What are Credit Ratings Good For?

On the front page of today’s Wall Street Journal (well, the on-line version anyway), there’s an article about how the major credit rating agencies ‘failed to see defaults coming.’  You can read some of the article on their public site, but you’ll need an on-line subscription for the entire text.  The point of the article is that a given country’s or company’s official credit rating usually severely underpredicts the real probability of default.  This is not news in the industry.

A few years ago as the credit crunch was getting underway, several of our business partners asked if we could model corporate bond credit risk using the official ‘big three’ credit ratings as inputs.  While this initially sounded pretty straightforward, my crack team of financial engineers showed me why it really doesn’t work.  Basically, there is supposed to be a relationship between the credit rating and the chance of default.  S&P claims that a rating of ‘single B’ means there is a 2% chance of default within 1 year.  But if you look at 100 different companies rated ‘B’, far more than 2 of them defaulted in the coming year.  Given how poorly the ratings predict what they are supposed to, my quants emphatically refused to build a system that used credit ratings as the basis of default.  But at the same time, many of our competitors were selling exactly that type of system – one that estimated how much money a fund could lose based on the credit ratings of their investments.  And these systems were selling very well.  The only problem was that they, just like the big credit ratings, severely underpredicted the real risk.

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Stress Testing the United States

A curious thing happened on the way to August 2… Last week, professional money managers started asking us to perform Stress Tests on the US Government.  Gulp.

My company provides a variety of different risk management services for professional investors.  Among them is a ‘Stress Test’ for Money Market Mutual Funds in which we simulate a number of different simultaneous market downturns to show the fund’s managers what impact these hypothetical events would have on their ability to continue to provide liquidity at $1.00/share.  Typically, we simulate three simultaneously ‘bad’ things happening: interest rates rising (so bonds lose value), credit spreads widening (so bonds lose value) and fund investors increasingly redeeming shares (so the fund has to sell bonds – potentially at a loss – to have the cash to pay back investors, a vicious downward spiral).  For most of our clients, we perform these stresses monthly.

Last week, we got calls from several clients asking us to run these stresses in the middle of the month because they’re concerned with the US not raising the debt ceiling.  The fact that professional money managers are paying us to perform a stress test on the US Government should ring alarm bells. Let me describe what they’re worried about:

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This Isn’t Rocket Science…

My firm’s been growing quite a bit recently and there are plenty of new faces around the office.  In one of the ‘getting to know you’ water-cooler type conversations (which never happen anywhere near the actual water cooler), someone asked me why I went into risk management and why I didn’t stay in nuclear physics.  “Well, this isn’t rocket science” I told him.  “It’s much harder than rocket science.”  He thought I was being sarcastic.  Here’s a summary of the explanation I gave:

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Making Sense of Risk Reports

We’re doing more and more business with firms that are less and less familiar with absolute risk measures.  Many institutional investors are familiar with Relative risk measures, like Tracking Error and Beta, but they are much less familiar with things like VaR, Stress Tests, Correlations, and all the other analytics that are standard fare for risk managers.  As Investor Analytics works with more of these institutional investors, its become clear to me that they could use a hand in interpreting their risk reports.  Most of our competitors (which my marketing department has told me it’s never a good idea to mention by name — see, I can learn something) don’t offer interpretation or consulting services.  They just produce the reports and send them out.  But I think it’s much more valuable to have a guide on how to make sense of them.

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Visual Correlations Part 1

I like pictures.  In my copious spare time I’m a black-and-white photographer.  I use film.  I have my own darkroom.  Maybe that’s why I think of correlations in terms of diagrams instead of just as numbers.

Correlations are the key to understanding how diversification lowers financial risk, and diversification is one of the simplest and oldest ways that humans have managed risk.  By spreading around the chances of bad things happening, you lower your risk.  “Don’t put all your eggs in one basket” is obvious, even to children. Most importantly – it works.  Financial portfolio management implements this advice by applying investment guidelines like “no more than 5% invested in any one company.”  This automatically forces the investments to be spread over at least 20 different companies.

But what happens if those 20 companies’ values/stock prices start rising and falling together – the equivalent of putting your eggs into 20 baskets and then carrying them all down a steep hill?  The relevant question then becomes ‘How much do you reduce your risk by spreading the investments across many companies if their stock prices are related?’  The short answer is that it all depends on the correlation between the assets.  If two stocks are highly correlated, then putting money into the second stock doesn’t lower the overall risk at all.  But if the two stocks are uncorrelated, then you can reduce your risk substantially by investing in the second stock. Read more of this post

Not Even Wrong

Comments are Welcome – please add a comment.

I had a bit of a disagreement the other day with a colleague about the interpretation of beta and correlation.  He claimed that if a fund has a high beta to a particular index, that meant that it also has a high correlation to the index.  He even cited a Wikipedia article to support his point of view.  Unfortunately, it’s rather common to believe the whole story is, as the article states, that “correlation measures direction, not magnitude” and “beta takes into account both direction and magnitude.”  If that’s most of what you know about beta and correlation, then it’s easy to make the

conclusion that my friend made.

Why do so many people make this mistake? Beta and Correlation are commonly used terms in traditional investing – mainly by mutual funds – and they’ve been thrown around long enough that people are familiar with the terms, so they think they understand them.  “Oh sure, I’ve heard of that before” and therefore, the person thinks to him/herself, I don’t need to know anything more about the topic.  In what’s called the Long Only world (think mutual funds), beta is regarded as a risk measure because of a simple relationship between the fund and its benchmark: beta is correlation times the fund’s volatility divided by the benchmark’s volatility.  Benchmarks are chosen to closely resemble the fund – for a technology fund, you pick a tech index as the benchmark.  For a health-care fund, you pick a health-care index.  Obviously.  A mutual fund’s benchmark is chosen because the two are highly correlated.  Mathematically, that means the correlation is close to 1.00.  So, in this case (and in this case only) beta is basically the ratio of the volatility of the fund to the volatility of the benchmark.  If beta is 1.1, that means the fund is 10% more volatile than the benchmark, and if the beta is 0.9, that means it’s 10% less volatile than the benchmark.  It all works because of the formula beta = correlation * (vol_fund / vol_index).  The problem is that people forget important aspects of the formula – like that correlation is baked into the beta – and they only remember “beta is a relative measure of volatility” or “beta is the slope of the regression line.”  Please keep in mind everything I’ve written in this past paragraph is only true for traditional long-only funds, like mutual funds. Read more of this post

Chocolate or Vanilla?

As always, comments are welcome.

Risk Measures basically comes in two flavors: chocolate or vanilla.  Really.  Sometimes it’s described as two styles: “Impact” or “Probability”, but I think of them as flavors.

One school of thought is that the best way to estimate a portfolio’s risk is to identify what drives each security’s value and then stress all those factors.  This tells you what impact you can expect on your portfolio for each of those different stresses.  Let’s call this Vanilla.

The chocolate school says that the best way to estimate a portfolio’s risk is not to treat each asset class separately like the plain vanilla people, but rather to estimate probabilities of various losses. Read more of this post

Risk at the Limit

You have a problem.  A big problem.  And it’s unfortunately a common problem for a Risk Officer.  What do you do when your risk measures are all in the red zone except for one of them, which happens to be your boss’s favorite?  And he just happens to be the Head Trader of your fund.  It’s like an engineer telling the captain of the ship that the engines can’t go any faster – “Captain (in good Scottish brogue), I’m givin’ it all she’s got.”  To which the Captain replies “No, Scotty, look at this other gauge – it says we’re only at half capacity.”  Which gauge is right – the captain’s or the engineer’s?  Fortunately, there is a way to tell.  Unfortunately, captains don’t like to accept it when they’re wrong.

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Beta to the Max

Last week, I was having a really hard time explaining myself until I realized that I was trying to explain in words how to take advantage of the fact that humans perceive images faster and better than we perceive words or numbers.  This has a lot to do with our evolutionary history and with behavioral finance.  My example was the interpretation of a fund’s beta – and my point was that it’s really really hard to get the right interpretation from the numbers without the right picture.  In this case, the right picture makes a BIG difference.  It was really frustrating trying to give an example of what I meant until I realized that I was suffering from the same thing I was trying to explain!  Wow, I guess I really can be that thick.  I should have been showing her the pictures rather than talking about them.  So that’s exactly what I then did. Read more of this post

Gauss’s Fat Tails

The term “fat tails” is thrown around with what I consider reckless abandon.  Most times I find that people use it without having an appreciation for what it really means and then they make the wrong conclusion.  So, I’m going to take a stab at explaining what I think a correct interpretation is.  The first and most prevalent wrong conclusion is that all quants and financial models underestimate the risk of extreme events.  Wrong – there are plenty of ways to reasonably and accurately model tails.  The second is that all models use the Normal Distribution.  Wrong – there’s a host of distributions that can be and are used.  A corollary to that wrong assumption is that VaR (Value-at-Risk), in particular, always uses the Normal Distribution, which is also wrong.  VaR makes no assumptions about what distribution is used, but I’ll have an entirely different set of posts about VaR.  This post will be part one of what may be a series of posts on the topic of fat tails in particular, and this one will be limited to discuss actual data and comparing it to the most commonly used distribution, the Normal curve.

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