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An Introduction to Derivative Securities, Financial Markets, and Risk ManagementAdvanced Financial Risk Management, 2nd ed.

 Blog Entries

Kamakura Corporation Named to World Finance 100

August 26, 2014
Transfer Pricing and Valuation Yield Curves without Swap Data: A KeyBank and KeyCorp Example

August 18, 2014
More Evidence on the Funding “Subsidy” of the Too Big to Fail Banks

August 14, 2014
Mortgage Servicing Rights Values Close Mixed for the Week as Current and Forward Mortgage Rates Drop 0.03%

August 13, 2014
Liquidity At Risk – A stochastic look at cashflows

August 12, 2014
Five of Seven Regional Banks Trade at Credit Spreads Better than the Too Big to Fail Banks

August 12, 2014
Kinder Morgan Energy Partners Leads the 20 Best Value Bond Trades with Maturities of 10 Years or More

August 11, 2014
Measuring the Funding Costs of the Too Big to Fail Banks:
The U.S. Dollar Cost of Funds Index™


August 8, 2014
Forward 1 Month T-Bill Rates Plunge 0.26% in 2 Years but Forward 10 Year U.S. Treasury Yield Drops Only 0.04% from Last Week

August 6, 2014
Credit Spreads and Default Probabilities: A Simple Model Validation Example

August 5, 2014
Vodafone Group PLC: Default Risk is Down Sharply But Value Ranks in the Bottom 10% of Bonds

July 30, 2014
American International Group Inc. Bonds:
A Reward to Risk Ratio Twice as High as the Median Bond Issue


July 29,2014
AT&T Inc. Bonds: Ten Times the Risk of IBM and Below Average Value

July 15, 2014
Brazil, Italy, Spain, Credit Default Swaps and the
European Commission Short Sale Ban, 2010-2014


July 14, 2014
Bank of America and MBIA Lead U.S. Bank Credit Default Swap Trading Volume, 2010-2014

March 19, 2014
Stress Testing and Interest Rate Risk Models: A Multi-Factor Stress Testing Example

March 13, 2014
Stress Testing: A Credit Spread Ranking of 12 U.S. and 12 International Banks

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“You’re not going to believe this,” my friend’s note said.  “Check out this annual report and look how one of the largest banks in the world is reporting its interest rate risk.”  He was right. I took a look at this large and prestigious bank’s interest rate risk reporting and I was immediately transported back to the disco era, 1977.  This post turns the clock back 30 years and explains how using net income simulation, with no market valuation-based risk analysis, destroyed the savings and loan industry and cost the U.S. taxpayers $1 trillion the first time.  We also explain how a bank, which we call Bank X, traveled so far back in time.

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For many years, “operational risk” was an area of risk management of great interest but lacking in a theoretical and conceptual framework that would place operational risk in the context of an integrated approach to enterprise wide risk management.  Indeed, to many, “operational risk” was a table of losses for specific events and not much else.  This is the operational risk equivalent of credit risk modeling with loss given default statistics but no default probabilities.

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One of the few virtues of being an aged (or more politely, “experienced”) risk manager is that one has made a lot of analytical mistakes that are easy to remember. Another virtue is that you have seen others make mistakes that proved fatal or near fatal, so we can remember the consequences of those errors too. In risk management, it would be nice if we never repeated the mistakes of the past, but, alas, we take two steps forward and one step backward. In this series, we discuss risk management errors by some of the world’s largest financial institutions and point out their consequences to help all of us avoid errors of the past and obvious errors going forward. In this post, we point out the consequences of basing risk assessment on a pseudo monte carlo approach instead of the real thing.

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Traditional asset and liability management (ALM) has ignored mortgage defaults and focused on interest rate-driven and mortgage age-driven prepayment. The events of the last two years have made it more obvious that prepayment and default are intimately linked and that home prices are a critical driver of both probabilities. This post explains how mortgage prepayment and default are modeled on an integrated basis using multinomial logit.

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One of the most frequently asked questions posed by clients is this: How did Kamakura choose Honolulu for its head office location? This post explains.

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