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Tag Archives: Black Swan

Giacomo John Roma pointed me to an interesting article by Taleb & Spitznagel as part of the LinkedIn discussion and here are my thoughts on this and the related discussion.

I infer that Taleb’s primary concern is leverage. Debt provides leverage, and unconstrained debt provides unconstrained leverage. That is the lesson of 2008. Taleb wants us to reduce the extent of the leverage in our economy, because debt has a property that affects our perception of risk, i.e. debt hides volatility.

Regarding VaR:

(1) If you examine the historical data, outside of social sciences and quality control, there are very few thing that are Normally distributed. Taleb’s point here is that much of the mathematics that is used in finance and economics is based strongly on the assumptions of normality, and therefore, are not good forecasters of their metrics. They only appear to work when the law of large numbers can be applied. Therefore Taleb is recommending that a lot of these mathematics and their resulting analytical tools are erroneous in the long run.

(2) There are serious problems with VaR even without the normality assumption. In my experiece (I never assume normality) with years of building econometric CMBS loss and default models I found that VaR would consistently underestimate losses in the first 3 to 5 years of the loan.

Black Swans are a real problem in mortgages. The historical data shows that they are real. Just look at 2008. My own estimates are that Black Swans in CMBS bonds are on the order of 20% to 80%.

I also found that the only realistic way to handle undisciplined leverage was to implement non-linear economic or risk capital controls. This comes back to Taleb’s point on non-linearity.

Non-linearity is not new. Credit card companies use non-linearity a lot. They impose 2x and even 3x or 30% rates on delinquent card holders. Try imposing that on institutions, and all hell breaks loose. This is the power of buyers and sellers at play, not mathematics and not finance.

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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.
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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.

Disclosure: In a previous life I examined 330,000 residential mortgages and 220,000 CMBS assets, and was surprised to find substantial unusable data in these assets.

I read Pandit memo: Citi profitable in 2009, and it got me thinking about my own work and findings in loss modeling. I have no doubt that Citi would be profitable some day, given the amount of bailout funds it has received.

What concerns me is that the stress test was completed in a matter of weeks. This suggest that Citi and other banks are using the same risk models and stress testing that got them into this mess to redo their stressing.

That the assumption these banks are making is that their models are still correct but they need to increase the severity of their stress factors. For example if you originally expected an average default rate of 1.96% you now use say 2x or 3.92%. Maybe some additional combinations such as also increasing average loss severity from 35% to 55%.

Sorry this does not fly with me. My work with CMBS loss models shows:

1. That the more popular loss models are significantly biased. The problem with statistical bias is that stressing them more does not give a better understanding of future losses.

2. Some of the model factors don’t even work or don’t work correctly.

3. Nassim Nicholas Taleb’s Black Swan hypothesis suggests that the CMBS Black Swan’s are on the order of 20% to 80%.

4. To do real stress testing you have to know 3 numbers, VaR, CVaR and Black Swan.

5. It takes at least a year to develop new stress models, not days or weeks.

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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.
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