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Author: Donald van Deventer Created: 3/10/2009 8:52 AM
Born and brought up in California, Don holds a Ph.D. in Business Economics, a joint degree of the Harvard University Department of Economics and the Harvard Graduate School of Business Administration. Don currently services on the Board of Directors of the Harvard Alumni Association and on the Alumni Council of the Graduate School of Arts and Sciences. He also holds a degree in mathematics and economics from Occidental College, where he graduated summa cum laude and Phi Beta Kappa. 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

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, just as twice differentiable yield curve segments produce a shorter length yield curve over the full length of the curve even though the shortest length between any two points is a linear function.  Finally, we compare 23 different techniques for smoothing yields and forward rates that have been discussed in this series and show why the maximum smoothness forward rate approach is the best technique by multiple criteria.

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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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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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