Agency Mortgage-backed Securities Research Articles

Practical Applications of Machine Learning–Based Systematic Investing in Agency Mortgage-Backed Securities

Practical Applications of Machine Learning–Based Systematic Investing in Agency Mortgage-Backed Securities

Cheapest-to-deliver pricing, optimal MBS securitization, and welfare implications

We study optimal securitization in the agency mortgage-backed securities (MBS) market. Many MBS are traded in the liquid to-be-announced (TBA) market, which however induces adverse selection due to cheapest-to-deliver pricing. We find that lenders pool high-value loans separately and trade them in a less liquid market. We estimate a model of MBS pooling and trading to study welfare implications of pooling policies. TBA market structure produces a trade-off between efficiency and equity; broader pooling increases liquidity and average welfare, but results in a larger cross-subsidy from smaller loans to larger loans. Minimizing costs or limiting strategic pooling results in a more regressive redistribution.

Practical Applications of Primer on Agency Mortgage-Backed Securities Specified Pools and Their Convexity Profiles

In <b>Primer on Agency Mortgage-Backed Securities Specified Pools and Their Convexity Profiles</b> from the Spring 2022 issue of <b><i>The Journal of Fixed Income</i></b>, authors <b>Glenn M. Schultz</b> (of <b>MUFG Securities</b>) and <b>Frank J. Fabozzi</b> (of <b>Johns Hopkins University</b> and <b>EDHEC Risk Institute</b>) describe the various sectors of the market for agency mortgage-backed securities (MBS) specified pools. While most MBS are traded on a generic basis—without identifying the underlying pool of mortgage loans—some trades specify the particular underlying pool of the security that must be delivered to clear the trade. The first type of trade is called a to-be-announced (TBA) trade, and the second type is called a specified-pool trade. When market participants trade agency MBS on a specified pool basis, they generally do so because they expect the loans in the underlying pools to display atypical prepayment behavior, which gives the related MBS atypical convexity properties. Specified pools are categorized into sectors, based on loan balance, loan-to-value (LTV) ratio, and geographical location, plus whether they consist of refinanced mortgages or mortgages for investment properties, relocating employees, or people with suboptimal credit scores. The authors describe each specified-pool sector and its convexity profile. Most have positive convexity relative to generic pools and trade at a premium, but jumbo and relocation mortgage pools have negative convexity relative to generic pools and trade at a concession. Investors can manage risk by allocating assets to customize the convexity profiles of their MBS portfolios.

Machine Learning–Based Systematic Investing in Agency Mortgage-Backed Securities

With a total outstanding balance of more than $8 trillion as of this writing, agency mortgage-backed securities (MBS) represent the second largest segment of the US bond market and the second most liquid fixed-income market after US Treasuries. Institutional investors have long participated in this market to take advantage of its attractive spread over US Treasuries, low credit risk, low transaction cost, and the ability to transact large quantities with ease. MBS are made of individual mortgages extended to US homeowners. The ability for a homeowner to refinance at any point introduces complexity in prepayment analysis and investing in the MBS sector. Traditional prepayment modeling has been able to capture many of the relationships between prepayments and related factors such as the level of interest rates and the value of the embedded prepayment option, yet the manual nature of variable construction and sheer amount of available data make it difficult to capture the dynamics of extremely complex systems. The long history and large amount of data available in MBS make it a prime candidate to leverage machine learning (ML) algorithms to better explain complex relationships between various macro- and microeconomic factors and MBS prepayments. The authors propose a systematic investment strategy using an ML-based mortgage prepayment model approach combined with a coupon allocation optimization model to create an optimal portfolio to capture alpha vs. a benchmark.

An Analysis of the Interest Rate Risk of the Federal Reserve’s Balance Sheet, Part 1: Background and Historical Perspective

As part of its implementation of monetary policy, the Federal Reserve (Fed) holds Treasury securities and agency mortgage-backed securities (MBS) in the System Open Market Account (SOMA). The market value of these securities and the Fed's income fluctuate with changes in interest rates. As such, the ongoing increases in policy rates to address inflationary pressures are expected to put downward pressure on the Fed's net income.

Primer on Agency Mortgage-Backed Securities Specified Pools and Their Convexity Profiles

Specified pools are agency mortgage-backed securities whose loan pools have been found to exhibit different (i.e., superior or inferior to) convexity relative to generic to-be-announced pools. In this article, the authors describe the various sectors of the specified pool market, the convexity profile of the sectors of the specified pool market, and the monitoring of specified pool prepayment differentials.

Editor’s Letter

1. Stanley J. Kon 1. Editor The performance of security selection within the Agency mortgage-backed securities market is primarily dependent on the quality of the trader’s prepayment model. Each prepayment wave has unique characteristics and an opportunity for dynamic adaptation. We begin

The Real Effects of Secondary Market Trading Structure: Evidence from the Mortgage Market

Abstract By allowing different agency mortgage-backed securities (MBS) to be traded based on limited characteristics, the to-be-announced (TBA) market generates liquidity and benefits the MBS market broadly. We quantify effects of the TBA structure on mortgage borrowers. Exploiting discontinuities in TBA eligibility, we estimate that TBA eligibility reduces mortgage rates by 7 to 28 basis points. The TBA eligibility benefit is larger for mortgages with higher expected prepayments. We also find that TBA eligibility affects refinancing, which has implications for monetary policy transmission. Our finding is relevant for housing policies, such as housing finance reforms and uniform MBS. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Quantitative easing and agency MBS investment and financing choices by mortgage REITs

AbstractWe study Agency Mortgage Real Estate Investment Trusts (Agency MREITs)—shadow banks that invest in guaranteed U.S. Agency mortgage‐backed securities (MBS) and that are principally funded with repo debt—to test how quantitative easing (QE) affects financial risk taking. Agency MREIT asset growth is inversely related to the Federal Reserve's Agency mortgage‐backed securities (MBS) purchases, reflecting investor portfolio rebalancing. Agency MREITs increased leverage during QE, consistent with “reaching for yield.” They countered the heightened solvency risk by extending repo maturity and increased hedging of funding costs to reduce liquidity and interest rate risk. Research linking QE to increased credit risk taking should account for contemporaneous changes in financing choices and risk management.

Cheapest-to-Deliver Pricing, Optimal MBS Securitization, and Market Quality

We study optimal securitization and its impact on market quality when the secondary market structure leads to cheapest-to-deliver pricing in the context of agency mortgage-backed securities (MBS). A majority of MBS are traded in the to-be-announced (TBA) market, which concentrates trading of heterogeneous MBS into a few liquid TBA contracts but induces adverse selection. We find that lenders segregate loans of like values into separate pools and tend to trade low-value MBS in the TBA market and high-value MBS outside the TBA market. We then present a model of optimal securitization for agency MBS. Lenders do not internalize the negative impact of such pooling and trading strategies on TBA market quality and thus create too many high-value MBS, which leads to more heterogeneity in MBS values, more adverse selection, and lower TBA liquidity. Lastly, we provide empirical evidence consistent with model predictions on how MBS pooling changes with trading costs and underlying loan value dispersion and how pooling practices affect MBS heterogeneity and TBA market adverse selection.

Defragmenting Markets: Evidence from Agency MBS

Agency mortgage-backed securities (MBS) issued by Fannie Mae and Freddie Mac have historically traded in separate forward markets. We study the consequences of this fragmentation, showing that market liquidity endogenously concentrated in Fannie Mae MBS, leading to higher issuance and trading volume, lower transaction costs, higher security prices, and a lower primary market cost of capital for Fannie Mae. We then analyze a change in market design—the Single Security Initiative—which consolidated Fannie Mae and Freddie Mac MBS trading into a single market in June 2019. We find that consolidation increased the liquidity and prices of Freddie Mac MBS without measurably reducing liquidity for Fannie Mae; this was in part achieved by aligning characteristics of the underlying MBS pools issued by the two agencies. Prices partially converged prior to the consolidation event, in anticipation of future liquidity. Consolidation increased Freddie Mac’s fee income by enabling it to remove discounts that previously compensated loan sellers for lower liquidity.

Fixed Income Market Structure: Treasuries vs. Agency MBS

This FEDS Note analyzes the structure of the agency mortgage-backed securities (MBS) market through the lens of the TRACE Treasury data initiative, which is a significant component of a broader inter-agency effort to enhance understanding and transparency of the Treasury securities market. As in several previous FEDS Notes describing the Treasury cash market structure, this note uses transactions reported to the Financial Industry Regulatory Authority (FINRA)'s Trade Reporting and Compliance Engine (TRACE) to examine aggregate trading volumes in the agency MBS market across venues, security types and participants. We show how agency MBS provide a useful counterfactual to cash Treasuries when analyzing the evolution of Treasury cash market structure and its implications for liquidity. We provide evidence that the participation of Principal Trading Firms (PTFs) in Treasury markets has caused the overall volume of intermediation to rise there, particularly in the interdealer broker (IDB) venue. We also find that, relative to Treasury markets, intermediation in the agency MBS market is concentrated among fewer firms, and in particular the primary dealers, suggesting that PTF participation in Treasury markets has diversified intermediation in the IDB venue across a larger number of firms.

Quantitative Easing and Financial Risk Taking: Evidence from Agency Mortgage REITs

An emerging literature documents a link between central bank quantitative easing (QE) and financial institution credit risk-taking. This paper tests the complementary hypothesis that QE may also affect financial risk-taking. We study Agency MREITs – levered shadow banks that invest in guaranteed U.S. Agency mortgage-backed securities (MBS) principally funded with repo debt. We show that Agency MREIT growth is inversely related to the Federal Reserve’s Agency MBS purchases, reflecting investor portfolio rebalancing. We also find that these institutions increased leverage during the later stages of QE, consistent with “reaching for yield” behavior. Agency MREITs seem to concurrently adjust their liquidity and interest rate risk profiles.

Rank-Based Error Tracking for Agency MBS Prepayment Models

Mode testing, also called error tracking, is a key requirement for collateral behavior forecasting models in securitized product, including models of prepayment, default, response, utilization, etc. The high dimensionality and statistical noise associated with agency mortgage-backed securities prepayment behavior make error tracking a complex task. The traditional method focuses on single dimension, for example, along vintage coupon, and does not provide a clear measure of model accuracy and effectiveness. The new method, a rank-based error-tracking methodology, provides an efficient and comprehensive approach to measure model performance. It provides a clear definition of accuracy-which allows clear measure to compare among models. This is superior to the existing one-dimensional method, and other supplement methods (e.g., lift curve method for loans). We discuss these issues as well as applicable statistical theory, and potential applications. <b>TOPICS:</b>MBS and residential mortgage loans, factor-based models <b>Key Findings</b> • The proposed ranked-based error tracking provides a comprehensive and consistent measure for model performance across macroeconomic environment and across pool/loan attributes, superior to the prevailing one-dimensional error-tracking methodology. • Two key measures are the steepness and accuracy of the ranking curve. The steepness measures quantify the model’s ability differentiating across macro and pool variables. The accuracy measures quantify the model’s ability forecasting the prepayment intensity. • The rank-based methodology can be applied to any selected sample pool universe and performance periods. It can be systematically used for model review and model comparison.

Cohort Trading and Security Design: Theory and Evidence from Agency MBS Markets

Agency mortgage-backed securities (MBSs) are traded via both individual-asset-based specified pool (SP) contracts and cohort-based to-be-announced (TBA) contracts, which allow for the delivery of heterogeneous MBSs at a uniform price. We theoretically derive and empirically document the economic effects of cohort trading on issuers' security design: (1) Low-value and high-value loans are securitized into separate groups of MBSs sold in TBA and SP markets respectively; (2) issuers pool low-value loans together into TBA MBSs, consistent with arbitrary packaging practices based on the uniform TBA pricing; (3) issuers separate distinct high-value loans into multiple SP MBSs, suggesting that SP selling revenue exhibits a certain degree of convexity in MBS value; and (4) large issuers take more advantage of security design than small issuers do. Finally, we quantify the economic benefits of strategic security design to issuers to be 3 basis points in price, about 36% of the transaction costs for SP MBSs.

Monetary stimulus and bank lending

The US Federal Reserve purchased both agency mortgage-backed securities (MBS) and Treasury securities to conduct quantitative easing. Using micro-level data, we find that banks benefiting from MBS purchases increase mortgage origination, compared with other banks. At the same time, these banks reduce commercial lending and firms that borrow from these banks decrease investment. The effect of Treasury purchases is different: either positive or insignificant in most cases. Our results suggest that MBS purchases caused unintended real effects and that Treasury purchases did not cause a large positive stimulus to the economy through the bank lending channel.

The Real Effects of Secondary Market Trading Structure: Evidence from the Mortgage Market

By allowing different agency mortgage-backed securities (MBS) to be traded based on limited characteristics, the to-be-announced (TBA) market generates liquidity and benefits the MBS market broadly. We quantify effects of the TBA structure on mortgage borrowers. Exploiting discontinuities in TBA eligibility, we estimate that TBA eligibility reduces mortgage rates by 7 to 28 basis points. TBA eligibility benefit is larger for mortgages with higher expected prepayments. We also find that TBA eligibility affects refinancing, which has implications for monetary policy transmission. Our finding is relevant for policies such as housing finance reforms and Uniform MBS.

Understanding Mortgage Spreads

Abstract Because most mortgages in the United States are securitized in agency mortgage-backed securities (MBS), yield spreads on MBS are a key determinant of homeowners’ funding costs. We study variation in MBS spreads in the time series and across securities and document that MBS spreads show a pronounced cross-sectional smile with respect to the securities’ coupon rates. We present a new pricing model that uses “stripped” MBS prices to identify the contribution of non-interest-rate prepayment risk to spreads and find that this risk explains the smile, whereas the time-series spread variation is mostly accounted for by nonprepayment risk factors. Received March 30, 2015; editorial decision November 21, 2018 by Editor Leonid Kogan. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Demand for Agency MBS in the Era of the Fed Balance Sheet Unwind

Since the Federal Reserve signaled plans to gradually pare down its holdings of Treasuries and agency debt and mortgage-backed securities (MBS) accumulated in the aftermath of the 2008 financial crisis, investors have expressed concern over how the removal of the Fed as an active buyer would impact the government-sponsored enterprise MBS market. In addition, expectations for increased MBS issuance fueled by a strong labor market have added to worries about rising supply in the face of declining demand. It has been more than one year since the Fed began to shrink its balance sheet. This article examines how market conditions over the past year have affected the speed of Fed balance sheet normalization relative to expectations, as well as ongoing developments in the agency MBS market, and finds that feared market imbalances have largely failed to materialize.

Investment Performance of Credit Risk Transfer Securities (CRTs): The Early Evidence

Credit risk transfer (CRT) securities were introduced by Fannie Mae and Freddie Mac in 2013. As of the end of 2017, in combination, the two government sponsored housing enterprises had issued a total of $53 billion in CRTs linked to residential mortgage loans with a total face value of $1.79 trillion. The goal is to shift mortgage risk from tax payers to the private sector. In return, investors expect to be compensated. The authors document the returns earned by investors in the various CRT tranches since their inception. The most senior tranches have provided an average representative return of 0.26% per month. The most junior tranches have provided an average representative return of 1.71% per month. These compare with average monthly returns of 0.03%, 0.15%, and 0.87% to T-bills, 15-year agency mortgage-backed securities, and 10-year high-yield corporate bonds, respectively, over the same time periods.