Search My Blog
 About Donald

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

 Connect
 Contact Us

Kamakura Corporation
2222 Kalakaua Avenue

Suite 1400
Honolulu HI 96815

Phone: 808.791.9888
Fax: 808.791.9898
info@kamakuraco.com

Americas, Canada
James McKeon
Director of USA Business Solutions
Phone: 215.932.0312

Andrew Zippan
Director, North America (Canada)
Phone: 647.405.0895

Asia, Pacific
Clement Ooi
Managing Director, ASPAC
Phone: +65.6818.6336

Austrailia, New Zealand
Andrew Cowton
Managing Director
Phone: +61.3.9563.6082

Europe, Middle East, Africa
Jim Moloney
Managing Director, EMEA
Phone: +49.17.33.430.184

Visit Us

Linked In Twitter Seeking Alpha
Careers at Kamakura
Technical Business Consultant – ASPAC
Asia Pacific Region

Business Consultant – ASPAC
Asia Pacific Region

Consultant
Europe

Kamakura Risk Manager Data Expert
Europe, North America, Asia & Australia

Client Relationship Managers

North America

 Archive
kamakura blog
   
  

Government yield curves are a critical input to the risk management calculations of central banks, bank regulators, major banks, insurance firms, fund managers, pension funds, and endowments around the world.  With the internationalization of fixed income investing, it is important to understand the dynamics of movements in yield curves world-wide, in addition to the major bond markets like those in Frankfurt, London, New York and Tokyo. In this paper, we fit a multi-factor Heath, Jarrow and Morton model to daily data from the U.S. Treasury market over the period from January 2, 1962 to March 31, 2017. The modeling process reveals a number of important implications for term structure modeling in other government bond markets.

Read More »

This paper analyzes the number and the nature of factors driving the movements in the Japanese Government Bond yield curve from September 24, 1974 through December 30, 2016. The process of model implementation reveals a number of important insights for interest rate modeling generally. First, model validation of observed yields is important because those yields are the product of a third-party curve fitting process that may produce spurious indications of interest rate volatility.

Read More »

In this note, we interview an enormously successful individual investor who earned 10.01% on his fixed income portfolio over the last year, 9.57% better than the 0.44% total return on the bond market aggregate AGG exchange traded fund (AGG).  The investor prefers to remain anonymous, but he agreed to this brief interview in order to help other individual investors achieve similar success.  We use the pseudonym “Mr. X.”

Read More »

This paper analyzes the number and the nature of factors driving the movements in the Thai Government Bond yield curve from September 15, 1999 through December 31, 2016. The process of model implementation reveals a number of important insights for interest rate modeling generally. First, model validation of observed yields is important because those yields are the product of a third-party curve fitting process that may produce spurious measures of interest rate volatility. Second, quantitative measures of smoothness and international comparisons of smoothness provide a basis for measuring data quality. Third, we outline a process for re-smoothing the raw data in a manner that preserves the maximum amount of true signal within that data.  Finally, we illustrate the process for comparing stochastic volatility and affine models of the term structure.

Read More »

In an article in August of 2014, we focused on one of the most persistently used formulas in fixed income markets:

Credit Spread = (1 – Recovery Rate)(Default Probability)

One is barraged on a daily basis with press and internet commentary using default probabilities “implied” from credit spreads. This simple formula asserts that the credit spread on a credit default swap or bond is simply the product of the issuer’s or reference name’s default probability times one minus the recovery rate on the transaction.

Read More »