Lessons for Insurers (4)

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…

4. Insurers should establish a robust liquidity management system to ensure that they have ample liquidity under stress scenarios.

The only trouble with this advice it that it is totally unneeded.  That is because almost all cases of insurer problems with liquidity, those problems were preceded by a loss that significantly exceeded management expectations for a worst loss.

So it would not have made a difference whether those insurers planned more for liquidity, those plans would have been inadequate.

Insurers are generally cash flow positive.  Liquidity is only ever a problem if that changes drastically.  Even the “runs on the bank” that have occured on insurers have followed large losses.

So this advice sounds nice, but is actually unnecessary.  If insurers properly anticipate extreme losses, then they will be prepared to pay those losses without triggering problems.

That is because they will:

1. Price for the losses so that they have sufficient income to pay the losses.

2. Only accept as much of the risks that might trigger extreme losses as they can afford and spread effectively.

Those are fundamental risk management tasks.  If they are done properly, liquidity management is relatively trivial.  It consists of remembering not to invest the funds you have on hand to pay those extreme claims in instruments that are illiquid or or widely fluctuating value.

Seems like a good rule in general.  One that many insurers forget after many years of positive cashflows.

Lessons for Insurers (1)

Lessons for Insurers (2)

Lessons for Insurers (3)

Lessons for Insurers (4)

Lessons for Insurers (5)

Lessons for Insurers (6)

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Basis Risk

In the simplest terms, basis risk is the difference between the hedge you bought and the risk that you own.  Especially the difference that is most noticeable when you had expected to be needing the hedge.

But that is not the topic here.  There is another Basis Risk.  That is the risk that you are using the wrong basis to judge your gains and losses.  This risk is particularly prevalent right after a bubble pops.  Everyone is comparing their wealth to what they thought that they had at the height of the bubble.

Here are two stories that show the problem with that:

1. Think about a situation where someone made an error in preparing your brokerage statement.  That IBM stock you have was worth $100,000.  The mistake was that an extra zero slipped in somehow.  The position was recorded as $1,000,000.  Add that in to your net worth and most of us would have an exaggerated feeling of wealth.  Think of how destructive to your long term happiness it would be if you really came to believe that you had that extra $900,000?  Two ways that could be destructive.  First of all, you could start spending other funds as if you had all that excess value.  Second of all, once you were informed of the error, you could then undertake more aggressive investments to try to make up the difference.  The only way to be safe from that destruction is if you never believe the erroneous basis for the IBM stock.
2. Possibly more realistic, think of someone in a casino.  During their day there they bet on some game or other continuously.  At one point in the visit, they are up by $100,000.  When they leave, they are actually down by $1000 compared to the amount they walked in the door with.  Should they tell people you lost $1000 or $101,000?

In both cases, it sounds silly to even think for long about the larger numbers as your “basis”.  But that seems to pervade the financial press.  Unfortunately, with regard to home values, many folks were persuaded to believe the erroneous valuation at the peak of the market and to borrow based upon that value.

So, now you know what is meant by this type of “Basis Risk”.  Unfortunately, it is potentially much larger than the first type of basis risk.  Behavioral Finance abounds with examples of how the wealth effect can distort the actions of people.  Possibly, the reason that the person in the casino walked out with a $1000 loss is realted to the sorts of destructive decisions that are made when wealth is suddenly increased.  Therefore, it is much more important to protect against this larger basis risk.

To protect against this type of risk requires a particularly strong ability to stick to your own “disciplined realism” valuation of your positions.  Plus an ability to limit your own valuation to include only reasonable appreciation.  Mark to mark is the opposite of the disciplined realism, at least when it comes to upside MTM.  For downside movements in value, it is best to make sure that your disciplined realism is at least as pessimistic as the market. 

This is a very different approach than what has been favored by the accounting profession and adoppted by most financial firms.  But they have found themselves in the position of the second story above.  They feel that they have made gigantic profits based upon the degree to which their bets are up in the middle of the session.  They have not left the casino yet, however.

And that is the last place where disciplined realism needs to be applied.  Most of us have been schooled to believe that “realized gains” are REAL and therefore can of course be recognized.  But think of that second story about the casino.  If you are taking those gains and putting them right back on the table, then you really do not “have” them in any REAL sense.  Firms need to adopt disciplined realism by recognizing that a series of similar positions are in reality not at all different from a single position held for a long time.  The gains should not be recognized until the size of the position is significantly reduced.

Models & Manifesto

Have you ever heard anyone say that their car got lost? Or that they got into a massive pile-up because it was a 1-in-200-year event that someone drove on the wrong side of a highway? Probably not.

But statements similar to these have been made many times since mid-2007 by CEOs and risk managers whose firms have lost great sums of money in the financial crisis. And instead of blaming their cars, they blame their risk models. In the 8 February 2009 Financial Times, Goldman Sachs’ CEO Lloyd Blankfein said “many risk models incorrectly assumed that positions could be fully hedged . . . risk models failed to capture the risk inherent in off-balance sheet activities,” clearly placing the blame on the models.

But in reality, it was, for the most part, the modellers, not the models, that failed. A car goes where the driver steers it and a model evaluates the risks it is designed to evaluate and uses the data the model operator feeds into the model. In fact, isn’t it the leadership of these enterprises that are really responsible for not clearly assessing the limitations of these models prior to mass usage for billion-dollar decisions?

But humans, who to varying degrees all have a limit to their capacity to juggle multiple inter-connected streams of information, need models to assist with decision-making at all but the smallest and least complex firms.

These points are all captured in the Financial Modeler’s Manifesto from Paul Wilmott and Emanuel Derman.

But before you use any model you did not build yourself, I suggest that you ask the model builder if they have read the manifesto.

If you do build models, I suggest that you read it before and after each model building project.

Multi dimensional Risk Management

Many ERM programs are one dimensional. They look at VaR or they look at Economic Capital. The Multi-dimensional Risk manager consider volatility, ruin, and everything in between. They consider not only types of risk that are readily quantifiable, but also those that may be extremely difficult to measure. The following is a partial listing of the risks that a multidimensional risk manager might examine:
o Type A Risk – Short-term volatility of cash flows in one year
o Type B Risk – Short-term tail risk of cash flows in one year
o Type C Risk – Uncertainty risk (also known as parameter risk)
o Type D Risk – Inexperience risk relative to full multiple market cycles
o Type E Risk – Correlation to a top 10
o Type F Risk – Market value volatility in one year
o Type G Risk – Execution risk regarding difficulty of controlling operational losses
o Type H Risk – Long-term volatility of cash flows over five or more years
o Type J Risk – Long-term tail risk of cash flows over 5 five years or more
o Type K Risk – Pricing risk (cycle risk)
o Type L Risk – Market liquidity risk
o Type M Risk – Instability risk regarding the degree that the risk parameters are stable

Many of these types of risk can be measured using a comprehensive risk model, but several are not necessarily directly measurable. But the muilti dimensional risk manager realizes that you can get hurt by a risk even if you cannot measure it.

Learnings from the Financial Crisis (3)

Gone is not always gone – another of the underpinnings of the market risk business is the constant of trading of risks. However, in the case of sub prime, some of the counterparties in these trades were very intimately related to the banks that sold the risky securities. Sometimes, they were investment funds that were sold by to bank customers; sometimes the banks lent the money to the same party that bought the security. Sometimes, the bank kept the security and bought protection from a counterparty. In each of these types of situations, banks found that they ended up needing to take back some of all of the risks that they thought that they had laid off. Insurers can learn that they need to keep relationships clear. The banking model has long suffered from the idea that they were a relationship business and they would try to do as much business as possible with the customers who they have the best relationships with. Insurers need to be constantly aware of this trap that creates more and sometimes cloudy concentration risk. Both net and gross risks need to be tracked and attended to.

One simple reason for this part of the problem is the terminology that risk managers use. Usually hedging transactions are called Risk Transfers. But in fact, they are almost never a real transfer of risk. Usually they would more appropriately be called Risk Offsets. This sloppy terminology supports sloppy thinking. And the high speed of a trading business left no time for reflection, so the misrepresentation was left totally unchallenged.

Now good risk managers knew the truth and so were concerned with counterparty risk and basis risk as well as contract risk. But in the end, the largest risk turned out to be reputation risk. Banks were usually unable to take the hit to their reputation that walking away from their closely or even semi related counterparties. Especially when those counterparties were funded with customer money.