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Calculating VaR: A Review of Methods

Calculating VaR

A Review of Methods

CONTRIBUTOR

Don Brown
Co-Head of Quantitative Analytics

TABLE OF CONTENTS

  1. Introduction

  2. Generating Scenarios

  3. Calculating Simulated P/L

  4. Conclusion

  5. References

Have questions about calculating VaR?

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Chapter 1
Introduction

Many firms now use Value-at-Risk (“VaR”) for risk reporting. Banks need VaR to report regulatory capital usage under the Market Risk Rule, as outlined in the Fed and OCC regulations and. Additionally, hedge funds now use VaR to report a unified risk measure across multiple asset classes. There are multiple approaches to VaR, so which method should we choose? In this brief paper, we outline a case for full revaluation VaR in contrast to a simulated VaR using a “delta-gamma” approach to value assets.

The VaR for a position or book of business can be defined as some threshold  (in dollars) where the existing position, when faced with market conditions similar to some given historical period, will have P/L greater than  with probability. Typically,  is chosen to be  or. To compute this threshold , we need to

  1. Set a significance percentile , a market observation period, and holding period n.1
  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/L to find the kth percentile (worst) loss.2 This loss defines our VaR T at the kth percentile for observation-period length n. Determining what significance percentile k and observation length n to use is straightforward and is 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 in the next two sections, with their advantages and drawbacks.

Chapter 2
Generating Scenarios

To compute VaR, we first need to generate projective scenarios of market conditions. Broadly speaking, there are two ways to derive this set of scenarios3

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

MONTE CARLO SIMULATION

Many commercial providers simulate future market conditions using Monte Carlo simulation. To do this, they must first estimate the distributions of risk factors, including correlations between risk factors. Using correlations that are derived from historical data makes the general assumption that correlations are constant within the period. As shown in the academic literature, correlations tend to change, especially in extreme market moves – exactly the kind of moves that tend to define the VaR threshold.4 By constraining correlations, VaR may be either overstated or understated depending on the structure of the position. To account for this, some providers allow users to “stress” correlations by increasing or decreasing them. Such a stress scenario is either arbitrary, or is informed by using correlations from yet another time-period (for example, using correlations from a time of market stress), mixing and matching market data in an ad hoc way.

Further, many market risk factors are highly correlated, which is especially true on the interest rate curve. To account for this, some providers use a single factor for rate-level and then a second or third factor for slope and curvature of the curve. While this may be broadly representative, this approach may not capture subtle changes on other parts of the curve. This limited approach is acceptable for non-callable fixed income securities, but proves problematic when applying curve changes to complex securities such as MBS, where the security value is a function of forward mortgage rates, which in turn is a multivariate function of points on the curve and often implied volatility.

MONTE CARLO SIMULATION

Many commercial providers simulate future market conditions using Monte Carlo simulation. To do this, they must first estimate the distributions of risk factors, including correlations between risk factors. Using correlations that are derived from historical data makes the general assumption that correlations are constant within the period. As shown in the academic literature, correlations tend to change, especially in extreme market moves – exactly the kind of moves that tend to define the VaR threshold.4 By constraining correlations, VaR may be either overstated or understated depending on the structure of the position. To account for this, some providers allow users to “stress” correlations by increasing or decreasing them. Such a stress scenario is either arbitrary, or is informed by using correlations from yet another time-period (for example, using correlations from a time of market stress), mixing and matching market data in an ad hoc way.

Further, many market risk factors are highly correlated, which is especially true on the interest rate curve. To account for this, some providers use a single factor for rate-level and then a second or third factor for slope and curvature of the curve. While this may be broadly representative, this approach may not capture subtle changes on other parts of the curve. This limited approach is acceptable for non-callable fixed income securities, but proves problematic when applying curve changes to complex securities such as MBS, where the security value is a function of forward mortgage rates, which in turn is a multivariate function of points on the curve and often implied volatility.

HISTORICAL SIMULATION

RiskSpan projects future market conditions by using actual (observed) -day changes in market conditions over the look-back period. For example, if we are computing 10-day VaR for regulatory capital usage under the Market Risk Rule, RiskSpan takes actual 10-day changes in market variables. This approach allows our VaR scenarios to account for natural changes in correlation under extreme market moves, such as occurs during a flight-to-quality where risky assets tend to underperform risk-free assets, and risky assets tend to move in a highly correlated manner. RiskSpan believes this is a more natural way to capture changing correlations, without the arbitrary overlay of how to change correlations in extreme market moves. This, in turn, will more correctly capture VaR.5

 

HISTORICAL SIMULATION

RiskSpan projects future market conditions by using actual (observed) -day changes in market conditions over the look-back period. For example, if we are computing 10-day VaR for regulatory capital usage under the Market Risk Rule, RiskSpan takes actual 10-day changes in market variables. This approach allows our VaR scenarios to account for natural changes in correlation under extreme market moves, such as occurs during a flight-to-quality where risky assets tend to underperform risk-free assets, and risky assets tend to move in a highly correlated manner. RiskSpan believes this is a more natural way to capture changing correlations, without the arbitrary overlay of how to change correlations in extreme market moves. This, in turn, will more correctly capture VaR.5

Chapter 3
Calculating Simulated P/L

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With the VaR scenarios defined, we move on to computing P/L under these scenarios. Generally, there are two methods employed

  1. A Taylor approximation of P/L for each instrument, sometimes called “delta-gamma”
  2. A full revaluation of each instrument using its market-accepted technique for valuation

Market practitioners sometimes blend these two techniques, employing full revaluation where the valuation technique is simple (e.g. yield + spread) and using delta-gamma where revaluation is more complicated (e.g. OAS simulation on MBS).

 

DELTA-GAMMA P/L APPROXIMATION

Many market practitioners use a Taylor approximation or “delta-gamma” approach to valuing an instrument under each VaR scenario. For instruments whose price function is approximately linear across each of the m risk factors, users tend to use the first order Taylor approximation, where the instrument price under the kth VaR scenario is given by

Making the price change in the kth scenario

Where ΔP is the simulated price change, Δxi is the change in the ith risk factor, and  is the price delta with respect to the ith risk factor evaluated at the base case. In many cases, these partial derivatives are approximated by bumping the risk factors up/down.6 If the instrument is slightly non-linear, but not non-linear enough to use a higher order approximation, then approximating a first derivative can be a source of error in generating simulated prices. For instruments that are approximately linear, using first order approximation is typically as good as full revaluation. From a computation standpoint, it is marginally faster but not significantly so. Instruments whose price function is approximately linear also tend to have analytic solutions to their initial price functions, for example yield-to-price, and these analytic solutions tend to be as fast as a first-order Taylor approximation. If the instrument is non-linear, practitioners must use a higher order approximation which introduces second-order partial derivatives. For an instrument with m risk-factors, we can approximate the price change in the kth scenario by using the multivariate second order Taylor approximation

To simplify the application of the second-order Taylor approximation, practitioners tend to ignore many of the cross-partial terms. For example, in valuing MBS under delta-gamma, practitioners tend to simplify the approximation by using the first derivatives and a single “convexity” term, which is the second derivative of price with respect to overall rates. Using this short-cut raises a number of issues:

  1. It assumes that the cross-partials have little impact. For many structured products, this is not true.7
  2. Since structured products calculate deltas using finite shifts, how exactly does one calculate a second-order mixed partials?8
  3. For structured products, using a single, second-order “convexity” term assumes that the second order term with respect to rates is uniform across the curve and does not vary by where you are on the curve. For complex mortgage products such as mortgage servicing rights, IOs and Inverse IOs, convexity can vary greatly depending on where you look at the curve.

Using a second-order approximation assumes that the second order derivatives are constant as rates change. For MBS, this is not true in general. For example, in the graphs below we show a constant-OAS price curve for TBA FNMA 30yr 3.5%, as well as a graph of its “DV01”, or first derivative with respect to rates. As you can see, the DV01 graph is non-linear, implying that the convexity term (second derivative of the price function) is non-constant, rendering a second-order Taylor approximation a weak assumption. This is especially true for large moves in rate, the kind of moves that dominate the computation of the VaR.9

In addition to the assumptions above, we occasionally observe that commercial VaR providers compute 1-day VaR and, in the interest of computational savings, scale this 1-day VaR by √10 to generate 10-day VaR. This approximation only works if

  1. Changes in risk factors are all independently, identically distributed (no autocorrelation or heteroscedasticity)
  2. The asset price function is linear in all risk factors

In general, neither of these conditions hold and using a scaling factor of √10 will likely yield an incorrect value for 10-day VaR.10

 

RATIONALIZING WEAKNESS IN THE APPROXIMATION

With the weaknesses in the Taylor approximation cited above, why do some providers still use delta-gamma VaR? Most practitioners will cite that the Taylor approximation is much faster than full revaluation for complex, non-linear instruments. While this seems true at first glance, you still need to:

  1. Compute or approximate all the first partial derivatives
  2. Compute or approximate some of the second partial derivatives and decide which are relevant or irrelevant. This choice may vary from security type to security type.

Neither of these tasks are computationally simple for complex, path-dependent securities which are found in many portfolios. Further, the choice of which second-order terms to ignore has to be supported by documentation to satisfy regulators under the Market Risk Rule.

Even after approximating partials and making multiple, qualitative assessments of which second-order terms to include/exclude, we are still left with error from the Taylor approximation. This error grows with the size of the market move, which also tends to be the scenarios that dominate the VaR calculation. With today’s flexible cloud computation and ultra-fast, cheap processing, the Taylor approximation and its computation of partials ends up being only marginally faster than a full revaluation for complex instruments.11

With the weaknesses in Taylor approximation, especially with non-linear instruments, and the speed and cheapness of full revaluation, we believe that fully revaluing each instrument in each scenario is both more accurate and more straightforward than having to defend a raft of assumptions around the Taylor approximation.

Chapter 4
Conclusion

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With these points in mind, what is the best method for computing VaR? Considering the complexity of many instruments, and considering the comparatively cheap and fast computation available through today’s cloud computing, we believe that calculating VaR using a historical-scenario, full revaluation approach provides the most accurate and robust VaR framework.

From a scenario generation standpoint, using historical scenarios allows risk factors to evolve in a natural way. This in turn captures actual changes in risk factor correlations, changes which can be especially acute in large market moves. In contrast, a Monte Carlo simulation of scenarios typically allows users to “stress” correlations, but these stresses scenarios are arbitrary which may ultimately lead to misstated risk.

From a valuation framework, we feel that full revaluation of assets provides the most accurate representation of risk, especially for complex instruments such as complex ABS and MBS securities. The assumptions and errors introduced in the Taylor approximation may overwhelm any minor savings in run-time, given today’s powerful and cheap cloud analytics. Further, the Taylor approximation forces users to make and defend qualitative judgements of which partial derivatives to include and which to ignore. This greatly increasing the management burden around VaR as well as regulatory scrutiny around justifying these assumptions.

In short, we believe that a historical scenario, full-revaluation VaR provides the most accurate representation of VaR, and that today’s cheap and powerful computing make this approach feasible for most books and trading positions. For VaR, it’s no longer necessary to settle for second-best.

References

  1. Board of Governors, Federal Reserve System, “Application of the Market Risk Module in Bank Holding Companies and State Member Banks (SR 09-1),” Federal Reserve System, 2009.

  2. Federal Register, “Code of Federal Regulations, Title 12, Vol.1, Part 3, Subpart F,” 2014.

  3. D. Heath, R. Jarrow and A. Morton, “Bond Pricing and the Term Structure of Interest Rates: A New Methodology for Contingent Claims Valuation,” Econometrica, vol. 60, no.1, pp. 77-105, 1992.

  4. RiskSpan, Inc., “Prepayment Model Validation Report,” 2016.

  5. P. Hartmann, S. Straetmans and C. De Vries, “Asset Market Linkages in Crisis Periods,” The Review of Economics and Statistics, vol. 86, no. 1, pp. 313-236, 2004.

  6. RiskSpan Inc., “RS Residential Credit Model,” 2016.

  7. F. Diebold, A. Hickman, A. Inoue and T. Schuermann, “Scale Models,” Risk, pp. 104-107, 1998.

ENDNOTES

1 The holding period n is typically one day, ten days, or 21 days (a business-month) although in theory it can be any length period.
 
2 We can also partition the book into different sub-books or “equivalence classes” and compute VaR on each class in the partition. The entire book is the trivial partition.
 
3 There is a third approach to VaR: parametric VaR, where the distributions of asset prices are described by the well-known distributions such as Gaussian. Given the often-observed heavy-tail distributions, combined with difficulties in valuing complex assets with non-linear payoffs, we will ignore parametric VaR in this review.
 
4 The academic literature contains many papers on increased correlation during extreme market moves, for example [5]

5 For example, a bank may have positions in two FX pairs that are poorly correlated in times normal times and highly negatively correlated in times of stress. If a 99%ile worst-move coincides with a stress period, then the aggregate P/L from the two positions may offset each other. If we used the overall correlation to drive a Monte Carlo simulated VaR, the calculated VaR could be much higher.

6 This is especially common in MBS, where the first and second derivatives are computed using a secant-line approximation after shifting risk factors, such as shifting rates ± 25bp

7 For example, as rates fall and a mortgage becomes more refinancible, the mortgage’s exposure to implied volatility also increases, implying that the cross-partial for price with respect to rates and vol is non-zero.

8 Further, since we are using finite shifts, the typical assumption that ƒxy = ƒyx which is based on the smoothness of ƒ(x,y) does not necessarily hold. Therefore, we need to compute two sets of cross partials, further increasing the initial setup time.

9 Why is the second derivative non-constant? As rates move significantly, prepayments stop rising or falling. At these “endpoints,” cash flows on the mortgage change little, making the instrument positively convex like a fixed-amortization schedule bond. In between, changes in prepayments case the mortgage to extend or shorten as rates rise or fall, respectively, which in turn make the MBS negatively convex.

10 Much has been written on the weakness of this scaling, see for example [7]

11 For example, using a flexible computation grid RiskSpan can perform a full OAS revaluation on 20,000 MBS passthroughs using a 250-day lookback period in under one hour. Lattice-solved options are an order of magnitude faster, and analytic instruments such as forwards, European options, futures and FX are even faster.

1 The holding period n is typically one day, ten days, or 21 days (a business-month) although in theory it can be any length period.

2 We can also partition the book into different sub-books or “equivalence classes” and compute VaR on each class in the partition. The entire book is the trivial partition.

3 There is a third approach to VaR: parametric VaR, where the distributions of asset prices are described by the well-known distributions such as Gaussian. Given the often-observed heavy-tail distributions, combined with difficulties in valuing complex assets with non-linear payoffs, we will ignore parametric VaR in this review.

4 The academic literature contains many papers on increased correlation during extreme market moves, for example [5]

5 For example, a bank may have positions in two FX pairs that are poorly correlated in times normal times and highly negatively correlated in times of stress. If a 99%ile worst-move coincides with a stress period, then the aggregate P/L from the two positions may offset each other. If we used the overall correlation to drive a Monte Carlo simulated VaR, the calculated VaR could be much higher.

6 This is especially common in MBS, where the first and second derivatives are computed using a secant-line approximation after shifting risk factors, such as shifting rates ± 25bp

7 For example, as rates fall and a mortgage becomes more refinancible, the mortgage’s exposure to implied volatility also increases, implying that the cross-partial for price with respect to rates and vol is non-zero.

8 Further, since we are using finite shifts, the typical assumption that ƒxy = ƒyx which is based on the smoothness of ƒ(x,y) does not necessarily hold. Therefore, we need to compute two sets of cross partials, further increasing the initial setup time.

9 Why is the second derivative non-constant? As rates move significantly, prepayments stop rising or falling. At these “endpoints,” cash flows on the mortgage change little, making the instrument positively convex like a fixed-amortization schedule bond. In between, changes in prepayments case the mortgage to extend or shorten as rates rise or fall, respectively, which in turn make the MBS negatively convex.

10 Much has been written on the weakness of this scaling, see for example [7]

11 For example, using a flexible computation grid RiskSpan can perform a full OAS revaluation on 20,000 MBS passthroughs using a 250-day lookback period in under one hour. Lattice-solved options are an order of magnitude faster, and analytic instruments such as forwards, European options, futures and FX are even faster.

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Institutionally Focused Broker-Dealer: Prepayment Analysis

An institutional-broker dealer needed a solution to analyze agency MBS prepayment data.

The Solution

The Edge Platform has been adopted and is actively used by the Agency trading desk to analyze Agency MBS prepayment data, to discover relationships between borrower characteristics and prepayment behavior.  

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 pool. At 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 structure. A 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 scenarios for 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 1: Deal Profile Stratification 

deal-compare-in-rs-edge

Example 2: Deal Comparative Analysis 

Deal Profile Stratification

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 performed. Jozwik recommends analyzing historical trends both from the recent past and from historical 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 crisis. Though potentially cumbersome to execute, this approach leverages the rich set of historical performance data available in the mortgage space.  

For a new RMBS Deal, portfolio managers can review the distribution of key features, such as FICO, LTV, and documentation type. They 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 ultra–conservative. 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 burden. RiskSpan’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 deal. Further, 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.

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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 rate–sheet 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.

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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

Loans with Less than Standard MI Coverage

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. Loans with Less-than-Standard MI Coverage

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: 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

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