Where Would We Be Without the Mortgage Market?
It’s bleak out there. Can you imagine how much bleaker it would be if the U.S. mortgage market weren’t doing its thing to prop up the economy?
The mortgage market is helping healthy borrowers take advantage of lower interest rates to improve their personal balance sheets. And it is helping struggling borrowers by offering generous loss mitigation options.
The mortgage market plays a unique role in the U.S. economy. It is a hybrid consortium of originators, guarantors, investors, and policymakers intent on offering competitive rates in a transparent market structure—a structure that is the beneficiary of both good government policy and a robust, competitive private marketplace.
The mortgage market’s pro-cyclical role in the U.S. economy allocates credit and interest rate risk among borrowers, investors and the federal government. When the government’s interest rates go down, so do mortgage rates.
March 2020
COVID-19 turned the world’s economies on their heads. Once strong growing economies ground to a stop. By mid-March, the negative effect of the pandemic in the U.S. was clear, with sharply rising unemployment claims and a declining Q1 GDP. COVID-19 did not spare the mortgage market. Fear of borrower defaults led to a freezing up of the credit market, which in turn fueled anxiety among mortgage servicers, guarantors, investors, and originators.
The U.S. government and Federal Reserve responded quickly. Applying lessons learned from the 2008, they initiated housing relief programs early. Congress immediately passed legislation enabling forbearance and eviction protection programs to borrowers and renters. The Federal Reserve promptly cut interest rates to near zero while using its balance sheet to quell market concerns and ensure liquidity.
The FHFA’s Credit Risk Transfer program worked as intended, sharing with willing investors the credit risk uncertainty and, in due course, the resulting credit losses. By April, the mortgage market’s guarantors—Ginnie Mae, Fannie Mae and Freddie Mac—imposed P&I advance programs on servicers and investors, thus ensuring the continuation of the mortgage servicing market.
Rallying the Troops
Boy, it was a tough spring for the industry. But now all the pieces were in place:
- New legislation to aid borrowers
- Lower rates and market liquidity from the Fed
- P&I advance solutions and underwriting guidance from the Agencies
The U.S. mortgage market was finally in a position to play its role in steadying the economy. Mortgages help the economy by lowering debt burden ratios and increasing available spendable income and investible assets. Both of these conditions contribute to the stabilization and recovery of the economy.
This relief is provided through:
- Rate-and-term refinances, which lower borrowers’ monthly mortgage payments,
- Purchase loans, which help borrowers capitalize on low interest rates to buy new houses, and
- Cash-out refinances, which enable borrowers to convert home equity into spendable and investable cash.
Mortgage origination volume in 2020 is now projected to reach $2.8 trillion—a 30% increase over 2019—despite 11% unemployment and more than 4 million loans in forbearance.
But near-term issues remain
It would be a misstatement to say all things are great for the U.S. mortgage market. While mortgage rates are at 50-year lows, they are not as low as they could be. The dramatic increase in volume has forced originators to raise rates in order to manage their production surges. Mortgage servicing rights values have plunged on new originations, which also leads to higher borrower rates. In other words, a good portion of the pro-cyclical benefit of lower interest rates is not actually making its way into the hands of mortgage borrowers.
In addition, the current high rate of unemployment and forbearance will ultimately come home to roost in the form of elevated default rates as the economy’s recovery from COVID-19 continues to look more U-shaped than the originally hoped for V-shape. Any increases in default rates will certainly be met with new rounds of government intervention. This almost always results in higher costs to servicers.
Long-term uncertainties
The pandemic continues to wreak havoc on people and economies. Its duration and cumulative impacts are still unknown but are certain to reshape the U.S. mortgage market. Still unanswered are the growing questions around how the following will affect local real estate values, defaults, and future business volumes:
- The emerging work-from-home economy
- Permanent employment dislocations from the loss of travel, entertainment, and retail jobs
- Loss of future rate-and-term refinance business because of today’s low rates
- Muted future purchase volumes due to high unemployment
Notwithstanding these uncertainties, the U.S. mortgage market will play a vital role in the economy’s rebuilding. Its resiliency and willingness to learn from past mistakes, combined with an activist role of government and its guarantors, not only ensure the market’s long-term viability and success. These qualities also position it as a mooring point for an economy otherwise tossed about in a turbulent storm of uncertainty.
Webinar: Basics of the Reference Rate Transition
webinar
Basics of the Reference Rate Transition
In June 2017, the ARRC announced the Secure Overnight Financing Rate (SOFR) as its recommended alternative rate, replacing LIBOR by the end of 2021.
Learn from RiskSpan experts Tom Pappalardo and Pat Greene the current industry standard for LIBOR, the possible challenges with SOFR, and how to mitigate your risk.
About The Hosts
Tom Pappalardo
Managing Director
Thomas Pappalardo is head of RiskSpan’s Data, Modeling and Analytics Consulting Practice and has 20+ years of broad experience in mortgage technology, finance and operations and retail banking industries. He is an experienced engagement manager, data and business requirements lead, business process and internal controls analyst and financial model validator. At RiskSpan, Tom has led multiple client engagements supporting the development of analytical applications, reengineering of business processes, validation of financial models and development of model risk management policies for the GSE’s (Fannie Mae, Freddie Mac, Federal Home Loan Banks), commercial banks, mortgage banks and non-bank servicers.
Patrick Greene
Senior Managing Director
Patrick Greene currently supports consulting and advisory services provided by RiskSpan for clients implementing securitization activities. In addition, he has delivered technology solutions and provided financial model validation support to multiple RiskSpan clients whose business practices rely on credit models, interest-rate models, prepayment models, income simulation models, counter-party risk models, whole loan valuation models, and bond redemption forecasting models. Pat is an experienced executive who has been responsible for the management of a leading asset securitization program for a national financial institution.
Using RS Edge to Quantify the Impact of The QM Patch Expiration
Using RS Edge Data to Quantify the Impact of the QM Patch Expiration
A 2014 Consumer Financial Protection Bureau (CFPB) rule established that mortgages purchased by the GSEs (Fannie Mae or Freddie Mac) can be considered “qualified” even if their debt-to-income ratio (DTI) exceeds 43 percent. This provision is known as the “qualified mortgage (QM) patch” or sometimes the “GSE patch.” It has become one of the most important holdouts of the Dodd-Frank Act and an important facilitator of U.S. lending activity under looser credit standards. The CFPB implemented the patch to encourage lenders to make loans that do not meet QM requirements, but are still “responsibly underwritten.” Because all GSE loans must pass the strict standards for conforming mortgages, they are presumed to be reasonably underwritten–notwithstanding sometimes having DTI ratios higher than 43 percent.
The QM patch is set to expire on January 10, 2021. This phaseout has spawned concern over the impact both on mortgage originators and potentially on borrowers when the patch is no longer available and GSEs are less apt to purchase loans with higher DTI ratios.[1]
We performed an analysis of GSE loan data housed in RiskSpan’s RS Edge platform to quantify this potential impact.
The Good News:
The slowdown in purchases of high-DTI loans is already occurring, which could partially mitigate the impact of the expiration of the patch.
We used RS Edge to analyze the percentage of QM loans to which the patch applies today. From 2016 through the beginning of 2019, Fannie and Freddie sharply increased their purchases of loans with DTI ratios greater than 43 percent, with these loans accounting for over 34 percent of Fannie’s purchases as recently as February 2019 and over 30 percent of Freddie’s purchases in November 2018 (see Figure 1).
Figure 1: % of GSE Acquisitions with DTI > 43 (2016 – 2019)

Our data shows, however, that Fannie and Freddie have already begun to wind down purchases of these loans. By the end of 2019, only about 23 percent of GSE loans purchased had DTI greater than 43 percent. This is illustrated more clearly in Figure 2, below.
Figure 2: % of GSE Acquisitions with DTI > 43 (2019 only)

As discussed in the December 2019 Wall Street Journal article “Fannie Mae and Freddie Mac Curb Some Loans as Regulator Reins in Risk,” the wind-down could be related to the GSE’s general efforts to hold stronger portfolios as they aim to climb out of conservatorship. However, our data suggests an equally plausible explanation for the slowdown Borrowers generally exhibit a greater willingness to stretch their incomes to buy a house than to refinance, so purchase loans are more likely than refinancings to feature higher DTI ratios. Figure 3 illustrates this phenomenon.
Figure 3: Most High-DTI Loans Back Home Purchases

The Bad News:
The bad news, of course, is that one-fifth of Freddie and Fannie loans purchased with DTI>43% is still significant. Over 900,000 mortgages purchased by the GSEs in 2019 were of the High-DTI variety, accounting for over $240 billion in UPB.
In theory, these 900,000 borrowers will no longer have a way of being slotted into QM loans after the patch expires next year. While this could be good news for the non-QM market, which would potentially be poised to capture this new business, it may not be the best news for these borrowers, who likely do not fancy paying the higher interest rates generally associated with non-QM lending.
Originators, not relishing the prospect of losing QM protection for these loans, have also expressed concern about the phaseout of the patch. A group of lenders that includes Wells Fargo and Quicken Loans has petitioned the CFPB to completely eliminate the DTI requirements under ability-to-pay rules.
Figure 4: % of DTI>43 Loans Sold to GSEs by Originator

We will be closely monitoring the situation and continuing to offer tools that will help to quantify the potential impact of the expiration.
[1] Consumer Financial Protection Bureau, July 25, 2019.[/vc_column_text][/vc_column][/vc_row]
Fannie Mae and Freddie Mac Launch New Uniform Mortgage-Backed Security (UMBS)
Today, Fannie Mae and Freddie Mac begin issuing the long-awaited Uniform Mortgage-Backed Security (UMBS). The Federal Housing Finance Administration (FHFA) conceived of this new standard in its 2012 “A Strategic Plan for Enterprise Conservatorships,” which marked the start of the Single Security Initiative (the history of which is laid out in the graphic below).
RiskSpan produces FHFA’s quarterly performance reports, most recently published Wednesday, May 29, which will support the agency’s oversight of the UMBS. The FHFA uses this report to monitor prepayment performance of passthroughs issued by Fannie and Freddie. These reports provide market participants with additional transparency on prepayment behavior alignment. They also allow the FHFA to monitor and address differences in conditional prepayments rates (CPR) between the two issuers and to align programs, policies, and practices that affect the cash flows of “To-Be-Announced” (TBA)-eligible Mortgage-Backed Securities (MBS).
The importance of RiskSpan’s contributions to the FHFA’s efforts are highlighted in Bloomberg’s May 30 article, “A $4 Trillion Plan Could Make or Break Dreams of U.S. Homebuyers”.
Low MI No Problem: Analyzing the Historical Performance of Home Affordable Loans
Introduction In our last CRT Deal Monitor post, we touched on a trend we have noticed- that the number of loans being originated with less-than-standard MI coverage has been increasing. This is a trend we will be covering in a series of blog posts. The following analysis provides a historical view of the performance of loans with less than standard MI coverage, like those being originated through the Fannie Mae HomeReady and Freddie Mac HomePossible programs. Fannie Mae CAS Deals contain a steadily growing percent of UPB in the HomeReady program. While Freddie Mac does not currently include a HomePossible indicator we suspect the same trend is occurring. In the coming months Freddie Mac will add this disclosure enhancement and we will investigate. Historical data indicates that these HomeReady loans perform just as well, if not better, than similar loans not in an affordability program (see appendix for the cohort definitions). However, this trend appears to be shifting as newer vintages with standard MI have experienced less (albeit slightly) losses than their HomeReady counterparts, though there is significantly less performance history available. The table below shows the cumulative default rate for each vintage segmented by LTV cutoffs for the HomeReady Program. Analysis The plots below present a profile of Fannie Mae HomeReady and Standard MI cohorts via the distributions of UPB, LTV, FICO, and DTI dating back to 1999. The cohorts are similar, though the Standard MI cohort does present a slightly better credit profile. The Standard MI cohort contains more loans with <= 95% LTV, slightly higher FICOs, slightly lower DTIs, and higher average loan sizes. All plots in this post are interactive:
- Click and drag in any of the plots to zoom on a region.
- Isolate groups by double clicking on the legend entries, and single click to add groups back in.
Cohort Characteristics Plots: To compare performance through time each cohort has been grouped by Vintage. The plot below shows the cumulative default rate based on months from origination for each Vintage MI cohort. Based on the data, the older HomeReady population has experienced a lower overall default rate vs. the same vintage with Standard MI. This effect is exaggerated for vintages originated immediately preceding the crisis and is observed consistently through 2011. Unsurprisingly, since the Low MI cohorts experienced a lower overall default rate, they also experienced a lower cumulative net loss which is displayed for each vintage on hover. Select a single vintage from the dropdown menu or isolate vintage(s) by clicking the lines or legend. Cumulative Default Rate Plot: Since the HomeReady population is characterized by having less than standard MI, we should expect this population to have a higher loss severity. This relationship is seen in the data and is most prominent from the 2005 vintage onward. With the exception of the 2011 vintage, the gap between severity for Low and Standard MI has grown stronger through time. Cumulative Severity Plot: In the next installment of this series we will cover specific loss characteristics for the HomeReady and Standard MI populations, and discuss the impact of Borrower Area Median Income, which is an eligibility requirement for the HomeReady population. Appendix: Cohort Selection Criteria: For this analysis, the historical performance of two cohorts ‘Low MI’ and ‘Standard MI’ were pulled from RiskSpan’s Edge Platform from the Fannie Mae Loan Performance Dataset. The cohorts contain approximately 800,000 and 2,1M loans respectively. The cohorts were established based on the current MI coverage requirements set by Fannie Mae, and were limited to loans with LTV > 90.1%. The matrix below shows MI coverage requirements for the HomeReady (Low MI) cohort and Standard MI cohort. Cohort 1 – Low MI Coverage: Cohort 2 – Standard MI Coverage:
GSE: Datamart Design and Build
The Problem
A government-sponsored enterprise needed a centralized data solution for its forecasting process, which involved cross-functional teams from different business lines.
The firm also sought a cloud-based data warehouse to host forecasting outputs for reporting purposes with faster querying and processing speeds.
The firm also needed assistance migrating data from legacy data sources to new datamarts. The input and output files and datasets had different sources and were often in different formats. Analysis and transformation were required prior to designing, developing and loading tables.
The Solution
RiskSpan built and now maintains a new centralized datamart (in both Oracle and Amazon Web Services) for the client’s revenue and loss forecasting processes. This includes data modeling, historical data upload, and the monthly recurring data process.
The Deliverables
- Analyzed the end-to-end data flow and data elements
- Designed data models satisfying business requirements
- Processed and mapped forecasting input and output files
- Migrated data from legacy databases to the new sources
- Built an Oracle datamart and a cloud-based data warehouse (Amazon Web Services)
- Led development team to develop schemas, tables and views, process scripts to maintain data updates and table partitioning logic
- Resolved data issues with the source and assisted in reconciliation of results
GSE: ETL Solutions
The Problem
The client needed ETL solutions for handling data of any complexity or size in a variety of formats and/or from different upstream sources.
The client’s data management team extracted and processed data from different sources and different types of databases (e.g. Oracle, Netezza, Excel files, SAS datasets, etc.), and needed to load into its Oracle and AWS datamarts for it’s revenue and loss forecasting processes.
The client’s forecasting process used very complex large-scale datasets in different formats which needed to be consumed and loaded in an automated and timely manner.
The Solution
RiskSpan was engaged to design, develop and implement ETL (Extract, Transform and Load) solutions for handling input and output data for the client’s revenue and loss forecasting processes. This included dealing with large volumes of data and multiple source systems, transforming and loading data to and from data marts and data ware houses.
The Deliverables
- Analyzed data sources and developed ETL strategies for different data types and sources
- Performed source target mapping in support of report and warehouse technical designs
- Implemented business-driven requirements using Informatica
- Collaborated with cross-functional business and development teams to document ETL requirements and turn them into ETL jobs
- Optimized, developed, and maintained integration solutions as necessary to connect legacy data stores and the data warehouses
Fannie Mae’s New CAS REMIC: Why REITs Are Suddenly Interested in CRT Deals
Fannie Mae has been issuing credit-risk-transfer (CRT) deals under its Connecticut Avenue Securities (CAS) program since 2013. The investor base for these securities has traditionally been a diverse group of asset managers, hedge funds, private equity firms, and insurance companies. The deals had been largely ignored by Real Estate Investment Trusts (REITs), however. The following pie charts illustrate the investor breakdown of Fannie Mae’s CAS 2018-C06 deal, issued in October 2018. Note that REITs accounted for only 11 percent of the investor base of the Group 1 and Group 2 M-2 tranches (see note below for information on how credit risk is distributed across tranches), and just 4 percent of the Group 1 B-1 tranche. Things began to change in November 2018, however, when Fannie Mae began to structure CAS offering as notes issued by trusts that qualify as Real Estate Mortgage Investment Conduits (REMICs). The first such REMIC offering, CAS 2018-R07, brought about a substantial shift in the investor distribution, with REITs now accounting for a significantly higher share. As the pie charts below illustrate, REITs now account for some 22 percent of the M-2 tranche investor base and nearly 20 percent of the B-1 tranche.
What Could Be Driving This Trend?
It seems reasonable to assume that REITs are flocking to more favorable tax treatment of REMIC-based structures. These will now be more simplified and aligned with other mortgage-related securities, as Fannie Mae points out. Additionally, the new CAS REMIC notes meet all the REIT income and asset tests for tax purposes, and there is a removal on tax withholding restrictions for non-U.S. investors in all tranches. The REMIC structure offers additional benefits to REITs and other investors. Unlike previous CAS issues, the CAS REMIC—a bankruptcy-remote trust—issues the securities and receives the cash proceeds from investors. Fannie Mae pays monthly payments to the trust in exchange for credit protection, and the trust is responsible for paying interest to the investors and repaying principal less any credit losses. Since it is this new third-party trustee issuing the CAS REMIC securities, investors will be shielded from exposure to any future counterparty risk with Fannie Mae. The introduction of the REMIC structure represents an exciting development for the CAS program and for CRT securities overall. It makes them more attractive to REITs and offers these and other traditional mortgage investors a new avenue into credit risk previously available only in the private-label market.
End Note: How Are CAS Notes Structured?
Notes issued prior to 2016 as part of the CAS program are aligned to a structure of six classes of reference tranches, as illustrated below:
Two mezzanine tranches of debt are offered for sale to investors. The structure also consists of 4 hypothetical reference tranches, retained by Fannie Mae and used for allocation of cash flows. When credit events occur, write-downs are first applied to the Fannie Mae retained first loss position. Only after the entire first loss position is written down are losses passed on to investors in mezzanine tranche debt – first M2, then M1. Loan prepayment is allocated along an opposite trajectory. As loans prepay, principal is first returned to the investors in M1 notes. Only after the full principal balance of M1 notes have been repaid do M2 note holders receive principal payments. Beginning with the February 2016 CAS issuance (2016-C01), notes follow a new structure of seven classes of reference tranches, as illustrated below:
In addition to the two mezzanine tranches, a portion of the bottom layer is also sold to investors. This allows Fannie Mae to transfer a portion of the initial expected loss. When credit events occur, both Fannie Mae and investors incur losses. Additionally, beginning with this issuance, the size of the B tranche was increased to 100 bps, effectively increasing the credit support offered to mezzanine tranches. Beginning with the January 2017 CAS issuance (2017-C01), notes follow a structure of eight classes of reference tranches, as illustrated below:
Fannie Mae split the B tranche horizontally into two equal tranches, with Fannie Mae retaining the first loss position. The size of the B1 tranche is 50 bps, and Fannie Mae retains a vertical slice of the B1 tranche.
What is SOFR and What Does it Mean For You?
What is SOFR
The Secured Overnight Financing Rate (SOFR) is a broad measure of the cost of borrowing cash overnight collateralized by U.S. Treasury securities. As such, it will reflect an economic cost of lending and borrowing relevant to the wide array of market participants active in the financial markets. However, SOFR is fundamentally different from LIBOR. SOFR is an overnight, secured, nearly risk-free rate, while LIBOR is an unsecured rate published at several different maturities. It is a fully transaction-based rate incorporating data from transactions across three segments of the U.S. Treasury Repo market (tri-party repo, General Collateral Finance (GCF) repo and bilateral repo cleared through the Fixed Income Clearing Corporation (FICC)).[1]
The ARRC noted the need for replacement rate spreads due to the differences between rates:
Because LIBOR is unsecured and therefore includes an element of bank credit risk, it is likely to be higher than SOFR and prone to widen when there is severe credit market stress. In contrast, because SOFR is secured and nearly risk-free, it is expected to be lower than LIBOR and may stay flat (or potentially even tighten) in periods of severe credit stress. Market participants are considering certain adjustments, referenced in the fallback proposal as the applicable ‘Replacement Benchmark Spread’, which would be intended to mitigate some of the differences between LIBOR and SOFR.[2]
Additional steps taken by government–sponsored enterprises (GSEs) have initiated the momentum in building out the SOFR market. In July 2018, Fannie Mae issued the first SOFR-denominated securities, leading the way for other institutions who have since followed suit. In November 2018, the Federal Home Loan Banks (FHLBs) issued $4bn in debt tied to SOFR. The action was taken to support liquidity and help demonstrate SOFR demand to develop the SOFR market for the approximately 7,000 member institutions – banks, credit unions, and insurers – who are in the process of transitioning away from LIBOR.[4] CME Group, a derivatives and futures exchange company, launched 3-month and 1-month SOFR futures contracts in 2018.[5] All of these steps taken to build out the market create a strong start for a rate that is already more stable than LIBOR—the transaction volume underpinning SOFR rates is around $750 billon daily, compared to USD LIBOR’s estimated $500 million.[6]
The ARRC has begun publishing guidance for fallback language and in the fall of 2018 published consultations on recommended language for floating rate notes and syndicated business loans.[7][8]
These initial steps to build out the necessary SOFR market put the United States ahead of the ARRC transition plan schedule and position the market well to begin SOFR implementation. However, a successful transition will require extensive engagement from other institutions. Affected institutions need to begin their transition now in order to make the gradual transition in time for the 2021 deadline.
Who Does This Transition Affect?
The transition affects any institutions that hold contracts, products, or tools that reference LIBOR and will not reach full maturity or phase out before the end of 2021.
What Actions Do Affected Institutions Need to Take?
- Establish a Sponsor and Project Team: Affected institutions need to take a phased approach to the transition away from LIBOR. Because of the need for continuous oversight, they should begin by identifying an executive sponsor and establishing a project team. The team should be responsible for all transition-related activities across the organization, including assessment of exposure and the applicability of alternative reference rates where necessary, planning the steps and timing of transition, and coordinating the implementation of transition away from LIBOR.
- Conduct an Impact Assessment: The first task of the project team is to complete an impact assessment to determine the institution’s LIBOR exposure across all financial products and existing contracts that mature after 2021, as well as any related models and business processes (including third-party vendors and data providers). Regarding contracts, the team should identify and categorize all variants of legacy fallback language in existing contracts. Additionally, the assessment should analyze the risk of the LIBOR transition to the institution’s basis and operational risk and across financial holdings.
- Mitigate Risks: Using results from the LIBOR exposure assessment, the project team should develop a plan running through 2021 to prioritize transition activities in a way that best mitigates risk on LIBOR exposure, and communicates the transition activities to employees and clients with ample time for them to learn about and buy into the transition objectives.
- Prepare new products and tools linked to alternative reference rates: This mitigates risk by limiting the number of legacy exposures that will still be in effect in 2021 and creates a clear direction for transition activities. New references may include financial instruments and products, contract language, models, pricing, risk, operational and technological processes and applications to support the new rates.
- Develop and Implement Transition Contract Terms: In legacy contracts that will mature after 2021, the project team will need to amend contracts and fallback language. The ARRC has begun to provide guidance for amendments or transitions related to some financial products and will continue to publish legacy transition guidance as it fulfills its mandate. Where necessary, products must move to ARRs.
- Update Business Processes: Based on the impact assessment, various business processes surrounding the management of interest rate changes, including those built into models and systems will require updating to accommodate the switch away from LIBOR. For new products utilizing the new index rate, procedures, processes and policies will need to be established and tested before rollout to clients.
- Manage Change and Communicate: The project team will need to develop educational materials explaining specific changes and their impacts to stakeholders. The materials must be distributed as part of an outreach strategy to external stakeholders, including clients and investors, as well as rating agencies and regulatory bodies. The outreach strategy should help to ensure that the transition message is consistent and clear as it is communicated from executives and board members to operational personnel, other stakeholders and outer spheres of influence.
- Test: Financial institutions will want to prepare for regulatory oversight by testing business processes in advance. Regulators may look for documentation of the processes used to identify and remediate LIBOR risks and any risk exposure that has not been completed.
1Federal Reserve Bank of New York. “Secured Overnight Financing Rate Data.” https://apps.newyorkfed.org/markets/autorates/sofr, Accessed November 2018.
2 Federal Reserve Bank of New York. “ARRC Consultation: Regarding more robust LIBOR fallback contract language for new originations of LIBOR syndicated business loans,” 24 September 2018. https://www.newyorkfed.org/medialibrary/Microsites/arrc/files/2018/ARRC-Syndicated-Business-Loans-Consultation.pdf, Accessed November 2018.
3 Federal Reserve Bank of New York. “Statement Introducing the Treasury Repo Reference Rates,” 3 April 2018. https://www.newyorkfed.org/markets/opolicy/operating_policy_180403, Accessed November 2018.
4Guida, Victoria. “Federal Home Loan Banks boost LIBOR replacement with $4B debt issuance,” Politico. 13 November 2018. https://www.politico.com/story/2018/11/13/federal-home-loan-banks-libor-replacement-939489, Accessed November 2018.
5 CME Group. “Secured Overnight Financing Rate (SOFR) Futures.” https://www.cmegroup.com/trading/interest-rates/secured-overnight-financing-rate-futures.html, Accessed November 2018.
6 Graph: LSTA. “LIBOR and the Loan Market.” 24 April 2018. https://www.lsta.org/uploads/DocumentModel/3523/file/libor-in-the-loan-market_042418.pdf, Accessed November 2018.
7 Federal Reserve Bank of New York. “ARRC Consultation: Regarding more robust LIBOR fallback contract language for new issuances of LIBOR floating rate notes,” 24 September 2018. https://www.newyorkfed.org/medialibrary/Microsites/arrc/files/2018/ARRC-FRN-Consultation.pdf, Accessed November 2018.
8 Federal Reserve Bank of New York. “ARRC Consultation: Regarding more robust LIBOR fallback contract language for new originations of LIBOR syndicated business loans,” 24 September 2018. https://www.newyorkfed.org/medialibrary/Microsites/arrc/files/2018/ARRC-Syndicated-Business-Loans-Consultation.pdf, Accessed November 2018.
16 Federal Reserve Bank of New York. “Minutes,” Alternative Reference Rates Committee (ARRC). 31 October 2017. https://www.newyorkfed.org/medialibrary/microsites/arrc/files/2017/October-31-2017-ARRC-minutes.pdf, Accessed November 2018.

