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Tag Archives: Esaki Snyder

Here is the March 16th 20011 CREPIG podcast interview with JW Najarian & Robert Schecter. It has been described as ‘educational’.

This interview covers many topics, the economy, residential mortgages, commercial properties, distressed property industry, and especially methodology errors.

This interview is also available at the QuantumRisk website, http://www.QuantumRisk.com/.

I hope you find this interview informative and an enabler to executing better investment decisions.

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This series of blogs is derived from my discussions on the LinkedIn Quant Finance: What is the best approach to handling CMBS &/or RMBS Credit Risk analysis? discussion forum.

Here is the problem with vintage per the triangular matrix method. (I gave it that term because nobody else I talked to knew what it was named.) If you look at the Esaki-Snyder report or the Wachovia CMBS 2008 Loss Study, there is this averaging method, that looks like a triangular matrix, to determine the average over vintages by loan age.

It gives one an incorrect shape of loss or defaults over the life of an asset. There is a simple test to prove this. Assume that all defaults (or losses) are constant over the life of the loan say 2% every year. The age-default profile should be a horizontal line. Now say that as the economy improves the defaults decrease by 0.2% per vintage, ie yr 2000=2%, yr 2001 = 1.8%, yr 2002 =1.6%, … but remains constant per vintage through the life of the asset.

You can do the same with increasing rates.

The method is biased because the averaged defaults (or loss) is no longer a horizontal line (the original correct input), and tends towards the value of the oldest vintage.

To make it easier for the reader I have provided the link to the Triangular Matrix Method Test Excel 2003 Spreadsheet. (Note: you may have some problems with IE8, try saving to disc.)

 

 Discalimer: This blog is purely for informational/educational purposes and is not intended to either negate or advocate any product, service, political position or persons.
Creative Commons License
QuantumRisk Blog Posts by Benjamin T Solomon is licensed under a Creative Commons Attribution-Noncommercial 3.0 United States License.
Based on a work at quantumrisk.wordpress.com.

This series of blogs is derived from my discussions on the LinkedIn Quant Finance: What is the best approach to handling CMBS &/or RMBS Credit Risk analysis? discussion forum.

In a previous comment I had found that FICO socres were not useful, Russell, however, found that FICO scores were extremely good predictors of defaults. Here I clarifying what & how I did my analysis:

If I’m correct all the data I used was non-agency, 2005, 2006 and some 2007. More than 100 ‘tapes’ from service providers and banks themselves. There were in total about 330,000 residential mortgages assets. And I think they were all subprime – thats why the incomplete data problem.

The probability distribution of the universe of underwritten FICO scores was identical to the probability distribution of the foreclosed (no deliquents) underwritten FICO scores. I did not look at vintage because firstly FICO scores should have been enough information, and secondly there is another problem with vintage.

I think the difference in our analyses is that I used FICOs at underwriting and Russell used FICOs at default. We will know in a future comment, and I’m not sure what Russell meant by “projected defaults”.

I would definitely like to hear from other analysts on how they handled RMBS defaults versus FICO scores so that all of have a very clear idea on how to use FICO scores in the most beneficial manner for the industry.

 

Discalimer: This blog is purely for informational/educational purposes and is not intended to either negate or advocate any product, service, political position or persons.
Creative Commons License
QuantumRisk Blog Posts by Benjamin T Solomon is licensed under a Creative Commons Attribution-Noncommercial 3.0 United States License.
Based on a work at quantumrisk.wordpress.com.