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Articles Tagged with: Prepayment Analytics

RS Edge for Loans & Structured Products: A Data Driven Approach to Pre-Trade and Pricing  

The non-agency residential-mortgage-backed-securities (RMBS) market has high expectations for increased volume in 2020. Driven largely by expected changes to the qualified mortgage (QM) patch, private-label securities (PLS) issuers and investors are preparing for a 2020 surge. The tight underwriting standards of the post-crisis era are loosening and will continue to loosen if debt-to-income restrictions are lifted with changes to the QM patch 

PLS programs can differ greatly. It’s increasingly important to understand the risks inherent in each underlying poolAt the same time, investment opportunities with substantial yield are becoming harder to find without developing a deep understanding of the riskier components of the capital structureA structured approach to pre-trade and portfolio analytics can help mitigate some of these challenges. Using a data-driven approach, portfolio managers can gain confidence in the positions they take and make data influenced pricing decisions 

Industry best practice for pre-trade analysis is to employ a holistic approach to RMBS. To do this, portfolio managers must combine analysis of loan collateral, historical data for similar cohorts of loans (within previous deals), and scenariofor projected performance. The foundation of this approach is:  

  • Historical data can ground assumptions about projected performance 
  • A consistent approach from deal to deal will illuminate shifting risks from shifting collateral 
  • Scenario analysis will inform risk assessment and investment decision  

Analytical Framework 

RiskSpan’s modeling and analytics expert, Janet Jozwik, suggests a framework for analyzing a new RMBS deal with analysis of 3 main components:  deal collateral, historical performance, and scenario forecasting. Combined, these three components give portfolio managers a present, past, and future view into the deal.  

Present: Deal Collateral Analysis 

Deal collateral analysis consists of: 1) a deep dive into the characteristics of the collateral underlying the deal itself, and 2) a comparison of the collateral characteristics of the deal being analyzed to similar deals. A comparison to recently issued deals can highlight shifts in underlying collateral risk within a particular shelf or across issuers.  

Below, RiskSpan’s RS Edge provides the portfolio manager with a dashboard highlighting key collateral characteristics that may influence deal performance. 

Example 1Deal Profile Stratification 


Example 2Deal Comparative Analysis 

Past: Historical Performance Analysis 

Historical analysis informs users of a deal’s potential performance under different scenarios by looking at how similar loan cohorts from prior deals have performedJozwik recommends analyzing historical trends both from the recent past and frohistorical stress vintages to give a sense for what the expected performance of the deal will be, and what the worst-case performance would be under stress scenarios. 

Recent Trend Analysis:  Portfolio managers can understand expected performance by looking at how similar deals have been performing over the prior 2 to 3 years. There are a significant number of recently issued PLS that can be tracked to understand recent prepayment and default trends in the market. While the performance of these recent deals doesn’t definitively determine expectations for a new deal (as things can change, such as rate environment), it provides one data point to help ground data-driven analyses. This approach allows users to capitalize on the knowledge gained from prior market trends.  

Historical Vintage Proxy Analysis:  Portfolio managers can understand stressed performance of the deal by looking at performance of similar loans from vintages that experienced the stress environment of the housing crisisThough potentially cumbersome to execute, this approach leverages the rich set of historical performance data available in the mortgage space 

For a new RMBS Dealportfolio managers can review the distribution of key features, such as FICO, LTV, and documentation typeThey can calculate performance metrics, such as cumulative loss and default rates, from a wide set of historical performance data on RMBS, cut by vintage. When pulling these historical numbers, portfolio managers can adjust the population of loans to better align with the distribution of key loan features in the deal they are analyzing. So, they can get a view into how a similar loans pool originated in historical vintages, like 2007, performed. There are certainly underwriting changes that have occurred in the post-crisis era that would likely make this analysis ultraconservative. These ‘proxy cohorts’ from historical vintages can provide an alternative insight into what could happen in a worst-case scenario.  

Future: Forecasting Scenario Analysis 

Forecasting analysis should come in two flavors. First, very straightforward scenarios that are explicitly transparent about assumptions for CPR, CDR, and severity. These assumptions-based scenarios can be informed with outputs from the Historical Performance Analysis above.  

Second, forecasting analysis can leverage statistical models that consider both loan features and macroeconomic inputs. Scenarios can be built around macroeconomic inputs to the model to better understand how collateral and bond performance will change with changing economic conditions.  Macroeconomic inputs, such as mortgage rates and home prices, can be specified to create particular scenario runs. 

How RiskSpan Can Help 

Pulling the required data and models together is typically a burdenRiskSpan’s RS Edge has solved these issues and now offers one integrated solution for:  

  • Historical Data: Loan-level performance and collateral data on historical and pre-issue RMBS deals 
  • Predictive Models: Credit and Prepayment models for non-agency collateral types 
  • Deal Cashflow Engine: Intex is the leading source for an RMBS deal cashflow library 

There is a rich source of data, models, and analytics that can support decision making in the RMBS market. The challenge for a portfolio manager is piecing these often-disparate pieces of information together to a cohesive analysis that can provide a consistent view from deal to dealFurther, there is a massive amount of historical data in the mortgage space, containing a vast wealth of insight to help inform investment decisions. However, these datasets are notoriously unwieldy. Users of RS Edge cut through the complications of large, disparate datasets for clear, informative analysis, without the need for custom-built technology or analysts with advanced coding skills.

FHFA 3Q2019 Prepayment Monitoring Report

FHFA’s 2014 Strategic Plan for the Conservatorships of Fannie Mae and Freddie Mac includes the goal of improving the overall liquidity of Fannie Mae’s and Freddie Mac’s (the Enterprises) securities through the development of a common mortgage-backed security. This report provides insight into how FHFA monitors the consistency of prepayment rates across cohorts of the Enterprises’ TBA-eligible MBS.

Download Report

Introducing: RS Edge for Loans and Structured Products

RiskSpan Introduces RS Edge for Loans and Structured Products  

RiskSpan, the leading mortgage data and analytics provider, is excited to announce the release of RS Edge for Loans and Structured Products. 

RS Edge is the next generation of RiskSpan’s data, modeling, and analytics platform that manages portfolio risk and delivers powerful analysis for loans and structured products.  Users can derive insights from historical trends and powerful predictive forecasts under a range of economic scenarios on our cloud-native solution. RS Edge streamlines analysis by bringing together key industry data and integrations with leading 3rd party vendors. 

An on-demand team of data scientists, quants, and technologists with fixed-income portfolio expertise support the integration, calibration, and operation across all RS Edge modules 

RMBS Analytics in Action 

RiskSpan has developed a holistic approach to RMBS analysis that combines loan collateral, historical, and scenario analysis with deal comparison tools to more accurately predict future performance. Asset managers can define an acceptable level of risk and ground pricing decisions with data-driven analysis. This approach illuminates risk from shifting collateral and provides investors with confidence in their positions. 

Loan Analytics in Action 

Whole loan asset managers and investors use RiskSpan’s Loan Analytics to enhance and automate partnerships with Non-Qualified Mortgage originators and servicers. The product enhances the on-boarding, pricing analytics, forecasting, and storage of loan data for historical trend analytics. RS Edge forecasting analytics support ratesheet validation and loan pricing 

About RiskSpan 

RiskSpan provides innovative technology and services to the financial services industry. Our mission is to eliminate inefficiencies in loans and structured finance markets to improve investors’ bottom line through incremental cost savings, improved return on investment, and mitigated risk.  

RiskSpan is holding a webinar on November 6 to show how RS Edge pulls together past, present, and future for insights into new RMBS deals. Click below to register.

FHFA 2Q2019 Prepayment Monitoring Report

FHFA’s 2014 Strategic Plan for the Conservatorships of Fannie Mae and Freddie Mac includes the goal of improving the overall liquidity of Fannie Mae’s and Freddie Mac’s (the Enterprises) securities through the development of a common mortgage-backed security. This report provides insight into how FHFA monitors the consistency of prepayment rates across cohorts of the Enterprises’ TBA-eligible MBS.

Download Report

RiskSpan Credit Risk Transfer Solution

RiskSpan Managing Director, Janet Jozwik, explains how the RS Edge Platform serves as an end-to-end Credit Risk Transfer (CRT) solution designed to help investors in each stage of CRT deal analysis. The RS Edge Platform hosts historical GSE data (STACR/CAS/CIRT/ACIS) and gives users the ability to conduct historical and surveillance analysis as well as predictive and scenario analysis. Additionally, RiskSpan gives users full access to our proprietary agency-specific prepayment and credit models and is integrated with Intex for deal cash flow analysis.

CRT Deal Monitor: April 2019 Update

CRT Deal Monitor: Understanding When Credit Becomes Risky 

This analysis tracks several metrics related to deal performance and credit profile, putting them into a historical context by comparing the same metrics for recent-vintage deals against those of ‘similar’ cohorts in the time leading up to the 2008 housing crisis.  

Some of the charts in this post have interactive features, so click around! We’ll be tweaking the analysis and adding new metrics in subsequent months. Please shoot us an email if you have an idea for other metrics you’d like us to track. 

Monthly Highlights: 

The seasonal nature of recoveries is an easy-to-spot trend in our delinquency outcome charts (loan performance 6 months after being 60 days-past-due). Viewed from a very high level, both Fannie Mae and Freddie Mac display this trend, with visible oscillations in the split between loans that end up current and those that become more delinquent (move to 90+ days past due (DPD)). This trend is also consistent both before and after the crisis – the shares of loans that stay 60 DPD and move to 30 DPD are relatively stable. You can explore the full history of the FNMA and FHLMC Historical Performance Datasets by clicking the 6-month roll links below, and then clicking the “Autoscale” button in the top-right of the graph. 

This trend is salient in April of 2019, as both Fannie Mae Connecticut Avenue Securities (CAS) and Freddie Mac Structured Agency Credit Risk (STACR) have seen 6 months of steady decreases in loans curing, and a steady increase in loans moving to 90+ DPD. While both CAS and STACR hit lows for recovery to current – similar to lows at the beginning of 2018 – it is notable that both CAS and STACR saw multi-year highs for recovery to current in October of 2018 (see Delinquency Outcome Monitoring links below). While continued US economic strength is likely responsible for the improved performance in October, it is not exactly clear why the oscillation would move the recoveries to current back to the same lows experienced in early 2018.  

Current Performance and Credit Metrics
Delinquency Trends:

The simplest metric we track is the share of loans across all deals that is 60+ days past due (DPD). The charts below compare STACR (Freddie) vs. CAS (Fannie), with separate charts for high-LTV deals (G2 for CAS and HQA for STACR) vs. low-LTV deals (G1 for CAS and DNA for STACR).

For comparative purposes, we include a historical time series of the share of loans 60+ DPD for each LTV group. These charts are derived from the Fannie Mae and Freddie Mac loan-level performance datasets. Comparatively, today’s deal performance is much better than even the pre-2006 era.

Low LTV Deals 60 DPD
High LTV Deals 60 DPD
Delinquency Outcome Monitoring:

The tables below track the status of loans that were 60+ DPD. Each bar in the chart represents the population of loans that were 60+ DPD exactly 6 months prior to the x-axis date.  

The choppiness and high default rates in the first few observations of the data are related to the very low counts of delinquent loans as the CRT program ramped up.  

STACR 6 Month Roll
CAS 6 Month Roll

The table below repeats the 60-DPD delinquency analysis for the Freddie Mac Loan Level Performance dataset leading up to and following the housing crisis. (The Fannie Mae loan level performance set yields a nearly identical chart.) Note how many more loans in these cohorts remained delinquent (rather than curing or defaulting) relative to the more recent CRT loans.

Fannie Performance 6 Month Roll
Freddie Performance 6 Month Roll
Deal Profile Comparison:

The tables below compare the credit profiles of recently issued deals. We focus on the key drivers of credit risk, highlighting the comparatively riskier features of a deal. Each table separates the high–LTV (80%+) deals from the low–LTV deals (60%-80%). We add two additional columns for comparison purposes. The first is the ‘Coming Cohort,’ which is meant to give an indication of what upcoming deal profiles will look like. The data in this column is derived from the most recent three months of MBS issuance loan–level data, controlling for the LTV group. These are newly originated and acquired by the GSEs—considering that CRT deals are generally issued with an average loan age between 6 and 15 months, these are the loans that will most likely wind up in future CRT transactions. The second comparison cohort consists of 2006 originations in the historical performance datasets (Fannie and Freddie combined), controlling for the LTV group. We supply this comparison as context for the level of risk that was associated with one of the worst–performing cohorts. 

Credit Profile LLTV – Click to see all deals
Credit Profile HLTV – Click to see all deals
Deal Tracking Reports:

Please note that defaults are reported on a delay for both GSEs, and so while we have CPR numbers available for the most recent month, CDR numbers are not provided because they are not fully populated yet. Fannie Mae CAS default data is delayed an additional month relative to STACR. We’ve left loss and severity metrics blank for fixed-loss deals.

STACR Performance – Click to see all deals
CAS Performance – Click to see all deals

FHFA 1Q2019 Prepayment Monitoring Report

FHFA’s 2014 Strategic Plan for the Conservatorships of Fannie Mae and Freddie Mac includes the goal of improving the overall liquidity of Fannie Mae’s and Freddie Mac’s (the Enterprises) securities through the development of a common mortgage-backed security.

This report provides insight into how FHFA monitors the consistency of prepayment rates across cohorts of the Enterprises’ TBA-eligible MBS.

Download Report

Case Study: Risk-as-a-Service

The Client

Portfolio and Risk Management Software Vendor

The Problem

Our client is a Portfolio and Risk Management software vendor and leading provider of on-demand derivative trading analytics, portfolio and risk management solutions for the global financial industry. Its flagship product provides thousands of users worldwide with advanced real-time portfolio and risk management solutions.  The product delivers risk analysis and transparency to funds of funds, institutional investors, asset managers and others that invest across multiple funds and asset classes. By independently sourcing, verifying, aggregating and normalizing the fund level data that is typically not accessible by investors, then applying common sets of risk exposures and risk scenarios, the product provides the framework for comprehensive risk analysis. 

The Solution

This client did not have the capabilities to provide risk services for residential mortgage securities and structured products and could not independently provide risk services to clients that had RMBS investments. The turnaround time on large portfolios of securities was also critical. 

RiskSpan provided a customized solution to support the recipient’s clients with risk analytics. RiskSpan supports clients through a batch risk service that is run overnight on a cloud computing solution leveraging RiskSpan’s Edge platform.  The RiskSpan service is offered to clients as either daily or monthly to meet client needs. This client uses the risk service in their product, a risk aggregator service.  

The Deliverables

RiskSpan provides the risk analysis for structured products. This risk services solution includes: 

  • OAS simulation processing for agency and non-agency securities​
  • Reporting of OAS, OA-Duration, Convexity, Vega, and eight Key Rate Durations​
  • Additional analytics and scenarios that RiskSpan currently supports and will support in the future as part of the Edge Platform can be provided on request via the batch service

Case Study: RS Edge – Analytics and Risk

The Client

Large Life Insurance Company – Investment Group


The Problem

The Client was shopping around for an analytics and risk platform to be used by both the trading desk and risk managers.

RiskSpan Edge Platform enabled highly scalable analytics and risk modeling providing visibility and control to address investment analysis, risk surveillance, stress testing and compliance requirements.

The Solution

Initially, the solution was intended for both the trading desk (as pre-trade analysis) as well as risk management (running scenarios on the existing portfolio).  Ultimately, the system was used exclusively by risk management and used heavily by mid-level risk management. 

Cloud Native Risk Service

We have transformed portfolio risk analytics through distributed cloud computing. Our optimized infrastructure powers risk and scenario analytics at speed and cost never before possible in the industry.

Perform advanced portfolio analysis to achieve risk oversight and regulatory compliance with confidence. Access reliable results with cloud-native interactive dashboards that satisfy investors, regulators, and clients.

Two Flexible Options
Fund Subscriber Service + Managed Service

Each deployment option includes on-demand analytics, standard batch and over-night processing or a hybrid model to suit your specific business needs. Our team will work with customers to customize deployment and delivery formats, including investor-specific reporting requirements.

Easy Integration + Delivery
Access Your Risk

Accessing the results of your risk run is easy via several different supported delivery channels. We can accommodate your specific needs – whether you’re a new hedge fund, fund-of-funds, bank or other Enterprise-scale customer.

“We feel the integration of RiskSpan into our toolkit will enhance portfolio management’s trading capabilities as well as increase the efficiency and scalability of the downstream RMBS analysis processes.  We found RiskSpan’s offering to be user-friendly, providing a strong integration of market / vendor data backed by a knowledgeable and responsive support team.”

The Deliverables

  • Enabled running various HPI scenarios and tweaked the credit model knobs to change the default curve, running a portfolio of a couple hundred non-agency RMBS
  • Scaling the processing power up/down via the cloud, and they would iterate through runs, changing conditions until they got the risk numbers they needed
  • Simplified integration into their risk reporting system, external to RiskSpan

Risk-as-a-Service – Transforming Portfolio Market Risk Analytics

Watch RiskSpan Co-Founder and Chief Technology Officer, Suhrud Dagli, discuss RiskSpan’s Risk-as-a-Service offerings. RiskSpan’s market risk management team has transformed portfolio risk analytics through distributed cloud computing. Our optimized infrastructure powers risk and scenario analytics at speeds and costs never before possible in the industry. Still want more? Take a look at our portfolio market risk analytics page.

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