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Tag Archives: Credit Risk

Rome could not be conquered. It destroyed itself from inside. The afterglow of Rome’s strength remained for about another 200 years, and kept the barbarians at bay.

Wall St., the New Rome, had reached the power and stature of old Rome, but on a much grander scale. And in 2008 New Rome fell from grace. Here are 3 inferences why.

Abuse of Strategy: Up to 2007, we thought of Wall St. firms as excellent well managed companies, the crème de la crème of American Capitalism, but as of 2008 we know that their management teams had bled their future sustainability by maximizing profits today at any expense. With hind sight, Wall St. firms chased mortgage products in the “mistaken” belief that they had securitized away all risk. Really? What happened to the risk-return relationship? I suppose that was conveniently forgotten?

In the strategy world this is known as harvesting. Harvesting is a correct corporate strategy in a mature dying industry, but not as a management game at shareholders long term expense.

Misuse of the Term Strategy: A few years ago, a very senior financial executive in the mortgage industry came up to me and said “you cannot use frequencies to estimate probabilities”! Makes you wonder if any of these finance types really had any understanding of the risks they were taking? History suggests that they did not.

The unfortunate reality in financial services, is that strategy is mistaken for a new formula (see Recipe for Disaster: The Formula That Killed Wall Street) or worse still a new IT system. Worse because without a design document and proper records and audits it is difficult to figure out what went into this IT system. These are not strategies.

At best they are tactics, more often they are business necessities. In the world of finance, understanding your formulae, your systems, and your risk methodologies are a pre-requisites of the business. These are not strategies. If anyone tells you otherwise they really don’t understand what they are doing. 

Finally, the really bad news, is that if a formula or an IT system is your company’s strategy, chances are somebody else on the other side of the market or the world has probably also figured it out and is using it against you.

Incorrect Strategic Implementation: Looking at Wall St., (not to blame them but they are rich with examples of how not to do it) many Wall St. firms had very high powered Market Risk, Credit Risk, and Operational Risk committees, and they still failed. This is like LTCM, with their two Nobel Laureates, all over again.

Lets not just point a finger at these committees. Wall St. collapsed in spite of Sarbanes Oxley Act of 2002. Wall St., collapsed 5-6 years after Sarbanes Oxley came into effect.

The point here is that implementation failed.

For Corporate Strategy advice, please contact Ben Solomon at QuantumRisk LLC. (Note fix email address)

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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.