LTV, Low and Slow — RS Edge Analysis

In agency MBS, the specified pool market prices high-LTV loans at a pay-up over TBA for the prepayment protection they offer. This relationship has been well established for both high-LTV purchase loans as well as MHA (Making Home Affordable–i.e., modification and refi programs for troubled loans) production. But what about low-LTV loans? In this post,…

Loans Under $200K Prepay Slowly—But Not in Every State

In agency pools, loans with balances below $200,000 offer prepayment protection (i.e., they prepay more slowly) relative to loans with higher balances. Servicers typically segregate these loans into specified pools that trade at a premium over TBA-deliverable pools. But the prepayment protection isn’t homogenous and varies significantly by state.1 The following chart compares the S-curve…

Fed MBS Runoff Portends More Negative Vega for the Broader Market

With much anticipation and fanfare, the Federal Reserve is finally on track to reduce its MBS holdings. Guidance from the September FOMC meeting reveals that the Fed will allow its MBS holdings to “run off,” reducing its position via prepayments as opposed to selling it off. What does this mean for the market? In the long-term, it means a large increase in net supply of Agency MBS and with it an increase in overall implied and realized volatility.

Machine Learning and Portfolio Performance Analysis

Attribution analysis of portfolios typically aims to discover the impact that a portfolio manager’s investment choices and strategies had on overall profitability. They can help determine whether success was the result of an educated choice or simply good luck. Usually a benchmark is chosen and the portfolio’s performance is assessed relative to it. This post, however, considers the question of whether a non-referential assessment is possible. That is, can we deconstruct and assess a portfolio’s performance without employing a benchmark? Such an analysis would require access to historical return as well as the portfolio’s weights and perhaps the volatility of interest rates, if some of the components exhibit a dependence on them. This list of required variables is by no means exhaustive.

Managing Risk Data: Financial Instrument Terms and Conditions

An instrument’s terms and conditions lie at the heart of cash flow generation and valuation. Not surprisingly, errors in terms and conditions can drive errors in valuation. Fortunately, fixing these errors is often straightforward, provided the terms and conditions data is readily available, which is not always the case for private placement instruments.

Data Management for a Robust Risk Framework

In an article published last year, the Harvard Business Review quotes IBM research that estimates that bad data costs US business $3 Trillion per year. Although it is difficult to identify the specific cost associated with bad data in market-risk management, it is obvious that managing data has never been more important. The success of a market-risk management implementation is largely dependent on a validated, scalable, and well-governed data management process.

Calculating VaR: A Review of Methods

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 [1] and [2]. 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.