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The Opportunity:
What would you pay to know the Black Swan of your CMBS deal risk?

Reverse that question!

What would a deal manager pay you to know the Black Swan of his CMBS deal?

The Need:
Inspite the bad publicity surrounding CDOs and structured finance the tranche strucutre is one of the most efficient methods of creating differentiated classes of assets, as The Committee on the Global Financial System explains:

A key goal of the tranching process is to create at least one class of securities whose rating is higher than the average rating of the underlying collateral pool or to create rated securities from a pool of unrated assets. This is accomplished through the use of credit support (enhancement), such as prioritisation of payments to the different tranches.

However, what really caused the market to substatially undervalue these assets was that the default rate and the severity of the loss was much greater than even the complex rating processes had estimated them to be.  Have you noticed that some (many?) of the AAA tranches had losses?

The Solution:
The answer is an econometric assessment of the default and loss distributions of CMBS assets. Note, not a single point value but the whole distribution. This distribution will provide the CMBS Deal’s 95% or 98% VaR and CVaR loss estimates.

From this distribution we can then recalculate the loss estimates for each tranche, and be pretty certain what the loss characteristics of these tranches are independently of the ratings assigned to the tranches. A backup second opinion, if you would.

Why would this provide better answers?
1. We know the assigned ratings did not cut it.
2. We know the vintage or triangular matrix method provided incorrect default and loss curves. I was the first & only person to correctly identify that a major tool, vinatges/triangular matrix method, used in the mortgage idustry was providing incorrect results. The link provides access to an Excel worksheet that allows you to confirm this for yourself. To understand the magnitude of this finding one only need to look at the vast array of mortgage analyses, from the Esaki-Snyder reports to the Wachovia 2008 CMBS Loss Study, that use this tool.
3. The DSCR loss model used in the CMBS industry does not match the historical data. I discovered this weakness in the method through extensive testing against the historical data. Do you know of anyone who has personally tested these tool against the historical data?

What would the solution look like?

The graph below is a simulated long term VaR & CVaR loss outcome for a simulated CMBS deal.


This graph is based on a set of CMBS loss & default distributions, and provides a second method to evaluating bond pricing. After all at the end of the day, isn’t cashflow analysis and DSCR’s about bond pricing? Notice how VaR (green dash) consistently underestimates CVaR (purple dash). We can add in Black Swans into this report. Note that my initial assessement was that CMBS Black Swans are on the order of 20% to 80%.

I am seeking partnerships with banks/investment funds/ratings companies to fund the development of this econometric CMBS business, which I expect to be transferrable to the RMBS sub-industry.

Call me 303-618-2800 or email me at benjamin . t . solomon AT QuantumRisk . com if you are interested in this business.

Disclosure: I’m a capitalist too, and my musings & opinions on this blog are for informational/educational purposes and part of my efforts to learn from the mistakes of other people. Hope you do, too. These musings are not to be taken as financial advise, and are based on data that is assumed to be correct. Therefore, my opinions are subject to change without notice. This blog is not intended to either negate or advocate any persons, entity, product, services or political position.

One Comment

  1. Zintro (founded by former Structured Finance exec Stuart Lewtan) Zintro ( connects professionals making high value (e.g. investment, market analysis, risk assessment, purchasing, technology selection, hiring…) decisions with experts in the field for fee based consults. It leverages technology and the broad reach of the Internet to create an on-line meeting place for those who value information enough to pay for it and those who are worthy of being paid for it.

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