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MBS: The Hidden Gem of Investments?

More buy-side firms are adopting multi-asset trading strategies involving mortgage-backed securities (MBS) in their quest to generate better returns and diversify risk. And many are having success.

While once MBS was considered risky territory, today that has changed. In fact, lately some market practitioners have hailed MBS as the new “hidden gem” of investment types.  

But even with the positive attention that MBS have recently garnered, some firms are hesitant to engage. Why? MBS are complex instruments that require specialized modeling considerations. Firms struggle with building the complete MBS analytics system they need to cover all aspects of analytical demands. They find this task challenging due to the following factors:

1. Lack of Relevant Expertise

Valuing and measuring MBS risk requires sophisticated modeling in diverse domains. Unfortunately, many firms lack the right expertise to carry this out effectively.

MBS involve numerous modeling considerations. For instance, a model of prepayment behavior is necessary to generating realistic MBS cash flows that account for homeowners’ option to prepay their mortgages. Similarly, a stochastic interest rate model is needed to inform the prepayment model, and is used to calculate measures such as option-adjusted spreads. Then there is the often complicated cash flow waterfall structures of MBS, which are captured by cash flow models.

Few institutions are sufficiently equipped to tackle these disparate areas purely on their own. Firms typically find that they have expertise or competitive advantage in only a subset of these domains, so the benefit-to-cost ratio of expending resources on the rest is low, even though necessary.

2. High Data Requirements

The data requirements of an MBS analytics system are high in that different types of data need to be collected, managed, and made to work together in one’s analysis. Reference data describing the terms of an MBS prospectus and up-to-date information on the collateral pool are inputs to the generation of cash flows. Modern prepayment and default models usually rely on econometric and loan-specific data to produce forecasts. In the presence of other instruments such as derivatives, relevant market data for pricing model calibration is also needed. Acquiring and marshalling all the necessary data is both difficult and expensive.

3. Complexity Affecting Computational Efficiency

The complexity of the calculations involved in MBS analysis is high, therefore computational efficiency is a significant issue. For example, in order to obtain the cash flows of a specific bond in a collateralized mortgage obligations (CMO) deal, the entire cash flow waterfall of that deal must in general be populated, due to the interdependencies of the different tranches that comprise the waterfall. This makes projecting the cash flows for an MBS much more expensive than, say, a floating rate note or even a typical callable bond. Another source of time consuming computation is the calculation of OAS where many paths of interest and prepayment rates must be generated for accuracy.

4. Need to Upgrade Analytics

Extending the coverage of one’s analytics library is challenging without a sound design that has been built in from the beginning. Say you need to switch to an updated prepayment model in light of new research, or incorporate new waterfall structures as specified by the MBS that you wish to trade. If each combination of instrument definition, modeling assumptions, and calculation type is treated on a case-by-case basis, as is often done, developing new analytics becomes more costly and difficult to scale than it needs to be. Reacting to changing market landscapes and business demands is challenging as a result.

Uncover the Gem…More Easily

The upside is that preparing to trade MBS doesn’t need to be this difficult or expensive. To combat these challenges while minimizing the associated costs, many firms are electing to implement a generic, modular approach to MBS analytics. By this I mean they are designing their MBS analytics framework by first identifying the fundamental concepts in their analysis workflow. From there, they use these concepts as building blocks that combine to form a complete MBS analytics system.

This MBS framework design is generic and flexible enough to support any kind of MBS analysis that you might want to carry out. Each module in the framework can be swapped out and replaced as desired, without affecting the other parts of the system. This allows you to focus on areas where value creation is greatest.

Like to learn more about how using a sophisticated framework can help you simplify MBS trading? Download our eBook: Best Practices in MBS Valuation and Risk.

About the author

Eric Peng

Manager, Quantitative Framework, FINCAD

Eric Peng is Manager, Quantitative Framework at FINCAD, where he supervises a team of quantitative analysts and works on analytics research and development. Prior to joining FINCAD, Eric held positions at Manulife Financial and the Canada Pension Plan Investment Board. He holds a Master of Mathematical Finance from the University of Toronto, and a BSc in Mathematics from the University of British Columbia.


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