## Archive for September 2009

### UNRISK (2)

September 30, 2009

UNRISK Part 2 – Understanding the distribution

(Part One)

Before you completely write this post off as statistical gibberish, and for those of you were fortunate enough to not get exposure to the subject, let’s just see what the distribution looks like.

Not too bad! What you see above is a simple slotting of credit scores across a typical credit portfolio. For the month of June, the scores rate from 1 to 12, with 1 good and 12 evul. The axis on the left hand side shows how much have we bet per score / grade category. We collect the scores, then sort them, then bunch them in clusters and then simply plot the results in a graph (in statistical terms, we call it a histogram). Drawn the histogram for a data set enough number of times and the shape of the distribution will begin to speak with you. In this specific case you can see that the scoring function is reasonably effective since it’s doing a good job of classifying and recording relationships at least as far as scores represent reasonable credit behavior.

So how do you understand the distribution? Within the risk function there are multiple dimensions that this understanding may take.

The first is effectiveness. For instance the first snapshot of a distribution that we saw was effective. This one isn’t?

Why? Let’s treat that as your homework assignment. (Hint: the first one is skewed in the direction it should be skewed in, this one isn’t).

The second is behavior over time. So far you have only seen the distribution at a given instance, a snapshot. Here is how it changes over time.

Notice anything? Homework assignment number two. (Hint: 10, 11 and 12 are NPL, Classified, Non performing, delinquent loans. Do you see a trend?)

The third is dissection across products and customer segments. Heading into an economic cycle where profitability and liquidity is going to be under pressure, which exposure would you cut? Which one is going to keep you awake at night? How did you get here in the first place? Assignment number three.

Can you stop here? Is this enough? Well no.

This is where my old nemesis, the moment generating function makes an evul comeback. Volatility (or vol) is the second moment. That is a fancy risqué (pun intended) way of saying it is the standard deviation of your data set. You can treat volatility of the distribution as a static parameter or treat it with more respect and dive a little deeper and see how it trends over time. What you see above is a simple tracking series that is plotting 60 day volatility over a period of time for 8 commodity groups together.

See vol. See vol run… (My apologies to my old friend Spot and the HBS EGS Case)

If you are really passionate about the distribution and half as crazy as I am, you could also delve into relationships across parameters as well as try and assess lagged effects across dimensions.

The graph above shows how volatility for different interest rates moves together and the one below shows the same phenomenon for a selection of currency pair. When you look at the volatility of commodities, interest rates and currencies do you see what I see? Can you hear the distribution? Is it speaking to you now?

Nope. I think you need to snort some more unrisk! Home work assignment number four. (Hint: Is there a relationship, a delayed and lagged effect between the volatility of the three groups? If yes, where and who does it start with?)

So far so good! This is what most of us do for a living. Where we fail is in the next step.

You can understand the distribution as much as you want, but it will only make sense to the business side when you translate it into profitability. If you can’t communicate your understanding or put it to work by explaining it to the business side in the language they understand, all of your hard work is irrelevant. A distribution is a wonderful thing only if you understand it. If you don’t, you might as well be praising the beauty of Jupiter’s moon under Saturn’s light in Greek to someone who has only seen Persian landscapes and speaks Pushto.

To bring profitability in, you need to integrate all the above dimensions into profitability. Where do you start? Taking the same example of the credit portfolio above you start with what we call the transition matrix. Remember the distribution plot across time from above.

THis has appeared previously in Jawwad’s excellent blog.

### Black Swan Free World (3)

September 29, 2009

On April 7 2009, the Financial Times published an article written by Nassim Taleb called Ten Principles for a Black Swan Free World. Let’s look at them one at a time…

3. People who were driving a school bus blindfolded (and crashed it) should never be given a new bus. The economics establishment (universities, regulators, central bankers, government officials, various organisations staffed with economists) lost its legitimacy with the failure of the system. It is irresponsible and foolish to put our trust in the ability of such experts to get us out of this mess. Instead, find the smart people whose hands are clean.

Since I cannot claim to have completely clean hands, I will simply point to the writings of Hyman Minsky.  His Financial Instability Hypothesis describes how a financial system goes to the extremes of leverage that creates a crash like what we just experienced.  He wrote this in the 1980’s and early 1990’s and then did not feel that there was much chance of the extreme part of that cycle happening any time soon.  He thought that the Fed had enough of a handle on the financial system to keep things from getting to a blow up state.

However, he did mention that with the advent of sources of debt and leverage and money outside of the traditional financial system, that if those elements grew enough then they could be the source of a severe problem.

How prescient.

In addition to reading what Minsky wrote, we should also be studying the thinking of those who totally avoided the sub prime securities that caused so much problems for so many very large financial institutions or who were in but got out in time to avoid fatal damages.

Those are often the people with the common sense that we should be using as the basis for the way forward.

Risk management programs need to have a deliberate risk learning function, where insights are developed from the firm’s losses and near misses as well as from others losses and near misses.

In this crisis, we should all seek to learn from those who were not enticed into the web of false knowledge about the riskiness of the sub prime securities.   One of the most interesting that I hear at the time when the markets were seizing up was that those who had escaped were too unsophisticated to have gotten into that market.

I spoke to one of those severely unsophisticated people on the buy side and he said that he never did spend too much time looking into the CDOs.  He said that he knew what the spreads were on straight mortgage backed securities.  And he had some idea of how many additional people were getting a slice in the creation of the CDOs.  And then he knew that the CDOs were promising higher yields for the same credit rating as the straight mortgage backed securities.   At that point, he was sure that something did not add up and he moved on to look at other things where the numbers did add up.  I guess he was just too unsophisticated to understand the stochastic calculus needed to explain how 2-1-1-1 = 3.

We need to learn that kind of unsophistication.

Black Swan Free World (10)

Black Swan Free World (9)

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Black Swan Free World (6)

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Black Swan Free World (4)

Black Swan Free World (3)

Black Swan Free World (2)

Black Swan Free World (1)

### Black Swan Free World (2)

September 27, 2009

On April 7 2009, the Financial Times published an article written by Nassim Taleb called Ten Principles for a Black Swan Free World. Let’s look at them one at a time…

2. No socialisation of losses and privatisation of gains. Whatever may need to be bailed out should be nationalised; whatever does not need a bail-out should be free, small and risk-bearing. We have managed to combine the worst of capitalism and socialism. In France in the 1980s, the socialists took over the banks. In the US in the 2000s, the banks took over the government. This is surreal.

Most assuredly the socialization of losses and privatization of gains is what has anyone outside of the banking sector furious. Within the sector, everyone seems to believe that they earned their share of the gains. Think about what you hear about the bonus scheme at the banks – the investment banks are said to be paying out about 50% of gains before bonus. I imagine that puts them approximately on par with the hedge funds, if the banks profit figure takes out overhead before calculating the 50% ratio. So the bank incentive comp is based upon the hedge fund incentive comp. Amazingly, the hedge fund managers manage to convince investors to give them their money and lenders to advance them funds to leverage without any hint of a bailout ever in their future. The hedge fund managers generally walk away from the fund when things go wrong and they are no longer have a chance for outsized gains.

Do the bank shareholders understand that they are really investing in a highly leveraged hedge fund? The folks getting those bonuses surely understand that.

Is this the worst of capitalism and socialism? Probably so.

How do we get out of this? It seems that rather than limiting compensation, we ought be assuring shareholders and debt holders of any firms that structure their compensation like hedge funds that they should expect to be treated like hedge funds in the event of failure. Goodbye, no regrets.

One way of looking at the compensation issue is to focus on time frame.  There are four time frames to consider:

1.  The employees – the recipients of the bonuses.  Their time frame looks backwards.  They want to be paid for the value that they created for the firm.  They want to be paid in cash for that value.

2.  The Short Term shareholders.  Their time frame is in quarters.  They are most interested in what will be posted as the next quarterly earnings.  They want to be able to cash out their investment at the point where they believe that the next quarter’s earnings will not grow enough to support future price increases.

3.  The Long Term shareholders.  Their time frame is in years – probably 3 – 5 years.  Which is the expected holding period for a long term shareholder.  They are looking for growth in value compared to share price and will usually sell when they believe that the intrinsic value of the firm starts to catch up with the market value.

4.  The public.  Our time frame is our lifetime.  We need to have a financial system that works our entire lifetime.   The public gets nothing from the changes in value of the financial system but ends up paying off the losses that exceed the capacity of the financial system.

The compensation and prudential capital for banks is a trade-off between the interests of all four of these groups.  In the run up to the crisis, the system tilted in the favor of employees and short term investors to the extreme detriment of the long term shareholders and public.

So the solution is likely to be best if the interests of the long term shareholders are made more important.  Right now, a large, possibly most of the long term shareholders are index funds.  Index funds are extremely unlikely to want to have any say in corporate governance or compensation.

So you could surmise that the compensation aspect of the crisis and the drift of all things corporate to fall under the sway of short term investors is a result of the prevalence of index funds.

Black Swan Free World (10)

Black Swan Free World (9)

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Black Swan Free World (3)

Black Swan Free World (2)

### Custard Cream Risk – Compared to What???

September 26, 2009

It was recently revealed that the custard Cream is the most dangerous biscuit.

But his illustrates the issue with stand alone risk analysis.  Compared to what?  Last spring, there was quite a bit of concern raised when it was reported that 18 people had died from Swine Flu.  That sounds VERY BAD.  But Compared to What?  Later stories revealed that seasonal flu is on the average responsible for 30,000 deaths in the US.  That breaks down to an average of 82 per day annually, or more during the flu season if you reflect the fact that there is little flu in the summer months.  No one was ever willing to say whether the 18 deaths were in addition to the 82 expected or if they were just a part of that total.

The chart below suggests that Swine flu is significantly less deadly than the seasonal flu.  However, what it fails to reveal is that Swine Flu is highly transmissable because there is very little immunity in the population.  So even with a very low fatality rate per infection, with a very high infection rate, expectations now are for more than twice as many deaths from the Swine Flu than from the seasonal flu.

For many years, being aware of the issue I tried to make a comparison whenever I presented a risk assessment.  Most commonly, I used a comparison to the risk in a common stock portfolio.  Was the risk I was assessing more or less risky than the stocks.  I would compare both the average return, the standard deviation of returns as well as the tail risk.  If appropriate, I would make that comparison for one year as well as for many years.

But I now realize that was not the best choice.  Experience in the past year reveals that many people did not really have a good idea of how risky the stock market is.  Many risk models would have pegged the 2008 37% drop in the S&P as a 1/250 year event or worse, even though there have now been similar levels of loss three times in the last 105 years on a calendar year basis and more if you look within calendar years.

The chart above was made before the end of the year.  By the end of the year, 2008 fell back into the 30% to 40% return column.  But if your hypothesis had been that a loss that large was a 1/200 event, the likelihood of one occurrence in a 105 year period is only about 31%.  Much more likely to see none (60%).  Two occurrences only about 8% of the time.  Three or more, only about 1% of the time.  So it seems that a 1/200 return period hypothesis has about a 99% likelihood of being incorrect.  If you assume a return period of 1/50 years, that would make the three observations a 75th percentile event.

So that is a fundamental issue in communicating risk.  Is there really some risk that we really know – so that we can use it as a standard of comparison?

The article on Custard Creams was brought to my attention by Johann Meeke.  He says that he will continue to live dangerously with his biscuits.

### ERM: Law of Unintended Consequences [2]

September 25, 2009

From Neil Bodoff

One of the reasons that so many counterparties bought CDS protection [from the same counterparty, precipitating a crisis] was their desire to reduce their regulatory capital requirements. So the regulatory framework had high capital requirements for credit risk, but low capital requirements when the credit risk was hedged. Basically the regulatory framework created a strong incentive for all banks to simultaneously execute the same strategy of hedging risk via CDS. Lessons are: [1] Whereas individual firms in a competitive market may pursue various strategies, the government’s monopoly on regulation might create a homogenizing effect on firms’ behavior, thus concentrating risk. Thus the regulatory framework itself becomes a systemic risk and thus requires extra scrutiny and care. [2] For any regulatory framework, the designers ought to choose someone to “roleplay” the part of firms trying to minimize regulatory capital requirements, so as to understand the behaviors and countermeasures the firms might take in response to the regulatory demands. [3] Beware of unintended consequences.

### The Cheeky, the Funky and the Dummy Monkey… (2)

September 25, 2009

From Stelios Ioannides (risk manager)

Continuation of earlier post.

Who is to blame?

OK enough, I agree with you: this is an exaggeration of the situation or the situations that we are currently experiencing but reality can be quite close. What happened in the credit sub-prime crisis can only be justified, in my opinion, by such “monkey” logic. At the end of the day, it’s about designing products, valuing (appropriately) risk, and getting on board the “right” clients with a “desirable”, for our purposes profile. Who is doing that?  And how? The industry failed spectacularly on that. It allowed to this “monkey” concept to grow and to gain potential.

Who cares about Value at Risk or CTE and the associated graphics, if there is no clue at all on how these “interesting” numbers were derived in the first place? Using a number with out know the source it is like having a map with numbers but with no street names. You do not know where you are, you might know where you are heading (vaguely) but there is absolutely no way you can reach your destination.

Having some well defined risk measures is just a well accepted methodology that justifies capital intensive and risk sensitive decisions at the big scale. So if you are applying it wrongly, things can fail dramatically, at a huge scale, causing chaos. And of course, when things go wrong the funky or the dummy monkeys will be blamed… These are the ones that will loose their jobs. The cheeky ones stay alive and are the ones that will be hiring soon again.

The way forward

Understanding the details and being aware with the fundamentals is crucial is this arena. “Understanding” is about having the right combination of skills and applying these fundamentals. It is also about being able to realize how decisions that might be executed in interrelated contexts like pricing, capital reserving and hedging (just to mention a few) might be derived by ones work.

Knowledge exists, technology exists and in my opinion, it is a pity that people still stick to the old practices.  There is strong need to refresh or at least fine tune these well established “ways of doing things”. In no situation, we should act like “robots” that mechanically do things.  We need monkeys that are owners and responsible of their piece of work regardless how small that is.

If we fail to do that then the “disease” might propagate in otter industries, and in that case of course, the consequences might scary (at least to say).  We spent millions or even billions for initiatives like Basel, but we have to make sure that some basic, common sense and ethical rules are being obeyed at all levels.

Risk industry calls for better quality transparency and people should soon or later realize that sharing knowledge and information and aligning interests and objectives would benefit, in most cases all parties (of the same side) involved in the project or deal. The way assumptions are derived is crucial. At the same time, being able to control the behavior of clients is of paramount importance.  How this is achieved? A way is possibly by proper underwriting and classification.

Conclusions

We are working in various dimensions, we are dealing with risk free worlds, real words, real market assumptions, marking to market and so on and so fourth. Concepts like “Stress Testing” are gaining momentum and potential.  In our daily work we have to face concepts like implied volatilities, volatility surfaces, “short-selling” (it took me a while to get this right, honestly) and a few even more complicated terms that I do not want to even to mention them here. The list of these complicated terms is endless and growing fast.

In any case, people have the duty to use these concepts in a consistent and ethical way.  Sticking to basic and rather simplistic approaches with regards o problem solving is not wise in the fast world we are living.

We have the duty to teach the new generations how to synthesize skills and knowledge and judge impartially and ethically. I personally believe that the future belongs to the people that the have the courage to ask right questions and the patience to apply the fundamentals … it is the duty of each one of us to find out what that really means.

Perhaps, we could elaborate more one that but due to lack to time I cannot. Hopefully this won’t be the case when I will have to deliver a super important risk project in the future. What am I? Well something in between the dummy and the funky monkey (hopefully closer to the later or better the former?)…

### No Thanks, I have enough “New”

September 24, 2009

It seems sad when 75 year old businesses go bust.  They had something that worked for several generations of managers, employees and investors.  And now they are gone.  How could that be?

There are two ways that old businesses can come to their demise.  They can do it because they stick to what they know and their product or service  (usually) slowly goes out of fashion.  Usually slowly, because all but the most ossified large successful companies can adapt enough to keep going for quite some time, even when faced by competition with a better business model/product or service.  Think of the US auto industry slowly declining for 40 years.

The second way is a quick demise. This usually happens after the old company chooses to completely embrace something completely new.  If their historic business is in decline, many large old firms are on the look out for that new transformational thing.  The mistake that they sometimes make is to be in much too much of a hurry. They want to apply their size advantage to the new thing and start getting economies of scale in addition to early adopter advantages.

The failure rate of new business is very, very high.  A big business that jumps to putting a large amount of its resources into the new business will be transforming a solid longstanding business effectively into a start-up.  But rarely do the big businesses in restart mode deliver anything like start-up returns.  So investors bare the risks of of the start-up with the returns only slightly higher than long term averages.

This is a clear example of when the CEO needs to be the risk manager.  The established firm needs to have a limit for “New” businesses.  The plan for the new business should reflect an orderly transition between the franchise business and what MAY become the new franchise.  This requires the CEO to have a time frame in mind that is appropriate for a business that may have existed before he/she was born and that, if the risks are managed well, should exist long after they are gone.

There are good underlying reasons why the “New” needs to be limited for a company with long term survival plans.  “New” involves several risks that a well established firm may have mastered a generation ago and have relegated to the corporate unconscious.

The first is execution risk.  The established firm will doubtless be excellent at execution of its franchise business.  But the “New” will doubtless require different execution.  An example of this from the insurance industry, when US Life Insurers started into the equity linked products, man of them experienced severe execution problems.  Their traditional products involved collecting cash and putting it into their general fund.  They only provided annual information to their customers if any.  Their administrative systems and procedures were set up within an environment that was not particularly time sensitive.  The money was in the right place, their accounting could catch up “whenever”.   With the new equity linked products, exacting execution was important.  Money was not left in the general fund of the insurer but needed to be transferred to the investment manager within three days of receipt.  So insurers adapted to this new world by getting to the accounting and cash transfers “whenever” but crediting the customer with the performance of their chosen equity fund within the legal 3 day limit.  This worked out fine with small timing delays creating some small gains and some small losses for the insurers.  But the extended bull market of the late 1990’s made for a repeated loss because the delay of processing and cash transfer meant that the insurer was commonly backdating to a lower purchase price for the shares than what they paid.  Some large old insurers who had jumped into this new world with both feet were losing millions to this simple execution risk.  In addition, for those who were slow to fix things, they got hit on the way down as well.  When the Internet bubble popped, there were many, many calls for customer funds to be taken out of the equity funds.  Slow processing meant that they paid out at a higher rate than what they received from their delayed transactions with the investment funds.

The insurers had a well established set of operational procedures that actually put them at a disadvantage compared to start-ups in the same business.

The second is the “unknown” risk.  A firm that has been operating for many years is often very familiar with the risks of its franchise business.  In fact, their approach to risk management for that business may well be so ingrained, that it is no longer considered a high priority.  It just happens.  And the risk management systems that have been in place may work well with little active top management attention.  These organizations are usually not very well positioned to be able to notice and prepare for the new unknown risks that the new business will have.

The third is the “Unknowable”.  For a new activity, product or business, you just cannot tell what the periodicity of loss events or the severity of those events.  That was one of the mistakes in the sub prime market  The mortgage market has about a 15 year periodicity.  Since a large percentage of people operating in the sub prime space were not in that market the last time there was a downturn, they had no personal experience with the normal cycle of losses in the mortgage market.  Then there was the unknowable impact of the new mortgage products and the drastic expansion into sub prime.  It was just unknowable what would be the periodicity and severity of losses in the “new” mortgage market.

So the point is that these things that are observed about the prior “new” things can be learned and extrapolated to future “new” things.

But the solution is not to never do anything “new”, it is to keep the “new” reasonable in proportion to the rest of the organization, to put limits on “new” just like there are limits on any other major aspect of risk.