Conspicuously absent from all the chatter around blockchain’s potential place in structured finance has been much discussion around the thorny matter of consensus. Consensus is at the heart of all distributed ledger networks and is what enables them to function without a trusted central authority.
Reducing time to delivery by developing in smaller incremental chunks and incorporating an ability to pivot is the cornerstone of Agile software development methodology.
Each month RiskSpan uses the RS Edge platform to generate a wide range of prepayment and issuance reports for the FHFA using Fannie Mae and Freddie Mac MBS pool- and loan-level data.
Ten members of RiskSpan's staff and executive team will be attending SFIG Las Vegas from February 25-28th. A sponsor and exhibitor at the show, RiskSpan will be showcasing their technologies including their market risk (RiskDynamic) and loan analytics (RS Edge) solutions at their booth. Suhrud Dagli, RiskSpan's cofounder and CTO will be speaking at a session on Tuesday entitled "Blockchain in Practice: A Series of Case Studies."
Big Data continues to enhance the way that structured finance deals are designed, marketed, and priced. Being able to quickly and accurately gather information to forecast future bond prices will allow experts to efficiently predict future interest rates, levels of inflation, and economic activity.
The benefits of implementing a CECL solution comfortably in advance of adoption are analogous to those of a flight simulator. Both enable professionals to gain insights into how a new system functions and reacts to various influences in a riskless environment. Like a flight simulator, early CECL system implementation enables institutions to move forward with this important standard in a controlled and well-considered manner. It benefits institutions by allowing time for analysis and supporting optimal decision-making.
Questioning the health of the Federal Home Loan Banks simply because their risk is concentrated in mortgages is misguided. The FHLBanks' member advance business is conservatively (over-)collateralized primarily by low-risk, plain vanilla mortgage loans.
The ability of machine learning models to predict loan performance makes them particularly interesting to lenders and fixed-income investors. This expanded post provides an example of applying the machine learning process to a loan-level dataset in order to predict delinquency. The process includes variable selection, model selection, model evaluation, and model tuning.
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.
As 2017 winds down and American households seek to figure out optimal withholding strategies under the new tax law, we've been contemplating which 2017 trends are likely to persist into 2018 and what looks to be new on the horizon. Here are few of them.
Whereas technology projects have typically required significant tradeoffs among speed, quality, and cost containment (“pick any two,” as the old joke goes), DevOps makes all three a possibility. To fulfill our commitment of providing exceptional customer satisfaction within reasonable timeframes and at a reasonable cost, DevOps provided us exactly what we were looking for–a structure to deliver better quality more rapidly.
Analysts and data scientists are constantly seeking new ways to parse increasingly intricate datasets, many of which are deemed “high dimensional”, i.e., contain many (sometimes hundreds or more) individual variables.
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?
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.
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.
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.
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.
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.
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.
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.