Linkedin    Twitter   Facebook

Get Started
Log In

Linkedin

Category: Article
Home / Article /

Mortgage Data and the Cloud – Now is the Time

As the trend toward cloud computing continues its march across an ever-expanding set of industries, it is worth pausing briefly to contemplate how it can benefit those of us who work with mortgage data for a living.  

The inherent flexibility, efficiency and scalability afforded by cloud-native systems driving this trend are clearly of value to users of financial services data. Mortgages in particular, each accompanied by a dizzying array of static and dynamic data about borrower incomes, employment, assets, property valuations, payment histories, and detailed loan terms, stand to reap the benefits of cloud and the shift to this new form of computing.  

And yet, many of my colleagues still catch themselves referring to mortgage data files as “tapes.” 

Migrating to cloud evokes some of the shiniest words in the world of computing – cost reduction, security, reliability, agility – and that undoubtedly creates a stir. Cloud’s ability to provide on-demand access to servers, storage locations, databases, software and applications via the internet, along with the promise to ‘only pay for what you use’ further contributes to its popularity. 

These benefits are especially well suited to mortgage data. They include:  

  • On-demand self-service and the ability to provision resources without human interference – of particular use for mortgage portfolios that are constantly changing in both size and composition. 
  • Broad network access, diverse platforms having access to multiple resources available over the network – valuable when origination, secondary marketing, structuring, servicing, and modeling tools are seeking to simultaneously access the same evolving datasets for different purposes. 
  • Multi-tenancy and resource pooling, allowing resource sharing while maintaining privacy and security. 
  • Rapid elasticity and scalability, quick acquiring and disposing of resources and allowing quick but measured scaling based on demand. 

Cloud-native systems reduce ownership and operational expenses, increase speed and agility, facilitate innovation, improve client experience, and even enhance security controls. 

There is nothing quite like mortgage portfolios when it comes to massive quantities of financial data, often PII-laden, with high security requirements. The responsibility for protecting borrower privacy is the most frequently cited reason for financial institution reluctance when it comes to cloud adoption. But perhaps counterintuitively, migrating on-premises applications to cloud actually results in a more controlled environment as it provides for backup and access protocols that are not as easily implemented with on-premise solutions. 

The cloud affords a sophisticated and more efficient way of securing mortgage data. In addition to eliminating costs associated with running and maintaining data centers, the cloud enables easy and fast access to data and applications anywhere and at any time. As remote work takes hold as a more long-term norm, cloud-native platform help ensure employees can work effectively regardless of their location. Furthermore, the scalability of cloud-native data centers allows holders of mortgage assets to grow and expand storage capabilities as the portfolio grows and reduce it when it contracts. The cloud protects mortgage data from security breaches or disaster events, because the loan files are (by definition) backed up in a secure, remote location and easily restored without having to invest in expensive data retrieval methods.  

This is not to say that migrating to the cloud is without its challenges. Entrusting sensitive data to a new third-party partner and relying on its tech to remain online will always carry some measure of risk. Cloud computing, like any other innovation, comes with its own advantages and disadvantages, and redundancies mitigate virtually all of these uncertainties. Ultimately, the upside of being able work with mortgage data on cloud-native solutions far outweighs the drawbacks. The cloud makes it possible for processes to become more efficient in real-time, without having to undergo expensive hardware enhancements. This in turn creates a more productive environment for data analysts and modelers seeking to give portfolio managers, servicers, securitizers, and others who routinely deal with mortgage assets the edge they are looking for.

Kriti Asrani is an associate data analyst at RiskSpan.

Want to read more on this topic? Check out COVID-19 and the Cloud.

Will a Rising VQI Materially Impact Servicing Costs and MSR Valuations?

VQI-GraphVQI-Current-Layers-September-2021

RiskSpan’s Vintage Quality Index computes and aggregates the percentage of Agency originations each month with one or more “risk factors” (low-FICO, high DTI, high LTV, cash-out refi, investment properties, etc.). Months with relatively few originations characterized by these risk factors are associated with lower VQI ratings. As the historical chart above shows, the index maxed out (i.e., had an unusually high number of loans with risk factors) leading up to the 2008 crisis.

RiskSpan uses the index principally to fine-tune its in-house credit and prepayment models by accounting for shifts in loan composition by monthly cohort.

Will a rising VQI translate into higher servicing costs?

The Vintage Quality Index continued to climb during the third quarter of 2021, reaching a value of 85.10, compared to 83.40 in the second quarter. The higher index value means that a higher percentage of loans were originated with one or more defined risk factors.

The rise in the index during Q3 was less dramatic than Q2’s increase but nevertheless continues a trend going back to the start of the pandemic. The increase continues to be driven by a subset of risk factors, notably the share of cash-out refinances and investor properties (both up significantly) and high-DTI loans (up modestly). On balance, fewer loans were characterized by the remaining risk metrics.

What might this mean for servicing costs?

Servicing costs are highly sensitive to loan performance. Performing Agency loans are comparatively inexpensive to service, while non-performing loans can cost thousands of dollars per year more — usually several times the amount a servicer can expect to earn in servicing fees and other ancillary servicing revenue.

For this reason, understanding the “vintage quality” of newly originated mortgage pools is an element to consider when forecasting servicing cash flows (and, by extension, MSR pricing).

Each of the risk layers that compose the VQI contributes to marginally higher default risk (and, therefore, a theoretically lower servicing valuation). But not all risk layers affect expected cash flows equally. It is also important to consider the VQI in relationship to its history. While the index has been rising since the pandemic, it remains relatively low by historical standards — still below a local high in early 2018 and certainly nowhere near the heights reached leading up to the 2008 financial crisis.

A look at the individual risk metrics driving the increase would also seem to reduce any cause for alarm. While the ever-increasing number of loans with high debt-to-income ratios could be a matter of some concern, the other two principal contributors to the overall VQI rise — loans on investment properties and cash-out refinances — do not appear to jeopardize servicing cash flows to the same degree as low credit scores and high DTI ratios do.

Consequently, while the gradual increase in loans with one or more risk factors bears watching, it likely should not have a significant bearing (for now) on how investors price Agency MSR assets.

VQI-Risk-Layer-All-Issued-Loans-September-2021VQI-Risk-Layers-FICO-660-September-2021

VQI-LTV-80-Shared-of-Issued-Loans-September-2021 VQI-Debt-to-Income-45-Share-of-Issued-Loans-September-2021 VQI-Adjustabel-Rate-Share-of-issued-Loans-September-2021 VQI-Loans-with-Subordinate-Financing-September-2021-1024x399.png

Population assumptions:

  • Monthly data for Fannie Mae and Freddie Mac.
  • Loans originated more than three months prior to issuance are excluded because the index is meant to reflect current market conditions.
  • Loans likely to have been originated through the HARP program, as identified by LTV, MI coverage percentage, and loan purpose, are also excluded. These loans do not represent credit availability in the market as they likely would not have been originated today but for the existence of HARP.

Data assumptions:

  • Freddie Mac data goes back to 12/2005. Fannie Mae only back to 12/2014.
  • Certain fields for Freddie Mac data were missing prior to 6/2008.

GSE historical loan performance data release in support of GSE Risk Transfer activities was used to help back-fill data where it was missing.

An outline of our approach to data imputation can be found in our VQI Blog Post from October 28, 2015.

Senior Housing Wealth Exceeds Record $9.57 Trillion

Homeowners 62 and older saw their housing wealth grow by 3.7 percent in the second quarter to a record $9.57 trillion, according to the latest quarterly release of the NRMLA/RiskSpan Reverse Mortgage Market Index.

The NRMLA/RiskSpan Reverse Mortgage Market Index (RMMI) rose in Q2 2021 to 334.71, another all-time high since the index was first published in 2000. The increase in older homeowners’ wealth was mainly driven by an estimated 3.4 percent increase in home values, offset by a 2.2 percent increase in senior-held mortgage debt.

For a comprehensive commentary, please see NRMLA’s press release.

How RiskSpan Computes the RMMI

To calculate the RMMI, RiskSpan developed an econometric tool to estimate senior housing value, mortgage balances, and equity using data gathered from various public resources. These resources include the American Community Survey (ACS), Federal Reserve Flow of Funds (Z.1), and FHFA housing price indexes (HPI). The RMMI represents the senior equity level at time of measure relative to that of the base quarter in 2000.[1] 

A limitation of the RMMI relates to Non-consecutive data, such as census population. We use a smoothing approach to estimate data in between the observable periods and continue to look for ways to improve our methodology and find more robust data to improve the precision of the results. Until then, the RMMI and its relative metrics (values, mortgages, home equities) are best analyzed at a trending macro level, rather than at more granular levels, such as MSA.

[1] There was a change in RMMI methodology in Q3 2015 mainly to calibrate senior homeowner population and senior housing values observed in 2013 American Community Survey (ACS).

How Are Ginnie’s New RG Pools Performing?

In February of this year, the Ginnie Mae II program began guaranteeing securities backed by pools of mortgages previously bought out of Ginnie Mae securities because of delinquency. In order to qualify for these new re-performing pools (known as “RG pools”) a loan must meet two (related) conditions: 

  • Borrower has made at least six months of timely payments prior to pool issuance. 
  • Pool issue date is at least 210 days from when the mortgage was last delinquent. 

The novelty of RG pools raises questions about their composition and performance relative to other Ginnie Mae pools. While it remains too early to make many conclusive statements, a preliminary look at the prepayment data indicates speeds somewhere between those of similar vintage Ginnie Mae multi and custom pools, with typical variability from servicer to servicer.  

In this post, we discuss the prepayment behaviors we have observed over the first seven months of RG pool securitization, issuance patterns, and collateral characteristics. 

Prepayments 

Latest September prepayment prints show that RG pools’ speeds generally fell in between those of similar coupon/vintage multi and custom pools.  Below charts shows that 2015/2016 3.5% RG pools prepaid at around 37-38 CPR in September, a couple of CPR slower than similarly aged multi pools and almost 10 CPR faster than custom pools.  

Prepayments for G2 3.5% RG, Custom and Multi Pools by Vintages, September Factor Month Prepayments for G2 3.5% RG Custom and Multi Pools by vintages, Sept FactorMonthNote: Loan level data

Below, we plot S-curves for 49 to 72 wala RG loans against S-curves for similarly aged multi and other custom loans from April to September factor months.  Speeds for RG loans with 25 to 100 bp of rate incentives have prepaid in mid-30s CPRs (Green line in below figure).  During the same period, similar multi pools have prepaid 5 to 8 CPR faster (blue line) than RG pools while similar custom pools have prepaid around 5 CPR slower (black line).  We also overlaid a s-curve for 7 to 18 wala G2 multi pools as a comparison (orange line).

S-curves for RG, Custom and Multi Pools (49 to 72 WALA) April to September Factor Months 
GNMA PoolNote: Loan level data, orange line is the s-curve for 7-18 wala G2 multi pools with a one-year lookback period 

Not surprisingly, prepayment behavior differs by servicer. Wells-serviced RG pools that are seasoned 49 to 72 months with 25 to 100 bp of rate incentives appear to be prepaying in low 30s CPRs (black line in below figure).  Similar loans from Penny Mac are prepaying 5 to 10 CPR faster, which tends to be the case for non-RG loans as well. 

S-curves for RG loans by servicers, 49 to 72 WALA, April to September Factor MonthsGNMA PoolsNote: Loan level data 

While the re-performing loans that are being securitized into RG pools are already seasoned loans, prepayments have been increasing as pool seasons.  For example, one-month old RG 3.5% pools have prepaid at 27 CPR while 6- and 7-month 3.5% pools prepaid at 45-50 CPR (black line below). In addition, overall prepayment speeds for same-pool-age 3.0%, 3.5%, and 4.0% have been on top of each other. 

 Prepayments for RG 3.0%, 3.5% and 4.0% Pools by Pool Age, March to September 2021 GNMA PoolsNote: only showing data points for cohorts with more than 50 loans

Issuance Volume 

Following a brief ramp-up period in February and March, issuance of RG pools has averaged around $2 billion (and roughly 300 pools) per month for the past five months (see Issuance chart below). The outstanding UPB of these pools stands at nearly $11 billion as of the September factor month. 

GNMA PoolsNote: RiskSpan uses reporting month as a factor month. For this chart, we adjust our factor date by one month to match the collection period.

RG pools already account for a sizable share of Ginnie II custom issuance, as illustrated in the following chart, making up 18% of G2 custom issuance and 3% of all G2 issuance since April.

GNMA PoolsNote: RiskSpan uses reporting month as a factor month. For this chart, we adjust our factor date by one month to match the collection period. 

RG Pool Characteristics 

Nearly all of RG pool issuance has been in 3.0% to 4.5% coupons, with a plurality at 3.5%. As of the September factor month, almost $4 billion (37%) of the outstanding RG pools are in 3.5% coupons. The 4% coupon accounted for the next-largest share–$2.5 billion (23%)—followed by $2.3 billion in 3.0% (20.9%) and $1.3 billion in 4.5% (11.8%). 

RG Pool Outstanding Amount by Coupon — September Factor Month GNMA Pools

 The following table compares the characteristics of RG pools issued since February with those of G2 single-family custom and multi pools issued during the same period.  The table highlights some interesting differences: 

  • Issuance of RG pools seems to be concentrated in higher coupons (3% to 4%) compared to issuances for G2 custom pools (concentrated on 2.5% and 3.0%) and G2 multi-lender pools (concentrated on 2.0% and 2.5%). 
  • Loan sizes in RG pools tend to fall between those of G2 customs and smaller than G2 multis.  For example, WAOLS for 3.5% RG pools is around 245k and is around 50k smaller than multi pools and 30k larger than other custom pools. 
  • RG pools consist almost exclusively of FHA loans while G2 multis have a much higher share of VA loans.  Almost 98% of 3.5% RG loans are FHA loans. 

 G2 RG vs. G2 Custom and G2 Multi (pools issued since February), Stat as of September Factor Month GNMA Pools

Wells Fargo and Penny Mac are far and away the leaders in RG issuance, accounting collectively for 62% of outstanding RG pools.  

RG Pools by Servicer, September Factor Month GNMA Pools

 How to Run RG Pools in Edge Perspective 

Subscribers to Edge Perspective can run these comparisons (and countless others) themselves using the “GN RG” pool type filter. The “Custom/Multi-lender” filter can likewise be applied to separate those pools in G2SF. 

Contact Us

Contact us if you are interested in seeing variations on this theme. Using Edge, we can examine any loan characteristic and generate an S-curve, aging curve, or time series.

Value Opportunities in Private-Label Investor Loan Deals

The supply of investor loan collateral in private securitizations has surged in 2021 and projects to remain high (more on this below). To gain an informational edge while selecting bonds among this new issuance, traders and investors have asked RiskSpan for data and tools to dissect the performance of investor loans. Below, we first show the performance of investor loans compared to owner-occupied loans, and then offer a glimpse into a few relative value opportunities using our data and analytics platform, Edge.

As background, the increase of investor loan collateral in PLS was spurred by a new FHFA policy, recently suspended, that capped GSE acquisitions of investor and second home loans at 7% of seller volume. This cap forced originators to explore private-label securitization which, while operationally more burdensome than GSE execution, has been more profitable because it bypasses the GSEs’ high loan-level pricing adjustments. Now that this difficult but rewarding PLS path has been more widely traveled, we expect it to become more efficient and to remain popular, even with the GSE channel reopening.

Subsector Performance Comparison: Investor Vs. Owner-Occupied Loans

Investor Loans Promise Longer Collection of Above-Market Rates

Compared to owner-occupants, investors have historically paid above-market mortgage rates for longer periods before refinancing. Figure 1 shows the prepayment rates of investors vs. owner-occupants as a function of refinance incentive (the borrower’s note rate minus the prevailing mortgage rate). As their flatter “s-curve” shows, the rise in investor prepayments as refinance incentive increases is much more subdued than for owner-occupants.

Crucially, this relationship is not fully explained by higher risk-based pricing premiums on investor loans. Figure 2 shows the same comparison as Figure 1 but only for loans with spreads at origination (SATO) between 50 and 75 bps. The categorical difference between owner-occupied and investor prepay speeds is partially reduced but clearly remains. We also tried controlling for property type, but the difference persists. The relative slowness of investors may result from investors spreading their attention across many elements of their P&L besides interest expense, from higher underwriting obstacles for a rental income-driven loan, and/or from lenders limiting allocation of credit to the investor type.

While we plot these graphs over a five-year lookback period to balance desires for recency and sample size, this relationship holds over shorter and longer performance periods as well.

Figure 1: The Investor Loans S-Curve is Significantly Flatter Than the Owner-Occupied Curve
Investor s-curve vs. owner-occupied s-curve. Includes prime credit, no prepayment penalty, original loan size $200K-$400K, ages 6-48 months for the past 5yr period performance.

The Investor Loans S-Curve is Significantly Flatter Than the Owner-Occupied Curve

Source: CoreLogic’s Private-Label RMBS Collateral Dataset, RiskSpan. Note: because the increase in private-label investor loan volume is coming from Agency cutbacks, the historical performance of investor loans within both Agency and private-label datasets are relevant to private-label investor loan future performance. In this analysis we show private-label data because it straightforwardly parses voluntary prepays vs. defaults, which of course is a critical distinction for PL RMBS investors. Nonetheless, where applicable, we have run the analyses in both datasets, each of which corroborates the performance patterns we show.

Figure 2: Even Controlling for SATO, The Investor vs. Owner-Occupied S-Curve Difference Persists Even Controlling for SATO, The Investor vs. Owner-Occupied S-Curve Difference Persists Same as Figure 1 but includes only loans with SATO between 50-75 bps Source: CoreLogic, RiskSpan

Investor Loans Pose Comparable Baseline Risk, Greater Downside Risk to Credit Investors

Credit performance of investor loans has been worse than owner-occupied loans during crises, which justifies a pricing premium. During benign periods, investor loans have defaulted at similar or lower rates than owner-occupied loans – presumably due to more conservative LTVs, FICOs and DTIs among the investor loan type – and have therefore been profitable for credit investors during these periods. See Figure 3.

Figure 3: Investor Loans Have Defaulted at Greater Rates During Crises and Similar Rates in Other Periods vs. Owner-Occupied Loans Default rates over time, investor loans vs. owner-occupied. Includes prime credit, ages 12-360 months. Investor Loans Have Defaulted at Greater Rates During Crises and Similar Rates in Other Periods vs. Owner-Occupied Loans Source: CoreLogic, RiskSpan

Relative Value Opportunities Within Investor Loans

California Quicker to Refinance California has the largest share of U.S. investor mortgages, as it does with all residential mortgages. California borrowers, both investors and owner-occupieds, have exhibited a steeper response to refinance incentives than have borrowers in other states. Figure 4 shows the comparison focusing on investors. While historical home price appreciation has enabled refinances in California, it has done the same in many states. Therefore, the speed differences point to a more active refinance market in California. All else equal, then, RMBS investors will prefer less California collateral.

Figure 4: California Prepays Significantly Faster In the Money Investor s-curves bucketed by geography (California vs. Other). Includes prime credit, no prepayment penalty, original loan size $200k-$400k, ages 6-48 months for the past 3yr performance period. California Prepays Significantly Faster In the Money Source: CoreLogic, RiskSpan

For AAA Investors, Limited-Doc Investor Loans May Offer a Two-Sided Benefit: They Buoy Premium Bonds, and a Small Sample Suggests They Lift Discount Bonds, Too

Limited-doc investor loans offer senior tranche holders the chance to earn above-market rates for longer than full-doc investor loans, a relative edge for premium bonds (Figure 5). This is intuitive; we would expect limited-doc borrowers to face greater obstacles to refinancing. This difference holds even controlling for spread at origination. Based on a smaller sample, limited-doc investor loans have also turned over more (see greater prepay rates in the negative refinance incentive bucket). This may result from a correlation between limited documentation and more rapid flipping into the rising HPI environment we have had nationally throughout the past seven years. If so, this would mean that limited-doc investor loans also help discount bonds, relative to full-doc investor loans, accelerate repayments at par.

Because limited-doc investor loans are rare in the RMBS 2.0 era, we widened the performance period to the past seven years to get some sample in each of the refinance incentive buckets. Nonetheless, with all the filters we have put on to isolate the effect of documentation type, there are only a few hundred limited-doc investor loans in the negative refinance incentive buckets.

Figure 5: Limited-Doc Investor Loans Have Prepaid Slower In-The-Money and Faster Out-of-the-Money Investor s-curves bucketed by doc type. Includes prime credit, no prepayment penalty, original loan size $400K-$800K, ages 6-48 months, SATO 25-125bps for the past 7yr performance period. Limited-Doc Investor Loans Have Prepaid Slower In-The-Money and Faster Out-of-the-Money Source: CoreLogic, RiskSpan

Size Affects Refi Behavior – But Not How You Think

An assumption carried over from Agency performance is that rate-driven prepays get likelier as loan size increases. This pattern holds across conforming loan sizes, but then reverses and refinance response gets flatter again as balances cross $800K. This is true for investor and owner-occupied loans in both Agency and private-label loan data, though of course the number of loans above $800K in the Agency data is small. Figure 6 shows this pattern for private-label investor loans. As shown, in-the-money prepayments are slowest among loans below $200K, as we would expect. But despite their much higher motivation to refinance, loans above $800K have similar S-curves to loans of just $200K-$400K.

The SATO is generally a few basis points higher for these largest loans, but this does not explain away the speed differences. Figure 7 shows the same comparison as Figure 6 except only for loans with SATO between 50-75 bps. Except for a slightly choppier graph because of the reduced sample size, the same rank-ordering is evident. Nor does controlling for property type or geography remove the speed differences. The largest loans, we conclude, have fewer credit alternatives and/or face more stringent underwriting hurdles than smaller loans, hampering their refi rates.

Rate refinances are fastest among the mid-sized loans between $400K-$600K and $600K-$800K. That these last two groups have similar S-curves – despite the greater dollar motivation to refinance for the $600K-$800Kgroup – suggests that the countervailing effect of lower ability to find refinancing outlets is already kicking in for the $600K-$800K size range.

All of this means that high-balance collateral should be more attractive to investors than some traditional prepayment models will appreciate.

Figure 6: The Largest Investor Loans Refinance Slower Than Medium-Sized
Investor s-curves bucketed by loan size. Includes prime credit, no prepayment penalty, ages 6-48 months for the past 5yr performance period.

The Largest Investor Loans Refinance Slower Than Medium-Sized

Source: CoreLogic, RiskSpan

Figure 7: Controlling For SATO, Largest Investor Loans Still Refinance Slower Than Medium-Sized
Same as Figure 4 but includes only loans with SATO between 50-75 bps

Controlling For SATO, Largest Investor Loans Still Refinance Slower Than Medium-Sized

Source: CoreLogic, RiskSpan

Preliminarily, Chimera Has Lowest Stressed Delinquencies of Top Investor Shelves

For junior-tranche, credit-exposed investors in the COVID era, 60-day-plus delinquencies have been significantly rarer on Chimera’s shelf than on other top investor shelves. The observable credit mixes of the three shelves appear similar. We ran this analysis with only full-doc loans and from only one state (California), and the rank-ordering of delinquency rates by shelves remains the same. Further to this point, note that the spread at origination of Chimera’s shelf is nearly as high as Flagstar’s. All of this suggests there is something not directly observable about Chimera’s shelf that has generated better credit performance during this stressed period. We caution that differences in servicer reporting of COVID forbearances can distort delinquency data, so we will continue to monitor this performance as our data updates each month.

Figure 8: Chimera Posts Lowest COVID Delinquencies, with Nearly Highest SATO of Top Investor Shelves
Investor DQ60+ rates over time, bucketed by shelf. Includes prime credit, ages 12-60 months.

Chimera Posts Lowest COVID Delinquencies, with Nearly Highest SATO of Top Investor Shelves
Source: CoreLogic, RiskSpan

The Greater Default Risk of Low-Doc Investor Loans Lasts About 10 Years

Low-doc investors default more frequently than full-doc investors, but only during the first roughly 120 months of loan age. Around this age, the default rates converge. For loans seasoned beyond this age, full-doc loans begin to default slightly more frequently than low-doc loans, likely due to a survivorship bias. This suggests that credit investors are wise to require a price discount for new issuance with low-doc collateral. For deals with heavily seasoned collateral, junior-tranche investors may counterintuitively prefer low-doc collateral — certainly if they can earn an extra risk premium for it, as it would seem they are not actually bearing any extra credit risk.

Figure 9: Low-Doc Investor Loans Default More Frequently Than Full-Doc Until Loan Age = 120
Investor default rates over time, bucketed by doc type. Includes prime credit, RMBS 2.0 era, for the past 7yr performance period.

Low-Doc Investor Loans Default More Frequently Than Full-Doc Until Loan Age = 120 Source: CoreLogic, RiskSpan

Summary

  • Investor loans face higher barriers to refinance than owner-occupied, offering RMBS investors the opportunity to earn higher coupons for longer periods.
  • For junior tranche investors, the credit performance of investor loans has been similar to owner-occupied loans during benign economic periods and worse during stressed times.
  • California borrowers respond more quickly to refinance incentives than borrowers from other states; investors will prefer less California collateral.
  • Limited-doc investor loans offer AAA investors a double benefit: slower refinances in the money, extending premium bonds; and faster turnover out of the money, limiting extension risk.
  • Low loan balances are attractive for their slow refinance response – as are non-conforming (high) loan balances above $800K. Traditional prepay models may miss this latter dynamic.
  • For credit investors, Chimera’s delinquency rates have been significantly better during the pandemic than other investor shelves. We will continue to monitor this as different ways of reporting COVID forbearances may confound such comparisons.
  • For credit investors, limited-doc investor loans default at higher rates than full-doc loans for about the first ten years of loan age; after this point the two perform very similarly, with limited-doc loans defaulting at slightly lower rates among these seasoned loans, likely due to survivor biases.

Contact Us

Contact us if you are interested in seeing variations on this theme. Using Edge, we can examine any loan characteristic and generate an S-curve, aging curve, or time series.

An Emerging Climate Risk Consensus for Mortgages?

That climate change poses a growing—and largely unmeasured—risk to housing and mortgage investors is not news. As is often the case with looming threats whose timing and magnitude are only vaguely understood, increased natural hazard risks have most often been discussed anecdotally and in broad generalities. This, however, is beginning to change as the reality of these risks becomes increasingly clear to an increasing number of market participants and industry-sponsored research begins to emerge.

This past week’s special report by the Mortgage Bankers Association’s Research Institute for Housing America, The Impact of Climate Change on Housing and Housing Finance, raises a number of red flags about our industry’s general lack of preparedness and the need for the mortgage industry to take climate risk seriously as a part of a holistic risk management framework. Clearly this cannot happen until appropriate risk scenarios are generated and introduced into credit and prepayment models.

One of the puzzles we are focusing on here at RiskSpan is an approach to creating climate risk stress testing that can be easily incorporated into existing mortgage modeling frameworks—at the loan level—using home price projections and other stress model inputs already in use. We are also partnering with firms who have been developing climate stress scenarios for insurance companies and other related industries to help ensure that the climate risk scenarios we create are consistent with the best and most recently scientific research available.

Also on the short-term horizon is the implementation of FEMA’s new NFIP premiums for Risk Rating 2.0. Phase I of this new framework will begin applying to all new policies issued on or after October 1, 2021. (Phase II kicks in next April.) We wrote about this change back in February when these changes were slated to take effect back in the spring. Political pressure, which delayed the original implementation may also impact the October date, of course. We’ll be keeping a close eye on this and are preparing to help our clients estimate the likely impact of FEMA’s new framework on mortgages (and the properties securing them) in their portfolios.

Finally, this past week’s SEC statement detailing the commission’s expectations for climate-related 10-K disclosures is also garnering significant (and warranted) attention. By reiterating existing guidelines around disclosing material risks and applying them specifically to climate change, the SEC is issuing an unmistakable warning shot at filing companies who fail to take climate risk seriously in their disclosures.

Contact us (or just email me directly if you prefer) to talk about how we are incorporating climate risk scenarios into our in-house credit and prepayment models and how we can help incorporate this into your existing risk management framework.  

Prepayment Spikes in Ida’s Wake – What to Expect

It is, of course, impossible to view the human suffering wrought by Hurricane Ida without being reminded of Hurricane Katrina’s impact 16 years ago. Fortunately, the levees are holding and Ida’s toll appears likely to be less severe. It is nevertheless worth taking a look at what happened to mortgages in the wake of New Orleans’s last major catastrophic weather event as it is reasonable to assume that prepayments could follow a similar pattern (though likely in a more muted way).

Following Katrina, prepayment speeds for pools of mortgages located entirely in Louisiana spiked between November 2005 and June 2006. As the following graph shows, prepayment speeds on Louisiana properties (the black curve) remained elevated relative to properties nationally (the blue curve) until the end of 2006. 

Comparing S-curves of Louisiana loans (the black curve in the chart below) versus all loans (the green curve) during the spike period (Nov. 2005 to Jun. 2006) reveals speeds ranging from 10 to 20 CPR faster across all refinance incentives. The figure below depicts an S-curve for non-spec 100% Louisiana pools and all non-spec pools with a weighted average loan age of 7 to 60 months during the period indicated.

The impact of Katrina on Louisiana prepayments becomes even more apparent when we consider speeds prior to the storm. As the S-curves below show, non-specified 100% Louisiana pools (the black curve) actually paid slightly slower than all non-spec pools between November 2003 and October 2005.

As we pointed out in June, a significant majority of prepayments caused by natural disaster events are likely to be voluntary, as opposed to the result of default as one might expect. This is because mortgages on homes that are fully indemnified against these perils are likely to be prepaid using insurance proceeds. This dynamic is reflected in the charts below, which show elevated voluntary prepayment rates running considerably higher than the delinquency spike in the wake of Katrina. We are able to isolate voluntary prepayment activity by looking at the GSE Loan Level Historical Performance datasets that include detailed credit information. This enables us to confirm that the prepay spike is largely driven by voluntary prepayments. Consequently, recent covid-era policy changes that may reduce the incidence of delinquent loan buyouts from MBS are unlikely to affect the dynamics underlying the prepayment behavior described above.

RiskSpan’s Edge Platform enables users to identify Louisiana-based loans and pools by drilling down into cohort details. The example below returns over $1 billion in Louisiana-only pools and $70 billion in Louisiana loans as of the August 2021 factor month.

Edge also allows users to structure more specified queries to identify the exposure of any portfolio or portfolio subset. Edge, in fact, can be used to examine any loan characteristic to generate S-curves, aging curves, and time series.  Contact us to learn more.

EDGE: QM vs Non-QM Prepayments

Prepayment speeds for qualified mortgages (QM loans) have anecdotally been faster than non-QM loans. For various reasons, the data necessary to analyze interest rate incentive response has not been readily available for these categories of mortgages.

In order to facilitate the generation of traditional refinancing curves (S-curves) over the last year, we have normalized data to improve the differentiation of QM versus non-QM loans within non-agency securities.

Additionally, we isolated the population to remove prepay impact from loan balance and seasoning.

The analysis below was performed on securitized loans with 9 to 36 months of seasoning and an original balance between 200k and 500k. S-curves were generated for observation periods from January 2016 through July 2021.

Results are shown in the table and chart below.

Edge-QM-vs-Non-QM-Refi-Incentive

Edge-QM-vs-Non-QM-Refi-Incentive

For this analysis, refinance incentive was calculated as the difference between mortgage note rate and the 6-week lagged Freddie Mac primary mortgage market survey (PMMS) rate. Non-QM borrowers would not be able to easily refi into a conventional mortgage. We further analyzed the data by examining prepayments speeds for QM and non-QM loans at different level of SATO. SATO, the spread at origination, is calculated as the difference between mortgage note rate and the prevailing PMMS rate at time of loan’s origination.

Edge-QM-vs-Non-QM-Refi-Incentive

Using empirical data maintained by RiskSpan, it can be seen the refinance response for QM loans remains significantly faster than Non-QM loans.

Using Edge, RiskSpan’s data analytics platform, we can examine any loan characteristic and generate S-curves, aging curves, and time series. If you are interested in performing historical analysis on securitized loan data, please contact us for a free demonstration.

Managing Market Risk for Crypto Currencies

 

Contents

Overview

Asset Volatility vs Asset Sensitivity to Benchmark (Beta)

Portfolio Asset Covariance

Value at Risk (VaR)

Bitcoin Futures: Basis and Proxies

Intraday Value at Risk (VaR)

Risk-Based Limits

VaR Validation (Bayesian Approach)

Scenario Analysis

Conclusion

Overview

Crypto currencies have now become part of institutional investment strategies. According to CoinShares, assets held under management by crypto managers reached $57B at the end of Q1 2021.  

Like any other financial asset, crypto investments are subject to market risk monitoring with several approaches evolving. Crypto currencies exhibit no obvious correlation to other assets classes, risk factors  or economic variables. However, crypto currencies have exhibited high price volatility and have enough historical data to implement a robust market risk process. 

In this paper we discuss approaches to implementing market risk analytics for a portfolio of crypto assets. We will look at betas to benchmarks, correlations, Value at Risk (VaR) and historical event scenarios. 

Value at Risk allows risk managers to implement risk-based limits structures, instead of relying on traditional notional measures. The methodology we propose enables consolidation of risk for crypto assets with the rest of the portfolio. We will also discuss the use of granular time horizons for intraday limit monitoring. 

Asset Volatility vs Asset Sensitivity to Benchmark (Beta)

For exchange-traded instruments, beta measures the sensitivity of asset price returns relative to a benchmark. For US-listed large cap stocks, beta is generally computed relative to the S&P 500 index. For crypto currencies, several eligible benchmark indices have emerged that represent the performance of the overall crypto currency market.

We analyzed several currencies against S&P’s Bitcoin Index (SPBTC). SPBTC is designed to track the performance of the original crypto asset, Bitcoin. As market capitalization for other currencies grows, it would be more appropriate to switch to a dynamic multi-currency index such as Nasdaq’s NCI. At the time of this paper, Bitcoin constituted 62.4% of NCI.

Traditionally, beta is calculated over a variable time frame using least squares fit on a linear regression of benchmark return and asset return. One of the issues with calculating betas is the variability of the beta itself. In order to overcome that, especially given the volatility of crypto currencies, we recommend using a rolling beta.

Due to the varying levels of volatility and liquidity of various crypto currencies, a regression model may not always be a good fit. In addition to tracking fit through R-squared, it is important to track confidence level for the computed betas.

Crypto-VolitilityFigure 1 History of Beta to S&P Bitcoin Index with Confidence Intervals

The chart above shows rolling betas and confidence intervals for four crypto currencies between January 2019 and July 2021. Beta and confidence interval both vary over time and periods of high volatility (stress) cause a larger dislocation in the value of beta.

Rolling betas can be used to generate a hierarchical distribution of expected asset values.

Portfolio Asset Covariance

Beta is a useful measure to track an asset’s volatility relative to a single benchmark. In order to numerically analyze the risk exposure (variance) of a portfolio with multiple crypto assets, we need to compute a covariance matrix. Portfolio risk is a function not only of each asset’s volatility but also of the cross-correlation among them.

Crypto-PortfolioFigure 2 Correlations for 11 currencies (calculated using observations from 2021)

The table above shows a correlation matrix across 11 crypto assets, including Bitcoin.

Like betas, correlations among assets change over time. But correlation matrices are more unwieldy to track over time than betas are. For this reason, hierarchical models provide a good, practical framework for time-varying covariance matrices.

Value at Risk (VaR)

The VaR for a position or portfolio can be defined as some threshold Τ (in dollars) where the existing position, when faced with market conditions resembling some given historical period, will have P/L greater than Τ with probability k. Typically, k  is chosen to be 99% or 95%.

To compute this threshold Τ, we need to:

  1. Set a significance percentile k, a market observation period, and holding period n.
  2. Generate a set of future market conditions (scenarios) from today to period n.
  3. Compute a P/L on the position for each scenario

After computing each position’s P/L, we sum the P/L for each scenario and then rank the scenarios’ P/Ls to find the the k th percentile (worst) loss. This loss defines our VaR Τ at the the k th percentile for observation-period length n.

Determining what significance percentile k and observation length n to use is straightforward and often dictated by regulatory rules. For example, 99th percentile 10-day VaR is used for risk-based capital under the Market Risk Rule. Generating the scenarios and computing P/L under these scenarios is open to interpretation. We cover each of these, along with the advantages and drawbacks of each, in the next two sections.

To compute VaR, we first need to generate projective scenarios of market conditions. Broadly speaking, there are two ways to derive this set of scenarios:

  1. Project future market conditions using historical (actual) changes in market conditions
  2. Project future market conditions using a Monte Carlo simulation framework

In this paper, we consider a historical simulation approach.

RiskSpan projects future market conditions using actual (observed) n-period changes in market conditions over the lookback period. For example, if we are computing 1-day VaR for regulatory capital usage under the Market Risk Rule, RiskSpan takes actual daily changes in risk factors. This approach allows our VaR scenarios to account for natural changes in correlation under extreme market moves. RiskSpan finds this to be a more natural way of capturing changing correlations without the arbitrary overlay of how to change correlations in extreme market moves. This, in turn, will more accurately capture VaR. Please note that newer crypto currencies may not have enough data to generate a meaningful set of historical scenarios. In these cases, using a benchmark adjusted by a short-term beta may be used as an alternative.

One key consideration for the historical simulation approach is the selection of the observation window or lookback period. Most regulatory guidelines require at least a one-year window. However, practitioners also recommend a shorter lookback period for highly volatile assets. In the chart below we illustrate how VaR for our portfolio of crypto currencies changes for a range of lookback periods and confidence intervals. Please note that VaR is expressed as a percentage of portfolio market value.

Use of an exponentially weighted moving average methodology can be used to overcome the challenges associated with using a shorter lookback period. This approach emphasizes recent observations by using exponentially weighted moving averages of squared deviations. In contrast to equally weighted approaches, these approaches attach different weights to the past observations contained in the observation period. Because the weights decline exponentially, the most recent observations receive much more weight than earlier observations.

Crypto-VaRFigure 3 Daily VaR as % of Market Value calculated using various historical observation periods

VaR as a single number does not represent the distribution of P/L outcomes. In addition to computing VaR under various confidence intervals, we also compute expected shortfall, worst loss, and standard deviation of simulated P/L vectors. Worst loss and standard deviation are self-explanatory while the calculation of expected shortfall is described below.

Expected shortfall is the average of all the P/L figures to the left of the VaR figure. If we have 1,000 simulated P/L vectors, and the VaR is the 950th worst case observation, the expected shortfall is the average of P/Ls from 951 to 1000.

Crypto-VaR

The table below presents VaR-related metrics as a percentage of portfolio market value under various lookback periods.

Crypto-VaRFigure 4 VaR for a portfolio of crypto assets computed for various lookback periods and confidence intervals

Bitcoin Futures: Basis and Proxies

One of the most popular trades for commodity futures is the basis trade. This is when traders build a strategy around the difference between the spot price and futures contract price of a commodity. This exists in corn, soybean, oil and of course Bitcoin. For the purpose of calculating VaR, specific contracts may not provide enough history and risk systems use continuous contracts. Continuous contracts introduce additional basis as seen in the chart below. Risk managers need to work with the front office to align risk factor selection with trading strategies, without compromising independence of the risk process.

Crypto-BasisFigure 5 BTC/Futures basis difference between generic and active contracts

Intraday Value

The highly volatile nature of crypto currencies requires another consideration for VaR calculations. A typical risk process is run at the end of the day and VaR is calculated for a one-day or longer forecasting period. But Crypto currencies, especially Bitcoin, can also show significant intraday price movements.

We obtained intraday prices for Bitcoin (BTC) from Gemini, which is ranked third by volume. This data was normalized to create time series to generate historical simulations. The chart below shows VaR as a percentage of market value for Bitcoin (BTC) for one-minute, one-hour and one-day forecasting periods. Our analysis shows that a Bitcoin position can lose as much as 3.5% of its value in one hour (99th percentile VaR).

Crypto-Intraday

 

Risk-Based Limits 

Right from the inception of Value at Risk as a concept it has been used by companies to manage limits for a trading unit. VaR serves as a single risk-based limit metric with several advantages and a few challenges:

Pros of using VaR for risk-based limit:

  • VaR can be applied across all levels of portfolio aggregation.
  • Aggregations can be applied across varying exposures and strategies.
  • Today’s cloud scale makes it easy to calculate VaR using granular risk factor data.

VaR can be subject to model risk and manipulation. Transparency and use of market risk factors can avoid this pitfall.

Ability to calculate intra-day VaR is key for a risk-based limit implementation for crypto assets. Risk managers should consider at least an hourly VaR limit in addition to the traditional daily limits.

VaR Validation (Bayesian Approach)

Standard approaches for back-testing VaR are applicable to portfolios of crypto assets as well.

Given the volatile nature of this asset class, we also explored an approach to validating the confidence interval and percentiles implied from historical simulations. Although this is a topic that deserves its own document, we present a high-level explanation and results of our analysis.

Building an approach first proposed in the Pyfolio library, we generated a posterior distribution using the Pymc3 package from our historically observed VaR simulations.

Sampling routines from Pymc3 were used to generate 10,000 simulations of the 3-year lookback case. A distribution of percentiles (VaR) was then computed across these simulations.

The distribution shows that the mean 95th percentile VaR would be 7.3% vs 8.9% calculated using the historical simulation approach. However, the tail of the distribution indicates a VaR closer to the historical simulation approach. One could conclude that the test indicates that the original calculation still represents the extreme case, which is the motivation behind VaR.

Crypto-Var-ValidationFigure 6 Distribution of percentiles generated from posterior simulations

Scenario Analysis

In addition to standard shock scenarios, we recommend using the volatility of Bitcoin to construct a simulation of outcomes. The chart below shows the change in Bitcoin (BTC) volatility for select events in the last two years. Outside of standard macro events, crypto assets respond to cyber security events and media effects, including social media.

Crypto-Scenario-AnalysisFigure 7 Weekly observed volatility for Bitcoin

Conclusion

Given the volatility of crypto assets, we recommend, to the extent possible, a probability distribution approach. At the very least, risk managers should monitor changes in relationship (beta) of assets.

For most financial institutions, crypto assets are part of portfolios that include other traditional asset classes. A standard approach must be used across all asset classes, which may make it challenging to apply shorter lookback windows for computing VaR. Use of the exponentially weighted moving approach (described above) may be considered.

Intraday VaR for this asset class can be significant and risk managers should set appropriate limits to manage downward risk.

Idiosyncratic risks associated with this asset class have created a need for monitoring scenarios not necessarily applicable to other asset classes. For this reason, more scenarios pertaining to cyber risk are beginning to be applied across other asset classes.  

CONTACT US TO LEARN MORE!

Related Article

Calculating VaR: A Review of Methods

RiskSpan Named to Inaugural STORM50 Ranking by Chartis Research – Winner of “A.I. Innovation in Capital Markets”

Chartis Research has named RiskSpan to its Inaugural “STORM50” Ranking of leading risk and analytics providers. The STORM report “focuses on the computational infrastructure and algorithmic efficiency of the vast array of technology tools used across the financial services industry” and identifies industry-leading vendors that excel in the delivery of Statistical Techniques, Optimization frameworks, and Risk Models of all types.

STORM50

RiskSpan’s flagship Edge Platform was a natural fit for the designation because of its positioning squarely at the nexus of statistical behavioral modeling (specifically around mortgage credit and prepayment risk) and functionality enabling users to optimize trading and asset management strategies.  Being named the winner of the “A.I. Innovation in Capital Markets” solutions category reflects the work of RiskSpan’s vibrant innovation lab, which includes researching and developing machine learning solutions to structured finance challenges. These solutions include mining a growing trove of alternative/unstructured data sources, anomaly detection in loan-level and other datasets, and natural language processing for constructing deal cash flow models from legal documents.

Learn more about the Edge Platform or contact us to discuss ways we might help you modernize and improve your mortgage and structured finance data and analytics challenges.

Logo

Company

Products

Security & Compliance

Linkedin

SOC2 Type II Certified

Get Started
Log in

Linkedin   

risktech2024