Back-Testing: Using RS Edge to Validate a Prepayment Model

Most asset-liability management (ALM) models contain an embedded prepayment model for residential mortgage loans. To gauge their accuracy, prepayment modelers typically run a back-test comparing model projections to the actual prepayment rates observed. A standard test is to run a portfolio of loans as of a year ago using the actual interest rates experienced during...

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…

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.