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.
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.
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.
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.
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.
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.
Validating short-rate models can be challenging because many different ways of modeling how interest rates change over time (“interest rate dynamics”) have been created over the years. Each approach has advantages and shortcomings, and it is critical to distinguish the limitations and advantages of each of them to understand whether the short-rate model being used is appropriate to the task. This can be accomplished via the basic tenets of model validation—evaluation of conceptual soundness, replication, benchmarking, and outcomes analysis. Applying these concepts to short-rate models, however, poses some unique complications.
There are literally thousands of different servicers of Fannie Mae and Freddie Mac pools. Does the servicer make any material difference in the prepayment speed?
Increasing regulatory scrutiny due to the catastrophic risk associated with anti-money-laundering (AML) non-compliance is prompting many banks to tighten up their approach to AML model validation. Because AML applications would be better classified as highly specialized, complex systems of algorithms and business rules than as “models,” applying model validation techniques to them presents some unique challenges that make documentation especially important.
Attribution analysis of portfolios typically aims to discover the impact that a portfolio manager’s investment choices and strategies had on overall profitability. They can help determine whether success was the result of an educated choice or simply good luck. Usually a benchmark is chosen and the portfolio’s performance is assessed relative to it.
This post, however, considers the question of whether a non-referential assessment is possible. That is, can we deconstruct and assess a portfolio’s performance without employing a benchmark? Such an analysis would require access to historical return as well as the portfolio’s weights and perhaps the volatility of interest rates, if some of the components exhibit a dependence on them. This list of required variables is by no means exhaustive.
An instrument’s terms and conditions lie at the heart of cash flow generation and valuation. Not surprisingly, errors in terms and conditions can drive errors in valuation. Fortunately, fixing these errors is often straightforward, provided the terms and conditions data is readily available, which is not always the case for private placement instruments.
Are there differences in the prepayment speeds of various Freddie Mac specified pools?
Watch the short video to find out the answer and to see just how easy this can be done with RS Edge.
In an article published last year, the Harvard Business Review quotes IBM research that estimates that bad data costs US business $3 Trillion per year. Although it is difficult to identify the specific cost associated with bad data in market-risk management, it is obvious that managing data has never been more important.
The success of a market-risk management implementation is largely dependent on a validated, scalable, and well-governed data management process.
The challenge associated with simply gauging the risk associated with “end user computing” applications (EUCs) let alone managing it—is both alarming and overwhelming. Scanning tools designed to detect EUCs can routinely turn up tens of thousands of potential files, even at not especially large financial institutions. Despite the risks inherent in using EUCs for mission-critical calculations, EUCs are prevalent in nearly any institution due to their ease of use and wide-ranging functionality.
Are there performance differences between FHA insured loans and those guaranteed by the VA?
Watch the short video for insights and to see just how easy this can be done with RS Edge.
For many companies, the question is no longer whether to use open source tools, but rather how to implement them with the appropriate governance and controls. Have security concerns been accounted for? How does one effectively institute controls over bad code? Are there legal implications for using open source software?
There are two main challenges when implementing a machine learning solution: building a model that performs well and effectively leveraging the results. Having a good understanding of the machine learning process and model being used is key to tackling both issues. Using a predictive model without appropriately understanding it can substantially increase risk and lead to missed opportunities. If the performance of a model is unclear, misunderstood, or overestimated then subsequent decisions will be biased or outright wrong. Likewise, if the ability of a model is underestimated then its use will not be optimized.
Since the financial crisis began in 2007, the “Non-Agency” MBS market, i.e., securities neither issued nor guaranteed by Fannie Mae, Freddie Mac, or Ginnie Mae, has been sporadic and has not rebounded from pre-crisis levels. In recent months, however, activity by large financial institutions, such as AIG and Wells Fargo, has indicated a return to the issuance of Non-Agency MBS. What is contributing to the current state of the securitization market for high-quality mortgage loans? Does the recent, limited-scale return to issuance by these institutions signal an increase in private securitization activity in this sector of the securitization market? If so, what is sparking this renewed interest?
The GSE NPL sales program gives investors the opportunity to profit from investing in non-performing loans from Fannie Mae and Freddie Mac and maximizing the number of loans they can get to re-perform. Freddie Mac and Fannie Mae (the GSEs) have been selling non-performing loans (NPLs) since 2014 and 2015, respectively, to reduce their holdings of less liquid non-performing assets and gain favorable execution over holding the loans as they have done in the past.
VIENNA, Va., May 15, 2017 – RiskSpan, a data management and predictive analytics firm that specializes in solutions for the mortgage, capital markets, and banking industries, today announced a new release of its loan-level mortgage query tool, RS Edge.