SMFG Capital Ratio Information A. Measurement Using the Quantification Model (A) Estimation of Loss Severity Distribution a. Smoothed Bootstrap Method b. Supplementing Results of Risk Control Assessments with Extreme Value Theory SMFG  2011 206 In order to capture potential risks, a statistical method known as Extreme Value Theory is used in addition to the results of risk  control assessments. Extreme Value Theory is the statistical assessment method by which risks that may occur in the future  accompanying larger losses than the actually observed ones in the internal loss data can be quantified, and fulfills the role of  supplementing the risk control assessments.  In estimating the loss distribution under this method, the Kernell function (partially assumed function) is applied to the loss  data by the pile-up of functions. In particular, the log-normal distribution is applied as the Kernell function. Under the non-parametric method, if we use historical internal loss data to generate the loss severity distribution, we are not  able to create the samples outside the actual observation points, and also it is particularly difficult to create a distribution with  a fat tail. However, through the use of the method that can combine such data (on actual observations) with data on potential  risks, it becomes possible to create large losses that occur rarely (with a potential impact) and that have not actually been found  in historical internal loss data. In generating the distribution, while “high-frequency low-severity” events are based on sufficient  historical internal loss data volume, for “low-frequency high-severity” events in the tail of the distribution, the historical internal  data volume is insufficient. This approach makes it possible to reflect the severity (frequency of occurrence) of potential risk that  has been assessed in the risk control assessments. In this way, using this model, realized risks and potential risks can be combined  with congruity. We employ the “smoothed bootstrap” method for generating the loss distribution. The smoothed bootstrap method is one of the  methods that connect the distribution, of the realized risk and the potential risk event, smoothly. Under this method, no  assumptions are made about the shape of the distribution as a whole, but assumptions are made on the individual distribution  related to realized individual losses. Therefore, this method takes advantage of the widely known parametric method (method  assuming a distribution) as well as the non-parametric one (method not assuming a distribution). 5,000,000 10,000,000 8,000,000 15,000,000 7,000,000 Gaining a grasp of realized risk Capturing potential risks Collection of internal loss data Statistical estimates from internal loss data (Extreme Value Theory) Estimates from risk control assessments Body part of the “high-frequency low-severity” loss severity distribution Tail part of the “low-frequency high-severity” loss severity distribution Combination of the loss severity distributions Smoothed bootstrap method Loss occurrence for the last 7 years  (or period actually collected) Estimates of potential risk  that may emerge (Example) (Example) Period Amount of loss Frequency of occurrence  2003 / 1H 2004 / 1H 2005 / 1H 2005 / 1H 2005 / 2H ¥100 million or more ¥1 billion or more ¥5 billion or more ¥10 billion or more Once in 5 years Once in 10 years Once in 50 years Once in 100 years Amount of loss Smoothed bootstrap method Body part Amount of losses  Tail part Frequency of occurrence ¥100 million ¥1 billion ¥5 billion ¥10 billion