Bonds, Portfolio Strategy, Banks, risk management
Contributor Since 2009
Donald R. van Deventer founded the Kamakura Corporation in April, 1990 and is currently Chairman and Chief Executive Officer. Dr. van Deventer's emphasis at Kamakura Corporation is enterprise wide risk management and modern credit risk technology. The third edition of his book, Advanced Financial Risk Management (with Kenji Imai and Mark Mesler) is forthcoming in 2022. Dr. van Deventer was senior vice president in the investment banking department of Lehman Brothers (then Shearson Lehman Hutton) from 1987 to 1990. During that time, he was responsible for 27 major client relationships including Sony, Canon, Fujitsu, NTT, Tokyo Electric Power Co., and most of Japan's leading banks. From 1982 to 1987, Dr. van Deventer was the treasurer for First Interstate Bancorp in Los Angeles. In this capacity he was responsible for all bond financing requirements, the company’s commercial paper program, and a multi-billion dollar derivatives hedging program for the company. Dr. van Deventer was a Vice President in the risk management department of Security Pacific National Bank from 1977 to 1982. Dr. van Deventer 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. He was appointed to the Harvard University Graduate School Alumni Association Council in 1999 and served through 2021. Dr. van Deventer was Chairman of the Council for four years from 2012 to 2016. From 2005 through 2009, he served as one of two appointed directors of the Harvard Alumni Association representing the Graduate School of Arts and Sciences. Dr. van Deventer also holds a degree in mathematics and economics from Occidental College, where he graduated second in his class, summa cum laude, and Phi Beta Kappa. Dr. van Deventer speaks Japanese and English.
The Kamakura Risk Information Services version 5.0 Jarrow-Chava reduced form default probability model makes default predictions using a sophisticated combination of financial ratios, stock price history, and macro-economic factors. The version 5.0 model was estimated over the period from 1990 to 2008, and includes the insights of the worst part of the recent credit crisis. Kamakura default probabilities are based on 1.76 million observations and more than 2000 defaults. The term structure of default is constructed by using a related series of econometric relationships estimated on this data base. Free trials are available at Info@Kamakuraco.com. An overview of the full suite of Kamakura default probability models is available here.
General Background on Reduced Form Models
For a general introduction to reduced form credit models, Hilscher, Jarrow and van Deventer (2008) is a good place to begin. Hilscher and Wilson (2013) have shown that reduced form default probabilities are more accurate than legacy credit ratings by a substantial amount. Van Deventer (2012) explains the benefits and the process for replacing legacy credit ratings with reduced form default probabilities in the credit risk management process. The theoretical basis for reduced form credit models was established by Jarrow and Turnbull (1995) and extended by Jarrow (2001). Shumway (2001) was one of the first researchers to employ logistic regression to estimate reduced form default probabilities. Chava and Jarrow (2004) applied logistic regression to a monthly database of public firms. Campbell, Hilscher and Szilagyi (2008) demonstrated that the reduced form approach to default modeling was substantially more accurate than the Merton model of risky debt. Bharath and Shumway (2008), working completely independently, reached the same conclusions. A follow-on paper by Campbell, Hilscher and Szilagyi (2011) confirmed their earlier conclusions in a paper that was awarded the Markowitz Prize for best paper in the Journal of Investment Management by a judging panel that included Prof. Robert Merton.
Disclosure: I have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours.