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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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One of the most frequently asked questions when people review predictive models of default is this: “Aren’t those explanatory variables correlated, and doesn’t this create problems with multicollinearity?” Since almost every default model has correlated explanatory variables, this is a question that comes up often. This post collects quotes on this issue from 12 popular econometrics texts and a comment by Prof. Robert Jarrow and a bank regulatory economist to answer this question.

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The Federal Reserve released the results of the Comprehensive Capital Analysis and Review stress tests on the afternoon of Thursday, March 5. In this note, we follow the examples of two very well-known papers. Andrew Haldane of the Bank of England, writing with Vasileios Madouros, asked whether the Basel capital ratios could outperform a benchmark statistical model in predicting default during the credit crisis in “ The Dog and the Frisbee.” The authors found that the Basel capital rules failed this model validation test.

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The Federal Reserve will announce the results of the “DFAST” stress tests on March 5. On March 13, 2014, we pointed out the many reasons why the Federal Reserve-mandated stress testing process will be a less accurate measure of financial institutions’ risk than the market’s price on those institutions’ promise to pay a dollar in the future. The market place considers all scenarios, not just three as in the Fed’s CCAR stress tests. The market place invests cold hard cash to price various financial institutions’ promises to pay.

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The author wishes to thank his colleague, Managing Director for Research Prof. Robert A. Jarrow, for twenty years of guidance and helpful conversations on this critical topic.

As zero interest rate policies and negative interest rates ripple through world financial markets, many legacy interest rate risk systems and asset and liability management systems have been unable to keep pace. In this note, we use 100,000 scenarios from a modern 9 factor Heath, Jarrow and Morton interest rate simulation from Kamakura Corporation to illustrate the model validation issues that arise when one admits that negative interest rates have a probability that is not zero.

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The author wishes to thank his colleague, Managing Director for Research Prof. Robert A. Jarrow, for twenty years of guidance and helpful conversations on this critical topic.

The Federal Reserve’s 2015 Comprehensive Capital Analysis and Review stress testing regime has included 3 points on the U.S. Treasury yield curve since 2014. The Bank for International Settlements has required that at least six interest rate risk factors be used to model market risk since 2010. At the same time, negative interest rates are visible across Europe, in Japan and in Hong Kong. Given the complexity of the interest rate risk environment, it is critical that the users of both internal and third-party asset and liability management and enterprise risk management systems perform an essential step in model validation.

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