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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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Kamakura Blog

May 20

Written by: Donald van Deventer
5/20/2016 3:08 AM 

A Case Study of Yingli Solar Using KRIS Default Probabilities
May 19, 2016

The objective of this write-up is to showcase how the Kamakura Risk Information Services (“KRIS”) default probabilities subscription service assists users to manage exposure ingress and egress based on Kamakura Default Probabilities (“KDP”) movements.

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On May 12, 2016, an affiliate of Yingli Green Energy Holding Company Limited (“Yingli Solar”) missed interest payments on medium term notes, thereby constituting default. This highlights how KRIS and related risk measures would have allowed investors to exit from a position in Yingli Solar well before the default event of May 12.

The default probabilities produced by the KRIS service include both the best practice reduced form default probabilities and the older and less accurate Merton model default probabilities. In this note, we use the KRIS version 6.0 reduced form default probabilities, the newest and most accurate model in the KRIS model portfolio.

The chart below shows the history of the 1-year (in blue) and 10-year (in red) default probabilities for Yingli Solar:

In Kamakura Corporation’s popular troubled company index, a firm is considered “troubled” if its default probability exceeds one percent. The box on the left hand side of the graph above shows that the 1-year default probability of Yingli Solar first exceeded 1.00% in September 2011, 4 years and 8 months before Yingli Solar defaulted. For a fixed income investor, this is tremendous early warning, and highlights exit options well before a default event.

Another important display in KRIS is the cumulative default probability for the issuer. On December 11, 2015, the cumulative 10-year default probability for Yingli Solar was an extremely high 79.41%. This is a debilitating level, and it was a signal provided by KRIS a full five months before default:

A third tool in the KRIS portfolio of analytics is the “implied rating,” the firm’s hypothetical legacy rating. The KRIS implied rating is a statistical prediction of how the company would be rated by the major international rating agencies IF they were to rate the company. Yingli Solar had no official legacy credit rating, but its implied rating on the day of default was B-, a very weak junk rating.

More importantly, the company had always had a very weak implied credit rating. On September 13, 2007, 8 years and 8 months before default, the firm’s implied rating was only BB-. This is below the legacy definition of investment grade, so it would have served warning to investors to exercise caution in the purchase of this company bonds.

Another indicator that is very valuable as an early warning signal is a comparison to the industry peer group, which in the case of Yingli Solar is a high technology peer group. It can be seen clearly in the graph below that Yingli’s one-year default probability moved from about the median level of the peer group to well above it in late 2011 and early 2012. This is another warning, more than 5 years before default, to exit from any holdings of Yingli Solar.

Finally, a critical indicator that is provided by KRIS is the linking of macro factors to default probability functions, and an analysis of Yingli Green Energy Holding Company shows that their KDPs are directly linked to the US GDP 5 year return. Since the performance of this organization was directly related to US GDP, a drop in this macro factor indicator essentially meant that the KDPs of Yingli moved in line with a drop in the US 5 year GDP, as can be seen in the figures below. The first figure identified the various macro factors that drove the creditworthiness of Yingli Green, and the second figure identifies the relationship between US GDP 5 year return and Yingli KDPs.

The KRIS default probability models are benchmarked on more than two million observations of firms of all types. The total number of defaults used in the current version 6.0 models is more than 2,600. The unparalleled volume of data and the research insights of Kamakura’s Managing Director Prof. Robert Jarrow make the KRIS default probability service the most accurate early warning credit risk assessment indicator framework available. That is one of the reasons why Credit Magazine named both KRIS and the Kamakura Risk Manager Software package “Innovations of the Year”. For more information about KRIS default probabilities, please contact your Kamakura representative or e-mail Kamakura’s credit experts at

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