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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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After riding up Mauna Kea volcano yesterday, Lance Armstrong is getting ready to travel from the Big Island of Hawaii to Adelaide for the Tour Down Under and the start of the 2010 cycling season.  Thanks to a blog that I stumbled on over the holidays, it seems like a good time to appreciate the risk management lessons from Lance over the years.  That’s the subject of today’s post.

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The author wishes to thank Keith Luna of Western Asset Management Company for helpful comments that led to this new version.

In Part 10 of this series, we present the final installment in yield curve and forward rate smoothing techniques before moving on to smoothing credit spreads.  We introduce the maximum smoothness forward rate technique introduced by Adams and van Deventer (1994) and corrected in van Deventer and Imai (1996), which we call Example H.  We explain why a quartic function is needed to maximize smoothness of the forward rate function over the full length of the forward rate curve. The conclusion is similar to the fact that a curve constructed from twice differentiable yield curve segments produces a shorter length yield curve over the full length of the curve than a “curve” with linear segments, even though the shortest length between any two points is a linear function.

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In Part 8 of this series, we observed that it has been proven that a cubic spline, in general, produces the smoothest set of curves that one can draw between data points.  This is certainly true in the case of smoothing the yield curve. As we show in part 10 of this blog series, this is NOT TRUE in the case of a cubic spline of forward rate curves if we apply the normal constraints one needs to fit observable financial data. In this post, we ignore that insight and flail ahead, erroneously assuming that if splines work reasonably well when applied to yields, they will work even better when applied to forward rates. In Example G, we apply cubic splines to forward rates and derive the related yields.  The results clearly illustrate why a true “maximum smoothness” approach is needed. We deliver that approach in Part 10, which explains the full maximum smoothness approach first proposed by Adams and van Deventer in 1994.

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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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