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Volume 3. Issue Number 3
“There is a scarcity of data in some commercial lending asset classes in KSA and in many other countries, as historically defaults have been
rare,” explained Bernard O’Sullivan, Managing Director and Head of Standard & Poor’s Risk Solutions in Europe, Middle East & Africa. “This has
been a significant challenge to overcome for many banks. The data consortium initiative will address this by rapidly building a robust dataset
reflecting the underlying factors that drive credit risk. We believe the initiative taken by SIMAH is a very positive example to others and will
have attractive and far reaching benefits for the participating member banks and investors in the region.”
“SIMAH has been assigned by the banks to study the best possible options, revise them scientifically, submit them in the form of an integrated
project and develop technical models that can be applied to all Saudi banks so as to measure risks,” said Nabil Abdullah Al-Mubarak, General
Manager of SIMAH. “Based on that, SIMAH has conducted extensive studies to determine the requirements, know the banks' capabilities,
analyze all other experiences in this regard and finally determine the entity that could carry out this project as a technical partner. Accordingly,
SIMAH sent out all offers to the companies interested and we chose Standard & Poor's Risk Solutions.”
Under the initiative, the participating member banks will provide information on their mid-market and large corporate defaults, both
historically and on an ongoing basis. This information will follow criteria established by Standard & Poor’s Risk Solutions and will be held on
SIMAH’s secure confidential database in Riyadh. In addition, information on a significant sample of performing commercial loans will be
assembled and will enable Standard & Poor’s Risk Solutions to develop a probability of default model for Saudi corporate debt, as well as
provide industry-wide statistics on historic recovery and default trends in the Kingdom.
Reinforcing the conclusions of a white paper sent to clients in November, Wilshire Analytics’ global equity risk model, the Wilshire GR6
Equity Risk ModelSM, shows the year-end spreads between the model’s long-term and short-term valuations of risk are at record highs.
Global average of equity risk spreads between equally-weighted and exponentially-weighted risk
Estimates:
November 2008 December 2007 Previous Record Spread (July 2002)
10.54% 1.28% 2.57%
The paper, “Wilshire Equity Analytics: A Comparative Study of the Wilshire GR6 Covariance Matrices,” by Edward Rackham, PhD, vice
president, Wilshire Associates, shows the impact on portfolio risk of five different risk estimation schemes.
“Dr. Rackham’s research shows that in order to be useful within the investment process, a selected risk model needs to reflect the volatility of
the expected trading horizon,” commented David L. Hall, senior managing director, Wilshire Associates and head of Wilshire Equity Analytics, a
division of Wilshire Analytics. “A manager using a model with a time horizon inconsistent with the investment horizon means that the manager
may be managing to the wrong estimates of portfolio total risk and tracking error.”
Dr. Rackham, who oversees the development of new risk, performance and other analytics functionality in the Wilshire AtlasSM, the premier
solution offered by Wilshire Equity Analytics, commented that the methodology used to estimate covariance can lead to significantly different
estimations of portfolio risk.
“The paper also discusses how the Wilshire Equity GR6 Risk Model’s 2005 implementation of Wilshire’s proprietary Structured Hadamard
Product Target Shrinkage Estimator (SHaPTSE) improves the performance of the model’s short term risk estimates through ‘shrinkage’ of any
spurious ‘off-diagonal elements’ found in the model’s daily covariance matrices,” Dr. Rackham said. “As I noted in the paper, “…in comparison
to longer-term risk estimates, “it seems reasonable to conclude that the best one-month forward-looking predictions of risk … are yielded by
the Daily Exponential … matrices.”
Dr. Rackham cautioned that this does not mean that long term risk practitioners should stop using monthly models as they may prefer more
stable risk numbers rather than concern themselves with sensitivity to daily fluctuations. “Users of risk analytics need to be aware that in times
of extreme market volatility differences between the risk estimates of different models tend to be exaggerated,” he explained.
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