Management by Onside Kick

Many American football fans can recall a game when their team drove the ball 80 or more yards in the waning moments of the game to pull within a touchdown of the team that had been dominating them. Then they call for the on side kick – recover the ball and charge to a win within a few more plays.

But according to NFL stats, that onside kick succeeds only 20% of the time in the waning minutes of the game.

Mid game onside kicks – that are surprises – work 60% of the time.

But mostly it is the successful onside kicks that make the highlights reel. RISKVIEWS guesses that on the highlights those kicks are 80% or more successful.

And if you look back on the games of the teams that make it to the Super Bowl, they probably were successful the few times that they called that play.

What does that mean for risk managers?

Be careful where you get your statistics. Big data is now very popular. Winners use Big Data. So many conclude that it will give better indications. But make sure that your data inputs are not from highlight reels or from the records of the best year for a company.

Many firms use default data collected by rating agencies for example to parameterize their credit models. But the rating agencies would point out that the data is from rated companies only. This makes little difference for rated Bonds. There the bonds are rated from issue to maturity or default. But if you want to build a default model of insurers or reinsurers then you need to know that many insurers and some reinsurers will drop their rating if it falls below a level where it hurts their business. So ratings transition statistics for insurers are more like the highlight reels below a certain level.

Some models of dynamic hedging strategies were in effect taking the mid game success rates and assuming that they would apply in bad times. But like the onside kick, things worked very different.

So realize that a business strategy and especially a risk mitigation strategy may work differently when things have gone all a mess.

And an onside kick is nothing more than putting the ball in play and praying that something good will happen.

Advertisement

Society and the Default Put

The idea of the Limited Liability Corporation is one of the innovations that is credited with making capitalism work. The structure allows a person or group to form a business without risking their entire fortune. That is the way that economics textbooks say it. It sounds like all upside.

But wait a minute. Think about it like a risk manager. A real risk manager, not the hucksters who sold the “risk goes away if you split it fine enough” or the “no increase in total risk because of diversification benefits” stories.

A real risk manager knows that a loss is a loss. A dollar (or euro, or pound) is a dollar. Losses do not disappear EVER. Unless you do the work to prevent them.

And limited liability is NOT a loss prevention program. It prevents losses from transmitting to a certain party. The owner of the company. But someone always gets those losses.

Think about it for just a fraction of a second. If a company has obligations that it cannot pay, who has a loss? You figured it out; their counterparties take the loss. It might be customers, suppliers, subcontractors, their bank, or bondholders. The limited liability idea protects only one group – the owners/shareholders. Everyone else has unlimited liability!

What we saw in the crisis, if you owe the bank $100,000 they own you. If you owe the bank $10,000,000 then you own the bank.

This limited liability idea is totally embedded now. Everyone believes that they have the RIGHT to create problems for everyone else that deals with them and JUST WALK AWAY.

In ancient times, the ultimate collateral was the debtor’s personal freedom. A person who defaulted on a debt became an indentured servant of the lender in the case of default. This idea persisted in one form or another until the 1800s when debtors prisons became out of favor. The US was one country that led the way on this movement. The US has always had a much easier attitude to bankruptcy. There has always been much less stigma attached to bankruptcy along with the easier legal climate.

So the system works this way – people and businesses can go bankrupt easily and put their excess losses onto their counterparties. And in reaction to this, counterparties must be careful who they do business with.

That means that Credit Risk Management is a fundamental aspect of the business environment.

However, when you recognize the underlying fundamental reason for that statement, you may question whether the new statistical based Credit Risk Management that has developed over the past 25 years actually satisfies the fundamental need of the system.

Under the statistical approach to CRM, diversification is the key risk management tool. This has replaced the time consuming and labor intensive credit underwriting process.

But it is the underwriting process that works to counter balance the default put that is implicit in the bankruptcy rules.

Without the underwriting, the statistical process will simply not work. It will give totally wrong information. That is because statistics does not work on any old bunch of numbers. Statistics only works on homogeneous sets of numbers.

Let’s review. The default put creates a situation where a person or a firm can take on obligations that they cannot repay AND they will not be held responsible to repay. When people or businesses operate AS IF they were going to pay obligations, then they can receive value from counterparties that is in excess of the value that they will repay. So their counterparties need to police this imbalance.

Statistical CRM means that the lender will make many loans with the expectation that only a few will fail to repay and there will be limited losses from those failures. But once borrowers notice this (or intermediaries who have a better chance to notice) their best outcome is to borrow as much as they can, to leverage up as much as possible. Their upside in the event that everything turns out well is then enormous and they suffer none of the downside.

So statistical CRM leads directly to deterioration in credit quality through excess leverage. No one is actually watching to make sure that the credit risk per loan is staying constant.

And the main risk management tool of diversification fails when the loans themselves become the major source of risk. The correlation between excessive lending and defaults is very high. It is different from the correlation between loans that can be repaid easily.

All this results directly from that default put.  You need to understand the true dynamics of the system if you want to get your risk management right.

Soverign Default Risk

Perspective is very important for a risk manager. That is because lack of perspective leads to many of the largest mistakes.in thinking about the cause and likelihood of loss events.

Regarding the US debt ceiling manufactured crisis, there is very interesting perspective on the issue of the US Federal Debt in an article in the NY Times.  The story links the current Tea Party movement all the way back to Jefferson and Madison.  It seems that the US has always had a major faction strongly opposed to big government and government debt.

However, Riskviews would suggest that some have taken a valid disagreement about the size of government and the level of debt and used that to manufacture a crisis that has the potential to create a second major global recession of the size and scope of the one we have not yet recovered from.

But if you have read the story of the 1930’s history, you will see that is exactly what happened then.  Government policy and actions during the 1930’s took several major turns as the economy staggered up and down.  To this day, there is no agreement of whether one set of government actions or the movements in the opposite direction were the cause or the solution to the problem.

We seem destined to repeat the same sort of lurching process to find our way out.

In fact, we will never know which really works – spending or austerity – to help with a bad part of the business cycle.

Another great source of perspective on Sovereign Default is the Reinhart, Rogoff book This Time is Different.  The book goes through hundreds of years of history and dozens of sovereign defaults.  One of their main conclusions is that sovereign default is usually a politically driven event, rather than a financially driven event.  The drama in Greece follows the historical patterns described in the book.  The involvement of the rest of the EU in the Greece situation is unusual, but not at all unique.

Reinhart and Rogoff make the case that sovereign defaults are mostly political, rather than economic.  That is the thinking that seems to motivate S&P in their downgrade decision on the US debt.  S&P says that

we have changed our view of the difficulties in bridging the gulf between the political parties over fiscal policy, which makes us pessimistic about the capacity of Congress and the Administration to be able to leverage their agreement this week into a broader fiscal consolidation plan that stabilizes the government’s debt dynamics any time soon.

But it is unclear to RISKVIEWS whether there is not also a major long term economic problem for most of the G20 economies.  The demographic imbalances may prove the downfall of one or several of the major economic powerhouses of the past 50 years.

It’s All Relative

Another way to differentiate risks and loss situations is to distinguish between systematic losses and losses where your firm ends up in the bottom quartile of worst losses.

You can get to that by way of having a higher concentration of a risk exposure than your peers.  Or else you can lose more in proportion to your exposure than your peers.

The reason it can be important to distinguish these situations is that there is some forgiveness from the market, from your customers and from your distributors if you lose money when everyone else is losing it.  But there is little sympathy for the firm that manages to lose much more than everyone else.

And worst of all is to lose money when no one else is losing it.

So perhaps you might want to go through each of your largest risk exposures and imagine how either of these three scenarios might hit you.

• One company had a loss of 50% of capital during the credit crunch of the early 1990’s.  Their largest credit exposure was over 50% of capital and it went south.  Average recoveries were 60% to 80% in those days, but this default had a 10% recovery.  That 60% to 80% was an average, not a guaranteed recovery amount.  Most companies lost less than 5% of capital in that year.
• Another company lost well over 25% of capital during the dot com bust.  They had concentrated in variable annuities.  No fancy guarantees, just guaranteed death benefits.  But their clientele was several years older than their average competitors.  And the difference in mortality rate was enough that they had losses that were much larger than their competitors, who were also not so concentrated in variable annuities.
• Explaining their claims for Hurricane Katrina that were about 50% higher as a percent of their expected total claims, one insurer found that they had failed to reinsure a large commercial customer whose total loss from the hurricane made up almost 75% of the excess.  Had they followed their own retention rules on that one case, that excess would have been reduced by half.

So go over your risks.  Create scenarios for each major risk category that might send your losses far over the rest of the pack.  Then look for what needs to be done to prevent those extraordinary losses.

Reliance on Risk Management

Many life insurance firms may not really be aware of the degree to which they are exposed to risk.

When these firms write a life insurance policy, they are immediately exposed to a significant amount of gross risk.  Looking at the entire liability book, the risk is immense.  Many multiples of capital.

I  am not talking about the fact that face amounts of insurance far exceed premiums.  What I am trying to point out is that there is a very large amount of risk created by accepting premiums with the guarantee of certain surrender values.  (There is somewhat more mortality risk there than many insurers may realize, but it is not significant on a gross basis compared to the interest rate risk on the cash values.)

Insurers tend to forget about this because there is a very longstanding practice of offsetting that risk by investing funds (called the assets) of the life insurer.

The folks who are insisting on market value accounting for insurance liabilities are trying to point out this fact of life.

In many markets, the insurer will then take investment risk – credit or market – with the investments and finally they will do something further that deeply offends the market value folks.

They will split some of the money that they are paid in risk premium with their policyholder/customer.

This practice can probably be traced back to the time when the predominant form of life insurance was mutual life insurance.  Under that structure, the policyholder is thought to share the risk of the insurance company, and it therefore makes sense that they would share in the risk premium.

Non-mutual firms found that they could not compete with this because most customers did not understand that they had the choice of one level of return within their insurance policy at a certain level of risk and a lower level of return with a lower amount of risk. The customers usually just saw the net return.  Risk was not communicated well.  Usually risk was communicated very vaguely while return seemed to be really tangibly conveyed.

So what the market value folks are trying to accomplish is to overcome hundreds of years of confusion about the actual level of risk of an insurer.

You see, risk premiums are usually collected in advance of losses.  If an insurer is paying some fraction of its risk premiums to its customers, and it does not have a loss sharing mechanism as is fundamental to a mutual insurance scheme, then it is acting similarly to a leveraged hedge fund.

The resources of the insurer to absorb losses is the capital, but the exposure to losses extends to a much larger pool of insured funds.

So the market valuing of insurance liabilities is really a risk recognition exercize.  It is trying to make a point, that point being that the practices of insurers have evolved to become much riskier than what they had been in the past.  And the mark to market system would force insurers to acknowledge that additional risk at the point at which they decide to tak on the risk.

Now, it appears that IFRS accounting is heading a different direction.  The IASB seems to be backing away from a full mark to market system for assets.  This will wreck havoc on the balance sheets and income statements of the insurers who will be marking their liabilities but not their assets to market.

Sort of like the mess that has existed in the other direction for some time not, were insurers in many situations have been marking assets, but not liabilities to market.

Insurance has a reputation for totally opaque financial reporting.  It seems that this reputation will continue to be well deserved.

Assumptions Embedded in Risk Analysis

The picture below from Dour VanDemeter’s blog gives an interesting take on the embedded assumptions in various approaches to risk analysis and risk treatment.

But what I take from this is a realization that many firms have activity in one or two or three of those boxes, but the only box that does not assume away a major part of reality is generally empty.

In reality, most financial firms do experience market, credit and liability risks all at the same time and most firms do expect to be continuing to receive future cashflows both from past activities and from future activities.

But most firms have chosen to measure and manage their risk by assuming that one or two or even three of those things are not a concern.  By selectively putting on blinders to major aspects of their risks – first blinding their right eye, then their left, then by not looking up and finally not looking down.

Some of these processes were designed that way in earlier times when computational power would not have allowed anything more.  For many firms their affairs are so very complicated and their future is so uncertain that it is simply impractical to incorporate everything into one all encompassing risk assessment and treatment framework.

At least that is the story that folks are most likely to use.

But the fact that their activity is too complicated for them to model does not seem to send them any flashing red signal that it is possible that they really do not understand their risk.

So look at Doug’s picture and see which are the embedded assumptions in each calculation – the ones I am thinking of are the labels on the OTHER rows and columns.

For Credit VaR – the embedded assumption is that there is no Market Risk and that there is no new assets or liabilities (business is in sell-off mode)

For Interest risk VaR – the embedded assumption is that there is no credit risk nor new assets or liabilities (business is in sell-off mode)

For ALM – the embedded assumption is that there is no credit risk and business is in run-off mode.

Those are the real embedded assumptions.  We should own up to them.

Lessons for Insurers (5)

In late 2008,  the The CAS, CIA, and the SOA’s Joint Risk Management Section funded a research report about the Financial Crisis.  This report featured nine key Lessons for Insurers.  Riskviews will comment on those lessons individually…

5. It is important to develop a counterparty risk management system and establish counterparty limits.

Insurers need to fully understand several things about both credit and reinsurance to get this right.

First of all, in a credit or reinsurance situation, the insurer is usually trading uncertainty in the “expected” range of probabilities for a potential loss at a very high attachment point, the failure point for the counterparty.

Second of all, the insurer needs to recognize that the failure of their counterparty usually does not in any way change their obligation.  When an insurer buys a bond, they are usually responsible to make payments to their policyholder regardless of whether the bond is good.  When an insurer buys reinsurance they are still responsible to pay claims whether or not the reinsurer is able to meet its obligations.

Recognize that in almost all cases, the standard risk management terminology is flawed.  Risk is usually not transferred.

The other consideration that is important to insurers is that they need to look for counterparty exposures everywhere in their operations.  In each of their insurance lines as well as in every part of their investment portfolio.  In firms where traditionally insurance and investments are treated as completelyt separate silos, risk managers are finding that both sides of the house are sometimes dealing with the exact same counterparties.  Aggregation and management of these concentrations is key.

And finally to scare you completely, a good way to think of counterparty risk is that you are bring a fraction of the entire balance sheet on to your balance sheet in return for a contingent payment.  So that should make you very interested in transparency.  Or maybe not.  Maybe you close your eyes when you drive around sharp curves also.

Lessons for Insurers (1)

Lessons for Insurers (2)

Lessons for Insurers (3)

Lessons for Insurers (4)

Lessons for Insurers (5)

Lessons for Insurers (6)

Why the valuation of RMBS holdings needed changing

Post from Michael A Cohen, Principal – Cohen Strategic Consulting

Last November’s decision by the National Association of Insurance Commissioners (NAIC) to appoint PIMCO Advisory to assess the holdings of non-agency residential mortgage-backed securities (RMBS) signaled a marked change in attitude towards the major ratings agencies. This move by the NAIC — the regulatory body for the insurance industry in the US, comprising the insurance commissioners of the 50 states – was aimed at determining the appropriate amount of risk-adjusted capital to be held by US insurers (more than 1,600 companies in both the life and property/casualty segments) for RMBS on their balance sheets.

Why did the NAIC act?

A number of problems had arisen from the way RMBS held by insurers had historically been rated by some rating agencies which are “nationally recognized statistical rating organizations” (NRSROs), though it is important to note that not all rating agencies which are NRSROs had engaged in this particular rating activity.

RMBS had been assigned (much) higher ratings than they seem to have deserved at the time, albeit with the benefit of hindsight. The higher ratings also led to lower capital charges for entities holding these securitizations (insurers, in this example) in determining the risk-adjusted capital they needed to hold for regulatory standards.

Consequently, these insurance organizations were ultimately viewed to be undercapitalized for their collective investment risks. These higher ratings also led to lower prices for the securitizations, which meant that the purchasers were ultimately getting much lower risk-adjusted returns than had been envisaged (and in many cases losses) for their purchases.

The analysis that was performed by the NRSROs has been strenuously called into question by many industry observers during the financial crisis of the past two years, for two primary reasons:

• The level of analytical due diligence was weak and the default statistics used to evaluate these securities did not reflect the actual level of stress in the marketplace; as a consequence ratings were issued at higher levels than the underlying analytics in part to placate the purchasers of the ratings, and a number of industry insiders observed that this was done.
• Once the RMBS marketplace came under extreme stress, the rating agencies subsequently determined that the risk charges for these securities would increase several fold, materially increasing the amount of risk-adjusted capital needed to be held by insurers with RMBS, and ultimately jeopardizing the companies’ financial strength ratings themselves.

Flaws in rating RMBS

Rating agencies have historically been paid for their rating services by those entities to which they assign ratings (that reflect claims paying, debt paying, principal paying, etc. abilities). Industry observers have long viewed this relationship as a potential conflict of interest, but, because insurers and buyers had not been materially harmed by this process until recently, the industry practice of rating agencies assigning ratings to companies who were paying them for the service was not strenuously challenged.

Further, since the rating agencies can increase their profit margins by increasing their overall rating fees while maintaining their expenses in the course of performing rating analysis, it follows that there is an incentive to increase the volume of ratings issued by the staff, which implies less time being spent on a particular analysis. Again, until recently, the rated entities and the purchasers of rated securities and insurance policies did not feel sufficiently harmed to challenge the process.

You may have missed these . . .

Riskviews was dormant from April to July 2009 and restarted as a forum for discussions of risk and risk management.  You may have missed some of these posts from shortly after the restart…

Crafting Risk Policy and Processes

From Jawwad Farid

Describes different styles of Risk Policy statements and warns against creating unnecessary bottlenecks with overly restrictive policies.

A Model Defense

From Chris Mandel

Suggests that risk models are just a tool of risk managers and therefore cannot be blamed.

No Thanks, I have enough “New”

Urges thinking of a risk limit for “new” risks.

The Days After – NEVER AGAIN

Tells how firms who have survived a near death experience approach their risk management.

Whose Loss is it?

Asks about who gets what shares of losses from bad loans and suggests that shares havedrifted over time and should be reconsidered.

How about a Risk Diet?

Discusses how an aggregate risk limit is better than silo risk limits.

ERM: Law of Unintended Consequences

From Neil Bodoff

Suggests that accounting changes will have unintended consequences.

Lessons from a Bull Market that Never Happened

Translates lessons learned from the 10 year bull market that was predicted 10 years ago from investors to risk managers.

Choosing the Wrong Part of the Office

From Neil Bodoff

Suggests that by seeking tobe risk managers, actuaries are choosing the wrong part of the office.

Random Numbers

Some comments on how random number generators might be adapted to better reflect the variability of reality.

The Future of Risk Management – Conference at NYU November 2009

Some good and not so good parts to this conference.  Hosted by Courant Institute of Mathematical Sciences, it was surprisingly non-quant.  In fact several of the speakers, obviously with no idea of what the other speakers were doing said that they were going to give some relief from the quant stuff.

Sad to say, the only suggestion that anyone had to do anything “different” was to do more stress testing.  Not exactly, or even slightly, a new idea.  So if this is the future of risk management, no one should expect any significant future contributions from the field.

There was much good discussion, but almost all of it was about the past of risk management, primarily the very recent past.

Here are some comments from the presenters:

• Banks need regulator to require Stress tests so that they will be taken seriously.
• Most banks did stress tests that were far from extreme risk scenarios, extreme risk scenarios would not have been given any credibility by bank management.
• VAR calculations for illiquid securities are meaningless
• Very large positions can be illiquid because of their size, even though the underlying security is traded in a liquid market.
• Counterparty risk should be stress tested
• Securities that are too illiquid to be exchange traded should have higher capital charges
• Internal risk disclosure by traders should be a key to bonus treatment.  Losses that were disclosed and that are within tolerances should be treated one way and losses from risks that were not disclosed and/or that fall outside of tolerances should be treated much more harshly for bonus calculation purposes.
• Banks did not accurately respond to the Spring 2009 stress tests
• Banks did not accurately self assess their own risk management practices for the SSG report.  Usually gave themselves full credit for things that they had just started or were doing in a formalistic, non-committed manner.
• Most banks are unable or unwilling to state a risk appetite and ADHERE to it.
• Not all risks taken are disclosed to boards.
• For the most part, losses of banks were < Economic Capital
• Banks made no plans for what they would do to recapitalize after a large loss.  Assumed that fresh capital would be readily available if they thought of it at all.  Did not consider that in an extreme situation that results in the losses of magnitude similar to Economic Capital, that capital might not be available at all.
• Prior to Basel reliance on VAR for capital requirements, banks had a multitude of methods and often used more than one to assess risks.  With the advent of Basel specifications of methodology, most banks stopped doing anything other than the required calculation.
• Stress tests were usually at 1 or at most 2 standard deviation scenarios.
• Risk appetites need to be adjusted as markets change and need to reflect the input of various stakeholders.
• Risk management is seen as not needed in good times and gets some of the first budget cuts in tough times.
• After doing Stress tests need to establish a matrix of actions that are things that will be DONE if this stress happens, things to sell, changes in capital, changes in business activities, etc.
• Market consists of three types of risk takers, Innovators, Me Too Followers and Risk Avoiders.  Innovators find good businesses through real trial and error and make good gains from new businesses, Me Too follow innovators, getting less of gains because of slower, gradual adoption of innovations, and risk avoiders are usually into these businesses too late.  All experience losses eventually.  Innovators losses are a small fraction of gains, Me Too losses are a sizable fraction and Risk Avoiders often lose money.  Innovators have all left the banks.  Banks are just the Me Too and Avoiders.
• T-Shirt – In my models, the markets work
• Most of the reform suggestions will have the effect of eliminating alternatives, concentrating risk and risk oversight.  Would be much safer to diversify and allow multiple options.  Two exchanges are better than one, getting rid of all the largest banks will lead to lack of diversity of size.
• Problem with compensation is that (a) pays for trades that have not closed as if they had closed and (b) pay for luck without adjustment for possibility of failure (risk).
• Counter-cyclical capital rules will mean that banks will have much more capital going into the next crisis, so will be able to afford to lose much more.  Why is that good?
• Systemic risk is when market reaches equilibrium at below full production capacity.  (Isn’t that a Depression – Funny how the words change)
• Need to pay attention to who has cash when the crisis happens.  They are the potential white knights.
• Correlations are caused by cross holdings of market participants – Hunts held cattle and silver in 1908’s causing correlations in those otherwise unrelated markets.  Such correlations are totally unpredictable in advance.
• National Institute of Financa proposal for a new body to capture and analyze ALL financial market data to identify interconnectedness and future systemic risks.
• If there is better information about systemic risk, then firms will manage their own systemic risk (Wanna Bet?)
• Proposal to tax firms based on their contribution to gross systemic risk.
• Stress testing should focus on changes to correlations
• Treatment of the GSE Preferred stock holders was the actual start of the panic.  Leahman a week later was actually the second shoe to drop.
• Banks need to include variability of Vol in their VAR models.  Models that allowed Vol to vary were faster to pick up on problems of the financial markets.  (So the stampede starts a few weeks earlier.)
• Models turn on, Brains turn off.

RISK USA Conference – October 2009

Many, many good questions and good ideas at the RISK USA conference in New York.  Here is a brief sampling:

• Risk managers are spending more time showing different constituencies that they really are managing risk.
• May want to change the name to “Enterprise Uncertainty Management”
• Two risk managers explained how their firms did withdraw from the mortgage market prior to the crisis and what sort of thinking by their top management supported that strategy
• Now is the moment for risk management – we are being asked for our opinion on a wide range of things – we need to have good answers
• Availability of risk management talent is an issue.  At both the operational level and the board level. 
• Risk managers need to move to doing more explaining after better automating the calculating
• Group think is one of the major barriers of good risk management
• Regulators tend to want to save too many firms.  Need to have a middle path that allows a different sort of resolution of a troubled firm than bankrupcy.
• Collateral will not be a sufficient solution to risks of derivatives.  Collateral covers only 30 – 50% of risk
• No one has ever come up with a theory for the level of capital for financial firms.  Basel II is based upon the idea of keeping capital at about the same level as Basel I. 
• Disclosure of Stress tests of major banks last Spring was a new level of transparency. 
• Banking is risky. 
• Systemic Risk Regulation is impossibly complicated and doomed to failure. 
• Systemic Risk Regulation can be done.  (Two different speakers)
• In Q2 2007, the Fed said that the sub-prime crisis is contained.  (let’s put them in charge)
• Having a very good system for communicating was key to surviving the crisis.  Risk committees met 3 times per day 7 days per week in fall 2008. 
• Should have worked out in advance what do do after environmental changes shifted exposures over limits
• One firm used ratings plus 8 additional metrics to model their credit risk
• Need to look through holdings in financial firms to their underlying risk exposures – one firm got red of all direct exposure to sub prime but retained a large exposure to banks with large sub prime exposure
• Active management of counterparties and information flow to decision makers of the interactions with counter parties provided early warning to problems
• Several speakers said that largest risk right now is regulatory changes
• One speaker said that the largest Black Swan was another major terrorist attack
• Next major systemic risk problem will be driven primarily by regulators/exchanges
• Some of structured markets will never come back (CDO squareds)
• Regret is needed to learn from mistakes
• No one from major firms actually went physically to the hottest real estate markets to get an on the ground sense of what was happening there – it would have made a big difference – Instead of relying solely on models. 

Discussions of these and other ideas from the conference will appear here in the near future.

Whose Loss is it?

As we look at the financial system and contemplate what makes sense going forward, it should be important to think through what we plan to do with losses going forward.

There are at least seven possibilities.  As a matter of public policy, we should be discussing where the attachment should be for each layer of losses.  Basel 2 tries to set the attachment for the fourth layer from the bottom, without directly addressing the three layers below.

So for major loss scenarios, we should have a broad idea of how we expect the losses to be distributed.  Recent practices have focused on just a few of these layers, especially the counterparty layer.  The “skin in the game” idea suggests that the counterparties, when they are intermediaries, should have some portion of the losses. Other counterparties are the folks who are taking the risks via securitizations and hedging transactions.

However, we do not seem to be discussing a public policy about the degree to which the first layer, the borrowers, needs to absorb some of the losses.  In all cases, absorbing some of the losses means that that layer really needs to have the capacity to absorb those losses.  Assigning losses to a layer with no resources is not an useful game.  Having resources means having valuable collateral or dependable income that can be used to absorb the loss.  It could also mean having access to credit to pay the loss, though hopefully we have learned that access to credit today is not the same as access to credit when the loss comes due.

+    +    +   +

This picture might be a useful one for risk managers to use as well to clarify things about how losses will be borne that are being taken on by their firm.  The bottom layer does not have to be a borrower, it can also be an insured.

This might be a good way to talk about losses with a board.  Let them know for different frequency/severity pairs who pays what.  This discussion could be a good part of a discussion on Risk Appetite and Risk Limits as well as a discussion of the significance of each different layer to the risk management program of the firm.

The “skin in the game” applies at the corporate level as well.  If you are the reinsurer or another counterparty, you might want to look at this diagram for each of your customers to make sure that they each have enough “skin” where it counts.

Unrisk – Part 3

From Jawwad Farid

Transition Matrix

Here is another way of looking at it. It is called a transition matrix. All it does is track how something rated/scored in a given class moves across classes over time

How do you link to profitability?

This is how profitability is calculated generally. Take the amount you have lent, multiply it by your expected adjusted return and voila, you have expected earnings. But that is not the true picture.

What you are missing is the impact of two more elements. Your cost of funds (the money you have lent is actually not yours. You have borrowed it at a cost and that cost needs to be repaid) and your best and worst case provisions. So true profitability would look something like this.

That is a pretty picture if I ever saw one. Especially when you compare the swing from the original projected number. Back to the question clients ask. Where do projected provisions come from? From transition matrices. And where do transition matrices come from. From applying your understanding of your distribution to your portfolio.

Remember these are not my ideas. They are hardly even original. The Goldman trader who first asked me about moment generating functions wanted to understand how well I understood the distributions that were going to rule my life on Fleet street?

Full credit for posing the distribution problem goes to our friend NNT (Nicholas Nassim Taleb) who first posed this as getting comfortable with the generating function problem. He wrote all of three books on the subject and then some. Rumor has it that he also made an obscene amount of money in the process (not with book writing, but with understanding the distribution). All he suggested was that before you took a punt, try and understand how much trouble could you possibly land in based on how what you are punting on is likely to behave in the future. Don’t just look at the past and the present, look the range, likely, unlikely, expected, unexpected.

UNRISK Part 1

UNRISK Part 2

UNRISK (2)

From Jawwad Farid

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.