How Buyouts Drive Ginnie Mae Prepayment Speeds

Because Ginnie Mae mortgage-backed securities are backed by the full faith and credit of the U.S. government, investors are not subject to credit losses. However, the potential for non-performing loan buyouts creates an additional layer of prepayment risk. As with any prepayment, investors receive the unpaid principal balance of the loan that goes through buyout. However, for all 30-year pass-throughs with 3% and higher coupons trading above par, any prepayment (due to a buyout or otherwise) represents a loss to the investor.

So how much of a concern are buyouts for investors?

Private-Label Securities – Technological Solutions to Information Asymmetry and Mistrust

At its heart, the failure of the private-label residential mortgage backed securities (PLS) market to return to its pre-crisis volume is a failure of trust. Unlike Agency-backed securities, private-label securities lack the safety net of government support. While this will always be the case, there are other means of garnering trust in order to revive the PLS market. At its core, mistrust is a product of information asymmetry—all stakeholders do not have access to the same information.

Back-Testing: Using RS Edge to Validate a Prepayment Model

Most asset-liability management (ALM) models contain an embedded prepayment model for residential mortgage loans. To gauge their accuracy, prepayment modelers typically run a back-test comparing model projections to the actual prepayment rates observed.

Why Model Validation Does Not Eliminate Spreadsheet Risk

Model risk managers invest considerable time in determining which spreadsheets qualify as models, which are end-user computing (EUC) applications, and which are neither. Seldom, however, do model risk managers consider the question of whether a spreadsheet is the appropriate tool for the task at hand.

Non-Qualified Mortgage Securitization Market

Since 2015, a new tier of the private-label residential mortgage-backed securities (PLS) market has emerged, with securities collateralized by non-qualified mortgage (non-QM) loans. These securities enable mortgage lenders to serve borrowers with non-traditional credit profiles.

Loans Under $200K Prepay Slowly—But Not in Every State

In agency pools, loans with balances below $200,000 offer prepayment protection (i.e., they prepay more slowly) relative to loans with higher balances. Servicers typically segregate these loans into specified pools that trade at a premium over TBA-deliverable pools. But the prepayment protection isn’t homogenous and varies significantly by state.

The Future of Mortgage Data is on a Blockchain

During last week's SFIG Residential Mortgage Finance Symposium, I moderated a panel on best practices in disclosure and reporting data related to private-label mortgage securities. We discussed many of the challenges confronting issuers, investors, rating agencies, and the industry with sharing relevant data in general and with implementing the SEC's Regulation AB II requirements in particular. Five minutes after my panel ended, my colleague Suhrud Dagli moderated a panel that discussed the applicability of blockchain technology to the securitization industry. Walking out of the symposium a short time later, I began to wonder how interesting it would have been if our two sessions had been combined.

An Analysis of VA Mortgage Refinance and Performance Data

In light of recent news stories[i] concerning efforts to stem aggressive solicitations that steer VA borrowers toward refinancings that are not necessarily in their best interest, we thought it fitting to take a look at some of the data underlying this trend. At issue are claims that VA borrowers are being persuaded to refinance their mortgages ostensibly to reduce their monthly payment. In many cases, however, the lower monthly payment was being made possible primarily by upfront fees and by extending the term of the mortgage. Consequently, even though the monthly mortgage payment was going down, the mortgage balance was often going up along with the number of payments required to pay off the mortgage. Let’s see what the data indicates.

Tuning Machine Learning Models

Tuning is the process of maximizing a model’s performance without overfitting or creating too high of a variance. In machine learning, this is accomplished by selecting appropriate “hyperparameters.”

AML Models: Applying Model Validation Principles to Non-Models

Anti-money-laundering (AML) solutions have no business being classified as models. To be sure, AML “models” are sophisticated, complex, and vitally important. But it requires a rather expansive interpretation of the OCC/Federal Reserve/FDIC<sup>1</sup> definition of the term <i>model</i> to realistically apply the term to AML solutions.

Machine Learning Model Selection

Machine learning model selection is the second step of the machine learning process, following variable selection and data cleansing. Selecting the right machine learning model is a critical step, as a model which does not appropriately fit the data will yield inaccurate results.

End-User Computing Controls – Building an EUC Inventory

An accounting manager at a mid-sized bank recently wondered aloud to us how to approach implementing end-user computing (EUC) controls.  She had recently become responsible for identifying and overseeing her institution's unknown number of EUC applications and had obviously given a lot of thought to the types of applications that needed to be identified and what the review process ought to look like. She recognized that a comprehensive inventory would need to be built, but, like so many others in her position, was uncertain of how to go about it.

Evaluating Supervised and Unsupervised Learning Models

Model evaluation is the process of objectively measuring how well machine learning models perform the specific tasks they were designed to do—such as predicting a stock price or appropriately flagging credit card transactions as fraud. Because each machine learning model is unique, optimal methods of evaluation vary depending on whether the model in question is “supervised” or “unsupervised.” Supervised machine learning models make specific predictions or classifications based on labeled training data, while unsupervised machine learning models seek to cluster or otherwise find patterns in unlabeled data.

Basel III Capital Requirements and CECL

With the upcoming implementation of IFRS 9 in 2018, the discussion of Basel III capital requirements is gaining importance. The relationship between capital and provisions for loan-loss has been a topic of discussion as the world moves towards mandating loss provisioning by looking out over the life of a financial asset. How will this new credit-loss approach for provisioning affect regulatory capital?

Feature Selection - Machine Learning Methods

Feature selection in machine learning refers to the process of isolating only those variables (or “features”) in a dataset that are pertinent to the analysis. Failure to do this effectively has many drawbacks, including: 1) unnecessarily complex models with difficult-to-interpret outcomes, 2) longer computing time, and 3) collinearity and overfitting. Effective feature selection eliminates redundant variables and keeps only the best subset of predictors in the model, thus making it possible to represent the data in the simplest way.

Fed MBS Runoff Portends More Negative Vega for the Broader Market

With much anticipation and fanfare, the Federal Reserve is finally on track to reduce its MBS holdings. Guidance from the September FOMC meeting reveals that the Fed will allow its MBS holdings to “run off,” reducing its position via prepayments as opposed to selling it off. What does this mean for the market? In the long-term, it means a large increase in net supply of Agency MBS and with it an increase in overall implied and realized volatility.

What is an “S-Curve” and Does it Matter if it Varies by Servicer?

Interestingly, the shape of a deal’s S-curve tends to vary depending on who is servicing the deal. Many things contribute to this difference, including how actively servicers market refinance opportunities. How important is it to be able to evaluate and analyze the S-curves for the servicers specific to a given deal? It depends, but it could be imperative.

Recent FASB Updates Related to CECL

Implementing CECL has brought about a host of accounting and other technical questions. The Financial Accounting Standards Board (FASB) works with the industry through a series of meetings to identify these questions, evaluate industry feedback, and periodically issue clarifying statements. We will continuously publish summarized points of interest from these meetings as they arise.

Reviving the Private-Label RMBS Market with Improvements to the Securitization Process

Weaknesses in securitization processes for mortgage loans contributed to the financial crisis of 2007 – 2008 and have led to a decade-long stagnation in the private-label residential mortgage-backed securities (PLS) market.

Although market participants have attempted to improve known weaknesses, lack of demand for private-label RMBS reflects investors’ reluctance to re-enter the market and the need for continued improvements to securitization processes to re-establish market activity.  While significant issues still need to be addressed, promising advances have been made in the PLS market that improve information provided to investors as well as checks and balances designed to boost transaction performance.

HARP Loan Expansion is Exciting News for CRT Data Analysts

Last year the Federal Housing Finance Agency (FHFA)—Fannie Mae’s and Freddie Mac’s regulator—announced a streamlined version of the federal government’s popular Home Affordable Refinance Program (HARP). The streamlined program will expand HARP eligibility to include mortgages originated on or after October 1, 2017.