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Don founded Kamakura Corporation in April 1990 and currently serves as its chairman and chief executive officer where he focuses on enterprise wide risk management and modern credit risk technology. His primary financial consulting and research interests involve the practical application of leading edge financial theory to solve critical financial risk management problems. Don was elected to the 50 member RISK Magazine Hall of Fame in 2002 for his work at Kamakura. Read More

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Kamakura Blog

Nov 10

Written by: Donald van Deventer
11/10/2009 11:54 PM 

Robert A. Jarrow1  and Donald R. van Deventer2

In September 2009, the Society of Actuaries released a paper entitled “The Financial Crisis and Lessons for Insurers” by Robert W. Klein, Gang Ma, Eric R. Ulm, Shaun Wang, Xiangjing Wei, and George Zanjani.  All readers of this paper should read the full paper because it’s an excellent summary of the issues involved. The full text of this paper is available via this link to the Society of Actuaries website:

In just one respect, however, using an American football analogy, the paper leaves the ball just short of the end zone and fails to score a touchdown. This paper extends the insights of their paper and tries to obtain the additional 7 points.

The paper starts off well by identifying “home prices” as the number one cause of what’s been called the “subprime, credit, or financial crisis.”  Although indeed a crisis, because the credit problems were “derivatives” from another fundamental cause, perhaps a better name is the “home price crisis” as the authors note on page 4:

The authors correctly cite New York University’s Nouriel Roubini and Robert Shiller of Yale University as two who warned of the home price bubble.  But, they were not the only market observers who were aware that inflated home prices were a fundamental risk factor in the economy. For example, see the December 10, 2003 “Loss Distribution Model” by one of the authors of this paper (Robert Jarrow) and additional co-authors at the Federal Deposit Insurance Corporation. The appendix of this FDIC paper identifies three macro economic factors that drive correlated U.S. bank defaults: home prices, interest rates and bank stock prices. Note the inclusion of home prices as a key macro factor. For an introduction to the FDIC Loss Distribution Model, see this link to the FDIC website:

As early as 2003, simulations in this FDIC paper showed what we now know with 20-20 hindsight: the FDIC was heavily underfunded with respect to its deposit insurance obligations.

The financial press also contained plenty of warning signs. For example, an “early warning” indicator of the home price crisis was contained in the Economist, which published this graph on June 16, 2005 showing that the home price bubble in the U.S., the United Kingdom, and Australia had far outstripped the bubble in Japanese home prices, which had already burst. The bursting of the bubble in Japan ultimately saw Japanese home prices drop 60% in major cities:

The full text of this article is available by subscription on this web page:

Returning to page 5 of the Society of Actuaries paper, the authors correctly note that traditional fixed income analytics – duration and its extensions – failed during the crisis:

The authors elaborate on the shortcomings of the traditional duration measures of risk on page 45:

This is another area where the authors fell short of the “end zone.” What they should have said was something like this:

“The current home crisis makes it quite clear that security values are affected by changes in various macro economic factors.  When these macro factors change, they change the value of a security by causing changes in the default probability, the credit spread, the prepayment probabilities, and the bid-offered spread.  ‘Duration’ and related measures of price changes (‘price elasticity’ to be more precise) stem from changes in only one obvious macro factor: interest rates.  What the current home crisis reveals is that duration measures are not enough.  The existing fixed income analytics have to be supplemented with additional macro measures of price elasticity like:

  • the percentage change in the security’s price in response to an x% change in home prices,
  • the percentage change in the security’s price in response to an x% change in commercial real estate prices,
  • the percentage change in the security’s price in response to an x% change in oil prices,
  • the percentage change in the security’s price in response to an x% change in foreign exchange rates,
  • the percentage change in the security’s price in response to an x% change in the stock price index,

and so on.”

The single minded focus of most risk managers on interest rate risk is one of the prime reasons why the market values of so many large institutions were devastated when home prices dropped. Risk managers had been operating without knowledge of their exposure to home prices and the other macro factors.

Note that when we say “security” above, we are also including every transaction on the balance sheet of insurance companies and other financial institutions. And, as we’ve noted in other publications, these macro factors affect mortality rates and the supply of various other liabilities within these institutions.

We also note in passing that stress testing the default probabilities, credit spreads, and prepayment rates themselves is not a substitute for stress testing the macro factors directly. If we break the link between changes in the macro factor and the valuation of the security, we obscure the risk.

The Society of Actuaries paper continues on page 7 by noting that there is a need for improved early warning systems for the potential failure of regulated insurance firms:

When the authors return to this topic on page 72, they note that the current techniques used by insurance regulators are backward looking and dependent on financial ratios to the exclusion of almost all else:

We agree with these statements, and we have another solution. Recently the Office of the Comptroller of the Currency announced that it has subscribed to daily updates of a default probability service – an early warning system for bank failures – that is driven not only by financial ratios but also by macro economic factors.  For details, contact us at

A similar tool would enable the National Association of Insurance Commissioners and other insurance regulators to watch the evolution of default probabilities across time for stock insurance and mutual insurance companies. These default probabilities would provide an early warning system for potential failures of regulated insurance companies. For example, the graph below shows the evolution of 1 year default probabilities (blue) and 5 year default probabilities (yellow) for MetLife:

The increase in MetLife’s default probabilities during the crisis shows a period where MetLife was stressed. The regulators, seeing this, could respond in an appropriate fashion. Fortunately, as is well known, MetLife successfully navigated the credit crisis. This is in stark contrast to the performance of AIG, Bear Stearns, Lehman Brothers, and Washington Mutual. Perhaps the early warning system that we propose would have enabled the regulators to act sooner and avoid these failures, or at a minimum, to be better prepared for such a contingency.

The authors note that the role of enterprise risk management has been growing in the insurance industry on page 55:

The Society of Actuaries authors turn next to the key role of enterprise risk management in the prevention of future crises in summarizing their conclusions on page 8:
We strongly agree with the importance of ERM and the informed use of models. The integration of market, credit, liquidity and operational risk is essential to the successful operation of any financial institution. We have been stressing this point for over a decade. In addition, models should not be a “black box.” Models should be validated and transparent. Without this transparency, users cannot know both the benefits and limitations of the models. We at Kamakura have always stressed a multiple models approach to the quantification of risk.

On page 14 and again later on page 61 the authors come tantalizingly close to insisting on stress testing with respect to a long list of macro factors, rather than interest rates alone, but they fall short.  They instead mention the symptoms of macro factor movements (ratings downgrades, failure of liquidity suppliers, increase in correlations in asset returns):

They come close again to mentioning the magic words “home prices” or “macro factors” on page 70, where they discuss “dynamic financial analysis,” in insurance industry lingo, known as stochastic multiperiod simulation to the rest of the world.  They contrast this approach to “RBC,” risk based capital in the insurance industry:

What they should have said is this:

“The best approach to preventing future crises driven by macro factors – interest rates, home prices, commercial real estate prices, stock prices, oil prices and foreign exchange rates – is stochastic, dynamic financial analysis and stress testing of those factors.  This Monte Carlo simulation and related stress tests would link changes in macro factors directly to default probabilities, credit spreads, prepayment rates, bid-offered spreads and liability considerations like mortality rates and the probability of other insurance events.”

It was only on page 77 on macro factor stress tests that the authors mention “stress tests” and “a major contracting in the housing market” in adjacent sentences. Since home prices were correctly identified as the root cause of the crisis, stress testing and simulation of these macro factors and their impacts on the firm should have been explicitly mentioned in the paper, but it was not.

Finally, the authors on page 76 make the point that reliance on ratings has greatly magnified the repercussions of the drop in home prices:

The failure of credit ratings was absolutely true. But, another implication of the reliance on ratings is even more important: since the mechanism that connects macro factors to changes in ratings is unknown, ratings do not reflect these macro risks. They remain hidden to the risk manager. That’s why ratings have to go. See our May 12, 2009 blog “A Ratings Neutral Investment Policy”

“The Financial Crisis and Lessons for Insurers” provides a useful summary for the Society of Actuaries.  Our only criticism is that they really needed to say more clearly, loudly and simply that

“Risk analysis should have macro factors like home prices as the basis for enterprise wide risk management calculations.  To do otherwise is to sow the seeds of the next crisis.”

If they had said only this, it would have been a touchdown!

1Ronald and Susan Lynch Professor of Investment Management, Johnson School of Management, Cornell University and Managing Director Research, Kamakura Corporation
2Chairman and Chief Executive Officer, Kamakura Corporation

Robert A. Jarrow  and Donald R. van Deventer
Kamakura Corporation
Honolulu, November 11, 2009