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Daily Archives: May 9th, 2009

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.

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

I agree with prepayments in RMBS space but I was looking for more than the usual accepted, standard knowledge. Sort of industry wisdom versus textbook stuff. Argyn had suggested other loss models and Russell had explained how migrating FICO scores affect RMBS portfolios. The discussion continues:

For example if Argyn had not mentioned bond pricing models that would not have jogged my memory about the negative correlations between RMBS and CMBS. This negative correlation is something anybody can test for themselves. I don’t have the data or the models so it is from memory. I would suggest testing residential losses against commercial losses by MSAs. There are some economic lag effects but never got to complete this study.

This negative correlation could be interpreted as consumer spending lags job loss or growth. Therefore, if the recession is long enough we can expect an increase in commercial property losses even as residential properties begin to recover. If the recession is short you won’t notice this lag because most consumers and property owners have some staying power. If the recession is too long then both are negatively affected.

OK let me step out of the ‘conventional space’ to make this discussion more interesting and hope that others would join in.

My thinking was once you have bought a deal, it is yours good, bad and ugly. So accounting for prepayments (the bad) in RMBS is a poor strategy. As a tactic you need to do it but as a strategy it is poor. And you are always stuck with the economy, and whatever it does.

My strategy for both RMBS & CMBS was to determine the CVaR (the ugly) that an investor was willing to tolerate within some investment horizon, say 5 years from the underwritten parameters.

To do this you need to know how a deal would perform over this investment horizon, from the underwritten parameters. That means prepayments are (1) of little value as they are driven by future changing economic fundamentals, and (2) they are management tools to deal with the bad.

The question I then had to answer was how do we minimize the ugly (CVaR) given only underwritten parameters?

Before I ran into the RMBS incomplete data problem and abandoned RMBS, I tested FICO scores. Could we use underwritten FICO scores to predict future losses in RMBS space? Once you have bought the deal you can watch credit scores deteriorate, but we want to avoid that as much as we can.

One of the things I did was to analyze the distribution of FICO scores in the ‘universe’ against the distribution in the defaulted assets. And to my surprise there was no difference. Underwritten FICO scores are unable to predict future deterioration of individual credit worthiness. That is one of the main reasons we abandoned RMBS because with incomplete data everything hinges on FICO scores. Again you can test this yourself.

I believe that from a forecasting perspective, FICO scores are a proxy for either disposal or discretionary income, that the two are highly correlated – I haven’t done this study, it is a guess having looked at tons of data.

In CMBS one can build a model to forecast future losses at a deal or portfolio level, and then determine from the CVaR-tolerance and investment-horizon, which portfolio you would invest in.

Anyone else seen these types of problems with FICOs?

Anybody tried the investment strategy I’ve outlined above?

 

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 form my discussions on the LinkedIn Quant Finance: What is the best approach to handling CMBS &/or RMBS Credit Risk analysis? discussion forum.

I’ve been building VaR and CVaR models for commercial property debt portfolios / deals this last 5 years.

I use a 2-layered model. The first layer (input) consists of a forecasting model based on historical data to project future losses, the second (output) layer is a Monte Carlo model.

This is my take on CMBS Loss/Credit Risk having looked at 220,000 CMBS assets (almost all the US commercial property assets since the early 1990s) for a previous employer. I can say this from experience:
1. VaR does both, it underestimates and overestimates losses.It especially underestimates losses in the 1st 2-3 years of a commercial property (bond)deal.
2. CVaR consistently gives stable estimates.
3. Much of the VaR variations are due to sampling plan and amount/quality of data you have.
4. The quality of the forecasting model is ever so critical.
5. I can say that as a general rule if you have to use tools like ARCH & GARCH you have factors missing in your data sample. But then again you may not have a choice if the data is not collected.

I have included Nassim Nicholas Taleb’s Black Swan to my set of metrics (I’ve handled this in a proprietary manner so I can’t discuss this just yet). And can tell you that there are good CMBS portfolios (Black Swan <= 20%) and some really bad ones. By bad I mean 80% wipe out.

Some other observations,
1. Residential mortgage losses are Lognormal but the catch is that much of this data is incomplete. And this problem is quite severe.
2. Commercial property losses are Gamma distributions and the incomplete data problem is not as severe.
3. I haven’t found any Normal distributions in real loss data.

My opinion to another discussion thread Is VaR a good and reliable method to measure Market Risk? would be a resounding ‘NO’ and definitely not for Credit Risk, but the industry wants to see it so I produce it.

I figure RMBS is similar but stopped working on RMBS when I realized that incomplete data was a severe problem.

I would be interested in other people’s experience in this area. I know that the DSCR based loss model is quite popular. Are there any other types of models in general use for credit risk?

 

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.