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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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In our blog on December 4, we argued that it’s obvious that the basis for compensation varies by the job: football coaches are paid for their skill, but large company CEOs are not.  Large company CEOs, we argued, are winners of a lottery that entitles them to huge payouts during their brief tenure.  In that piece, we compared the high correlation between skill and compensation among American collegiate football coaches and financial services CEOs, focusing on Coach June Jones of the Southern Methodist University Mustangs and Lloyd Blankfein, CEO of Goldman Sachs. Santa Claus came earlier this year, delivering updates on both men on December 23 and 24. We also add an update for the firing of Texas Tech's Mike Leach on 12/30.

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One of the most important lessons from the credit crisis is that so called “core deposits,” consumer savings and demand deposits, aren’t really “core” when you most need them, when the bank is in trouble.  This post gives some examples from the credit crisis and discusses implications for best practice liquidity risk  and interest rate risk management.

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This post is an appreciation of our Kamakura Risk Manager and Kamakura Risk Information Services clients, who have ranked us number one in the world again in a survey just out from a prestigious risk management publication.

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In this post we make an important change in our definition of the “best yield curve.” In this post we change the definition of “best” to be the yield curve with “maximum smoothness,” which we define mathematically. Given the other constraints we impose for realism, we find that this definition of “best” implies that yield curve should be formed from a series of cubic polynomials.  We then turn to Example F, in which we apply cubic splines to yields and derive the related forward rates.  We compare the result to Example E, the quadratic spline of forward rates, and we draw some interesting conclusions. We then lay out our plans for Part 9 in the series, a cubic spline of forward curves.

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In this post we take a logical step forward from Example D in part 6 of this series, where we used quadratic splines to fit yields. The result of that exercise was two insights.  First we saw much more variation in forward rates than yields, and we found that the “right hand” side constraint on the “best yield curve” can have a big impact on the nature of both yields and forwards. In this post, we turn to Example E, in which we apply quadratic splines to forward rates.  The result is a big improvement in realism, which is the ultimate criterion for “best yield curve.” We close by making our normal comparison to the misused Nelson-Siegel technique and lay out our plans for Part 8 in the series, “maximum smoothness yield curves.”

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