterparties seek relative value. Efficiency in data management and analytics separates winner and losers.

Accelerated adoption of AI-driven solutions will drive market operational efficiency in the coming years. The adoption and use cases are just beginning to be uncovered. 

New Investors, New Ideas

New investors are bringing fresh capital to the market with new ideas on how to maximize risk-adjusted returns. Investors backed by private equity are seeking new returns in virtually every category of structured markets: MSRs, BPLs and CLOs. Interest in these classes will only grow in the coming years as more investors seek to maximize returns in private assets.

The international investor community remains strong as global asset allocation is shifting towards the U.S. and fewer opportunities exist in overseas markets

RiskSpan sits at the intersection of all of these trends by helping structured finance investors of every type to leverage technology and data solutions that uncover market opportunities, mitigate risks and deliver new products

Great conference! Get in touch with us to learn more about how RiskSpan help clients simplify, scale, and transform their structured finance analytics!

The Curious Case of Curtailments

With more than 90% of mortgages out-of-the-money from a refinancing standpoint, the MBS market has rightly focused on activities that affect discounts, including turnover and to a much lesser extent cash-out refinancings. In this analysis we examine the source of fast speeds on new issue loans and pools.

As we dig deeper on turnover, we notice a curious behavior related to curtailments that has existed for several years but gone largely ignored in recent refi-dominated environments. Curtailment activity, especially higher-than-expected curtailments on new-production mortgages, has steadily gotten stronger in more recent vintages.

For this analysis we define a curtailment as any principal payment that is larger than the contractual monthly payment but smaller than the remaining balance of the loan, which is more typically classified as payoff due to either a refinancing or house sale. In the first graph, we show curtailment speeds for new loans with note rates that were not refinanceable on a rate/term basis.1 As you can see, the 2022 vintage shows a significant uptick in curtailments in the second month. Other recent vintages show lower but still significant early-month curtailments, whereas pre-2018 vintages show very little early curtailment activity.

Digging deeper, we separate the loans by purpose: purchase vs. refi. Curtailment speeds are significantly higher among purchase loans than among refis in the first six months, with a noticeable spike at months two and three.

Focusing on purchase loans, we notice that the behavior is most noticeable for non-first-time homebuyers (non-FTHB) and relatively absent with FTHBs. The 2022-vintage non-FTHB paid nearly 6 CPR in their second month of borrowing.

What drives this behavior? While it’s impossible to say for certain, we believe that homeowners purchasing new homes are using proceeds from the sale of the previous home to partially pay off their new loan, with the sale of the previous loan coming a month or so after the close of the first loan.

How pervasive is this behavior? We looked at purchase loans originated in 2022 where the borrower was not a first-time home buyer and noted that 0.5% of the loans account for nearly 75% of the total curtailment activity on a dollar basis. That means these comparatively high, early speeds (6 CPR and higher on some pools) are driven by a small number of loans, with that vast majority of loans showing no significant curtailments in the early months.

High-curtailment loans show large payments relative to their original balances, ranging from 5% to 85% of the unpaid balance with a median value of 25%. We found no pattern with regard to either geography or seller/servicer. Looking at mortgage note rates, 80% of these high-curtailment loans were at 3.5% or lower and only 10% of these borrowers had a positive refinancing incentive at all. Only 1.5% had incentives above 25bp, with a maximum incentive of just 47bp. These curtailments are clearly not explained by rate incentive.

The relatively rarity of these curtailments means that, while in aggregate non-FTHBs are paying nearly 6 CPR in the early months, actual results within pools may vary greatly. In the chart below, we show pool speeds for 2022-vintage majors/multi-lenders, plotted against the percentage of the pool’s balance associated with non-FTHB purchases. We controlled for refi incentive by looking at pools that were out of the money by 0bp to 125bp. As the percentage of non-FTHBs in a pool increases, so does early prepayment speed, albeit with noise around the trend.

We observe that a very small percentage of non-FTHB borrowers are making large curtailment payments in the first few months after closing and that these large payments translate into a short-term pop in speeds on new production at- or out-of-the-money pools. Investors looking to take advantage of this behavior on discount MBS should focus on pools with high non-FTHB borrowers.

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