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CH&CO_SMA review_Op Risk comments and suggestions

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Following BCBS publication in March 2016, CH&Co welcomes the opportunity to provide comments on the consultative document covering the Standardised Measurement Approach for Operational Risk.
We have though suggested some areas for improvement, based on our research and simulations, which are presented in the exhibits.
In this regard, please find in these slides our analysis and suggestions, on what we consider as important points for discussion.

For further information on BCBS consultative document, please follow this link : http://www.bis.org/bcbs/publ/d355.htm.

For further information on the key impacts of the reviewed SMA, please follow this link and read our contribution : http://fr.slideshare.net/HeleneFreon/chcosma-reviewop-risk-survey-and-challenges.

CH&Co is a consulting firm which specializes in supporting clients within the financial services sector. We have developed a strong Risk Management practice, and through our missions and mandates, we have had the chance to build specific expertise in Operational Risk.

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CH&CO_SMA review_Op Risk comments and suggestions

  1. 1. June 2016 Standardised Measurement Approach for operational risk (BCBS Consultative Document, March 2016) Comments, proposals and open questions Benoît Genest – bgenest@chappuishalder.com Hélène Fréon – hfreon@chappuishalder.com
  2. 2. 2GRA – Op Risk | Survey | SMA– June 2016 © Chappuis Halder & Co.| 2016 | All rights reserved Table of contents 1 Introduction on the review of the SMA methodology based on theoretical profiles • Drivers of the SMA capital variations over time for a given loss profile • Risk sensitivity of the SMA methodology through 4 theoretical profiles 3 Detailed solutions to be discussed 4 Outstanding Issues 2 Analysis of the Consultative Document of March 2015
  3. 3. 3GRA – Op Risk | Survey | SMA– June 2016 © Chappuis Halder & Co.| 2016 | All rights reserved Evolution of the SMA Capital considering different BI scenarios Annual statistics of the sample • Ca. 6 000 loss events < 10 M€ per year • 10 M€ < 2 to 5 loss events < 100 M€ per year • Annual average loss amount = 23 000 € 16-year observation period • LC, 16-year considering the loss profile is similar • BI = 9 500 M€ (2006-2015) • Simulated BI considering the scenarios (2016-2021) Description of the theoretical initial loss profile • The loss profile is described in the adjacent illustration. It is smiliar across the 16-year period • Small loss events (<10 M€) have a higher frequency compared to severe events (>10 M€) BI scenarios (1) Constant BI: assumes the BI stagnates to 9 500 M€ over time (16 years). (2) Variable BI: considers the BI is constant before 2016 (9 500 M€) and either increases or decreases from 2016 on, assuming a constant annual variation (+/- 200 M€) in both cases between 2016 and 2021. Analysis of the impacts per scenario (1) Considering a constant BI, SMA capital stagnates over the 15-year simulated period. (2) Considering a variable BI, SMA capital is estimated between 1460 M€ and 1600 M€ depending on the BI scenario (increase/decrease) representing a 15% of the BI level. The BI evolution have a strong impact on the growth rate of the SMA capital over time, for this specific profile. Drivers of the SMA capital variations over time for a given loss profile (1/2) Impact of BI variations assuming various theoretical scenarios The SMA methodology is highly dependent on the ratio LC/BIC which defines the growth rate of the SMA capital value over time. Key learning Aggregatedlossamounts(inM€) 2016 2017 2018 2019 2020 2021 1460 M € 1480 M € SMAcapital(inM€) 2016 2017 2018 2019 2020 2021 +10% -7% 6-year simulated profiles (starting in 2016) Constant BI (In 2016, BI = 9 500 M€) Variable BI (annual var. +/- 200 M€) 1 2 Initial theoretical loss profile (identical on the period) 10 M 100 M0 M 1320 690 The loss profile is similar across the considered 16-year period
  4. 4. 4GRA – Op Risk | Survey | SMA– June 2016 © Chappuis Halder & Co.| 2016 | All rights reserved 2016 2017 2018 2019 2020 2021 2016 2017 2018 2019 2020 2021 SMAcapital(inM€)SMAcapital(inM€) Compared effects | Inclusion of a 100 M€ loss vs. inclusion of 10 10 M€ losses in 2016 loss data history 2016 loss data history | Inclusion of a 100 M€ loss (Shock in severity, average variations across scenarios in 2016 and 2021) 2016 loss data history | Inclusion of ten 10 M€ losses (Shock in frequency, average variations across scenarios in 2016 and 2021) 1 2 • Following the previous simulations scenarios, the adjacent illustration compares the effect of two stresses applied in the 2016 to the loss data history  One shock in severity (stress 1)  One shock in frequency (stress 2) • SMA Capital variations depend on: a shock in severity increases SMA Capital on 2016 by 15% and 12% in 2021 (average variations between and profiles across the 3 BI scenarios). Whereas, a shock in frequency keeps on the SMA capital at a similar level (-2% on average). • SMA Capital growth rate is significantly attenuated when BI decreases or stagnates over time. Drivers of the SMA capital variations over time for a given loss profile (2/2) Impact of loss distribution: stresses applied to loss frequency and severity (1) The SMA methodology increases the capital requirements when losses are extreme (> 100 M€). Indeed, for the same aggregated amount of loss added to the loss data history (100 M*1 or 10 M*10), the impact on the SMA capital depends on its distribution. (2) A severe loss is taken into account in the LC for 10 years and is weighted 19 times in the LC calculus. Thus its impact is amplified and supported over a long time. In the latter case (occurrence of an extreme loss), the only lever to reduce SMA Capital for 10 years is to have a decreasing BI during the same period. Key learnings Stress 1 profiles Initial profiles Stable BI 1460 M € 1650 M € - 1,5% - 2% Stress 2 profiles Initial profiles +12% - 12% +15%
  5. 5. 5GRA – Op Risk | Survey | SMA– June 2016 © Chappuis Halder & Co.| 2016 | All rights reserved 4 cases have been simulated below with different distribution profiles to illustrate the sensitivity of the reviewed SMA to the specificities of distribution profiles (using theoretical hypothesis with extreme losses to illustrate a profile that replicates the same distribution over time) Risk sensitivity of the SMA methodology through 4 theoretical profiles (1/2) Description of the simulated casesAgregatedamountsoflosses(inM€) 898 10 M 100 M0 M Loss distribution profile under a leptokurtic probability distribution: the most impacting losses are less frequent than the most severe ones Statistics of the sample used : • 2 000 losses < 10 M€ • Average = 0,449 M€ • Std. Deviation = 388 K€ 10 M 100 M0 M 655 Distortion of case 1: inclusion of Loss class 2 events (> 10 M€) with a concentration on Loss class 1 events. Statistics of the sample used : • 38 losses > 10 M€ • 1962 losses < 10 M€ • Average = 0,744 M€ • Std. Deviation = 2,350 M€ 832 896 10 M 100 M0 M 2 583 Case 1 including an extreme loss (2 583 M€) generating a fat tail Statistics of the sample used : • 1999 losses < 10 M€ • 1 loss > 100 M€ • Average = 1,740 M€ • Std. Deviation = 57,8 M€ CASE 1 | Loss distribution profile with no tail CASE 2 | Distortion of case 1 with no extreme losses CASE 3 | Presence of a « fat tail » -12% 832 10 M 100 M0 M 540 Mix cases 2 & 3 where the majority of loss events are observed within Loss Class 1 when looking at the 2 000 simulated loss events Statistics of the sample used : • 1962 losses< 10 M€ • 33 losses> 10 M€ • 5 losses> 100 M€ • Average = 1,260 M€ • Std. Deviation = 11,7 M€ 1 149 CASE 4 | Mix of cases 2 and 3 Agregatedamountsoflosses(inM€) Agregatedamountsoflosses(inM€) Agregatedamountsoflosses(inM€)
  6. 6. CHAPPUIS HALDER & CO. 66GRA – Op Risk | Survey | SMA– June 2016 © Chappuis Halder & Co.| 2016 | All rights reserved Risk sensitivity of the SMA methodology through 4 theoretical profiles (2/2) Evolution of the SMA capitalSMAcapital(M€) Bucket1 Bucket2 Bucket3 Bucket4 Bucket5 -79,9% -82,8% BI (M€) 0,2% 0,05%LC/BIC 30 00010 000100 6 625 12 100 10 Bucket1 Bucket2 Bucket3 Bucket4 Bucket5 -73,9% -80,2% BI (M€) 7,3% 2%LC/BIC 30 00010 000100 6 700 12 100 Bucket1 Bucket2 Bucket3 Bucket4 Bucket5 BI (M€) 30 00010 000100 12 400 12 100 743% 204% 106,5%LC/BIC +37,3% +59% +12,3% SMAcapital(M€) SMAcapital(M€) Bucket1 Bucket2 Bucket3 Bucket4 Bucket5 BI (M€) 30 00010 000100 7 340 12 100 74% 20% 1%LC/BIC -36,9% -60% 10 10 10 SMAcapital(M€) 100% 50 000 50 000 50 000 50 000 CASE 1 | LC = 3,14 M€ CASE 2 | LC = 126 M€ CASE 3 | LC = 12 930 M€ CASE 4 | LC = 1 272 M€
  7. 7. 7GRA – Op Risk | Survey | SMA– June 2016 © Chappuis Halder & Co.| 2016 | All rights reserved Table of contents 1 Introduction on the review of the SMA methodology based on theoretical profiles 3 Detailed solutions to be discussed 4 Outstanding Issues 2 Analysis of the Consultative Document of March 2015 • Starting assumption | Definition of a prevailing reference date • Limits and proposals to BCBS’ questions (Q2, Q3)
  8. 8. 8GRA – Op Risk | Survey | SMA– June 2016 © Chappuis Halder & Co.| 2016 | All rights reserved Two types of loss events are considered when calculating the Loss Component (LC) • Provisions on expected loss generating events • Observed losses generated by Operational Risk events The Basel Committee indicates each loss – whether observed or provisioned – shall be registered with occurrence, accounting and reporting dates.(cf. § 43 reference date bullet point 5). Consequence : bias in the Loss Component calculus When taking loss events into account in the LC calculus, the reference date depends upon the type of loss event : • For provisions : the Basel Committee mentions the accounting date (cf. § 45 reference date bullet point 1) • For losses on observed events : the bank is free to choose either the accounting or the reporting date. (cf. § 45 reference date bullet point 2) This open-ended choice in the reference date generates skews the LC computation, for the chosen reference date will necessarily vary across banks. Why should banks be allowed to defined their reference date when taking into account loss events (excluding provisions on expected losses) in the LC computation ? In the Consultative Document, the Basel Committee specifically signalled its willingness to promote transparency through a standardised and homogenous framework for Operational Risk Measurement across European financial institutions. Therefore the prevailing type of reference date should be clearly specified and applicable to all banks for any type of loss events. As the accounting date is mentioned as relevant for provisions, this shall be prevailing for all eligible loss events to the LC computation registered in the data history. Definition of a standard reference date (CH&Co suggestion) In the following slides, CH&Co assumes the reference date is the accounting date for any type of loss events What are the limits? Starting assumption | Definition of a prevailing reference date
  9. 9. 9GRA – Op Risk | Survey | SMA– June 2016 © Chappuis Halder & Co.| 2016 | All rights reserved Q2. What are respondents’ views on the inclusion of loss data into the SMA? Are there any modifications that the Committee should consider that would improve the methodology? Limits and proposals to BCBS’ questions (Q2, Q3) Reminder of questions 2 et 3 Q3. What are respondents’ views on this example of an alternative method to enhance the stability of the SMA methodology? Are there other alternatives that the Committee should consider?
  10. 10. 10GRA – Op Risk | Survey | SMA– June 2016 © Chappuis Halder & Co.| 2016 | All rights reserved Qualitative asymmetry (considering a 10-year loss history) • Data collection standards (in terms of quality and completeness) improved and will vary between t-10 and t : banks should get to benefit from a “learning effect” over time, enhancing their data collection and treatments standards • When computing loss events across a 10-year history, the LC therefore aggregates losses of variable quality. History mismatch within the LC/BI ratio (as defined by BCBS §23 bullet point 1) • There is a mismatch between the depth of the LC history (5 to 10 years) and the BIC history (3 years) • Thus, the ratio is skewed. Flexibility to define the depth of the loss data history – 5 to 10 years depending on available loss data (cf. §43 bullet point 1) • This might leave open the possibility of loss events arbitrage, especially in the case of remote historical events (> 5 years). • A 5-year loss history is considered less illustrative of the loss distribution profile for a given bank compared to a 10-year one, however a loss happening 10 years ago might not be representative of the current bank’s profile Limits • BI data history: The reviewed definition of the BI computation will necessarily force banks to recalculate the BI pre-2016 to comply with the mandatory BI depth. In practice, this can burden banks with additional computations. • Loss data history: Some banks do not hold a 10-year internal data history (loss events), which also requires a significant effort on the long-term in terms of both completeness and quality of the data collection. Proposals to the Basel Committee: Align both BI and LC history on a 10-year observation period (1) Define a minimum required in terms of the history depth for a bank starting the SMA methodology : 5 years for both LC and BI history (2) The ratio must match the histories taken into account whatever the depth chosen: 5 years minimum for the LC and BI, ideally 10 years. But, if a bank hold only 5 years of data for the BI and 10 years for the LC, the logic of our proposal is to calculate the ratio with 5 years for each component. (3) Set up a transitional period (following BCBS’ suggestion on the LC history in§43 bullet point 1) for banks which do not hold enough data, then to fill this gap it is recommended to use additional information through stress-tests on external data in order to complete the 10 years required. (4) Complete the internal data base with additional data : especially losses events with a probability of occurrence comprised between 5 and 10 years. 2 types of data might be used : simulated losses (via internal scenarios – EBA stress tests for example) or external data sourced from external database. Proposal |Align both BI and LC observation period to provide for a long-term and sound standard What are the limits? Limits and propositions on Q2 (1/3) Align the depth of both BI and LC histories to provide for a long-term and sound standard
  11. 11. 11GRA – Op Risk | Survey | SMA– June 2016 © Chappuis Halder & Co.| 2016 | All rights reserved What are the limits? Open-ended definition of a de-minimis threshold for internal loss data collection (mandatory, defined by each bank, §43 bullet point 4) The Basel Committee assumes that a threshold shall be applied when collecting loss data to guarantee the soundness of internal data. Though, this de-minims gross loss threshold cannot exceed 10 K€ for banks with an existing loss data history. How to define this de-minimis threshold ? The inappropriate calibration of the threshold might affect the quality of data collection: • An exceedingly low threshold might constrain to collect marginal/irrelevant incidents and might limit the OR system efficiency (time- consuming collection) • An exceedingly high threshold can prejudice the efficiency of the OR framework in neglecting important risk situations and/or area Proposals to the Basel Committee (1) Materiality : Consideration of a materiality threshold which has to illustrate the loss distribution and the bank’s risk appetite according to its different business activities. (2) Internal calibration (by banks) : Banks should define their own threshold(s) considering their risk appetite and their business activities. However, in order to stabilize the calibration methodology, BCBS has to insist on the: • Homogenization of the thresholds’ definition and distinction of local thresholds (per activity) and aggregated threshold for the bank’s mitigation as a group  Based on the diversification of the banks’ activities/portfolios, it is recommended to use a threshold adjusted to each business line in order to mitigate the risk locally and to reflect the loss profile related.  The capital requirements calculus in terms of the SMA methodology will then restrain the losses counting to a certain threshold calibrated by the bank as a group. Proposal | Define and adapt reporting thresholds per type of bank/activity Limits and propositions on Q2 (2/3) Define indicative reporting thresholds, to be defined per type of bank/activity
  12. 12. 12GRA – Op Risk | Survey | SMA– June 2016 © Chappuis Halder & Co.| 2016 | All rights reserved What are the limits? What are the suggested solutions? Lack of granularity in the loss classes and absence of losses frequency • The LC is a weighted sum of the empirical averages of losses on observed events (based on the loss classes developed above) • The weight (3) depends on the losses’ amounts and are calibrated by the Basel Committee through the QIS 2015 (cf. formula LC Component §35) • The 3 levels (defined as loss classes) are lacking in granularity. The gaps between the loss classes are significant as they put at a same stage different levels of losses The variations in the losses profiles are not considered in the LC calculus: The weight coefficients are point in time on the QIS 2015, no future adjustment is mentioned by the Basel Committee Correction of the LC formula to precise the losses weighting (cf. solution 2) • Addition of new Loss Classes: the LC formula has to close the gaps by including more levels for losses values especially for Loss Class 1 & 2 • Periodic review of the weighting by the Basel Committee, based on :  The variation of the systemic and idiosyncratic risks for the period of time considered  The evolution of loss distributions collected by the Basel Committee through the previous QIS and the variation of risk sensitivity Review of the Loss Component computation Inclusion of 10 years of loss history in LC • Losses are not discounted over time • The difference in the underlying loss: based on the type of loss, the amortization rate is different. For example, a technical equipment claim versus a cash amount loss cannot be capitalize the same way Capitalization of losses considered as an opportunity cost, using a discount factor explained by: (cf. solution 1) • Risk Free rate + Internal rate of return: with the assumption that the reference date is the date of accounting of the loss • Exchange rate: if the losses considered are not in euros (with the same assumption on the reference date) Aggregation of each loss amount to a specific annual depreciation rate adapted to its underlying: • Losses are considered as a decreasing sequence over time as they are weighted by their occurrence date Losses accounting through the bank’s history CH&Co Assumption In the rest of this document, 3 loss classes are considered and defined in the CD as follows: • Loss class 1 : losses on observed events with amounts lower than 10M€ • Loss class 2 : losses on observed events with amounts between 10M€ and 100M€ • Loss class 3 : losses on observed events with amounts higher than 100M€ Limits and propositions on Q2 (3/3) Inclusion of internal data in the LC calculus
  13. 13. 13GRA – Op Risk | Survey | SMA– June 2016 © Chappuis Halder & Co.| 2016 | All rights reserved Efficiency of the OR system not properly accounted We consider that an Operational Risk system improves its effectiveness if it is able to: • Adapt/Adjust its management of the risks regarding the previous events especially in the prevention and mitigation processes which represents heavy investments for banks • Project and control over time the evolution of the different components used in the SMA methodology (BI and LC) Backward-looking vision only • The SMA methodology include exclusively past losses and incomes • The method is not future-oriented in terms of the BI (Gross Income projections are not included) Weak considerations of the systemic risk’s effects • The model considers only the idiosyncratic part of the risk as it is based on the internal data of the bank • The lack of external data inclusion and stress-tests based on specific scenarios is an issue for on optimum understanding of the variations of the systemic risk over time Introduction of a « rewarding » dimension based on the OR system efficiency (cf. solution 3.1) The reward should be adjusted on the bank’s ability to control its: • Operational Risk exposure (observed events from Loss Class 1 over the last 3 years) • Activities’ volume (BI computed over the last 3 year) Introduction of a forward-looking component which takes into account the variations of the OR exposure over time (cf. solution 3.2) This component should be calibrated regarding: • The projections of the expected losses (EL) and BI for 3 years based on the expected variation of the OR exposure and the internal/external data (through stress-test scenarios) • The difference between the observed and the expected values (back- testing) Review of the SMA capital calculus What are the limits? What are the suggested solutions? Limits and solutions proposed for Q3 (1/2) Improvements proposed to review the SMA formula
  14. 14. 14GRA – Op Risk | Survey | SMA– June 2016 © Chappuis Halder & Co.| 2016 | All rights reserved Limits and solutions proposed for Q3 (2/2) Diagnosis of the alternative method (calibration of the m factor) Presentation of the alternative method: ILM calculus • The Committee suggests in the consultative document to calculate the Internal Loss Multiplier through an alternative method: ILM = 𝑚𝐿𝐶+ 𝑚−1 𝐵𝐼𝐶 𝐿𝐶+ 2𝑚−2 𝐵𝐼𝐶 m Where m is a factor to be calibrated • L’ILM is a multiplier illustrating the evolution of internal losses compared to the BI, its variation goes hand-in-hand with the capital requirements evolution over time • The alternative function presented above has to verify the same properties than the logarithmic function: the growth behaviour for an increasing ratio LC/BIC and the positive values. If we use theses conditions for the alternative method, we conclude that the m factor has to be greater than 1. 0 0,5 1 1,5 2 2,5 500 1000 1500 2000 2500 3000 3500 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500 7000 7500 8000 8500 9000 9500 10000 InternalLossMultiplier SMAcapital(inM€) Loss Component (in M€) Alternative : m = 1,2 Alternative : m = 1,5 BI = 9 500 M€ LC → +∞ The alternative aims at replacing the SMA methodology for banks with severe and low occurrence probability losses in their history (especially for the Loss class 3 : amounts above 100 M€) (cf. solution 4) The idea developed by the Basel Committee(annexe 2 CD) : • Regarding the weighting for losses > 100 M€ and the accounting of 10 years internal losses, the SMA methodology penalizes hardly banks which had in their history an extreme and infrequent loss. • Thus, in this cases, the alternative restrains the evolution of the capital requirements by delimiting the ILM to an m level. Comparison of the alternative method and the classic SMA methodology Main assets of the alternative method during stress periods of time (occurrence of extreme losses > 100 M€) • Stabilization of the impacts on capital requirements during an extreme loss shock for a given financial market • For a bank, the alternative method allows a limitation of the capital requirements volatility in case of the occurrence of severe losses and ability to hedge all the potential events Main drawback of the alternative method : • The SMA methodology through this alternative is more conservative than the classic SMA for a bank profile where the ratio LC/BIC is under 1 • Example : For a BI equal to 9 500 M€, and LC < 1 750 M€, SMA capitalalternative method > SMA capital classic method The m factor has to be calibrated according to: • The ability of the SMA formula to cover all the potential operational risks which the bank is expose to • The insurance of a stable financial market by minimizing the variations between banks in case of extreme losses
  15. 15. 15GRA – Op Risk | Survey | SMA– June 2016 © Chappuis Halder & Co.| 2016 | All rights reserved Table of contents 3 Outstanding Issues 2 Analysis of the Consultative Document of March 2015 2 Detailed solutions to be discussed • Detailed solution 1 | Losses accounting through the bank’s history • Detailed solution 2 | Review of the Loss Component computation • Detailed solution 3 | Review of the SMA capital formula • Detailed Solution 4 | Proposal of a calibration for the m factor 1 Introduction on the review of the SMA methodology based on theoretical profiles
  16. 16. 16GRA – Op Risk | Survey | SMA– June 2016 © Chappuis Halder & Co.| 2016 | All rights reserved Capitalisation Methodology of losses| The loss is considered as an “opportunity cost” 𝐿𝑜𝑠𝑠 𝑡0 = 𝐿𝑜𝑠𝑠 𝑡 ∗ 𝐹𝑋 𝑟(𝑡0) ∗ 𝐷𝐹 𝑡0, 𝑡 Proposal: The operational risk loss is considered as an opportunity cost in other words it’s the estimation of missing opportunities to invest in the bank’s businesses. This cost is then capitalized through a coefficient composed of: • Discount factor: 𝐷𝐹 𝑡0, 𝑡 = 𝑒 𝑅 𝑡0,𝑡 ∗(𝑡0−𝑡), 𝑡0 > 𝑡 • Risk free rate (OIS) and internal rate of return (IRR) : R 𝑡0, 𝑡 = IRR 𝑡0, 𝑡 +OIS 𝑡0, 𝑡 • Exchange Rate: 𝐹𝑋 𝑟(𝑡0) = 1, 𝑖𝑓 𝑡ℎ𝑒 𝑙𝑜𝑠𝑠 𝑖𝑠 𝑒𝑥𝑝𝑟𝑒𝑠𝑠𝑒𝑑 𝑖𝑛 𝑒𝑢𝑟𝑜 𝑟 𝑡0 = 𝐸𝑥𝑐ℎ𝑎𝑛𝑔𝑒 𝑟𝑎𝑡𝑒 𝑡0 , 𝑖𝑓 𝑛𝑜𝑡 Limit identified: The discount rate of losses amounts depends on each type of loss considered as the amortisation rate of the losses is based on the underlying considered (example : equipment loss vs. cash loss) Proposals to the Basel Committee: • We suggest to consider the losses history as a decreasing arithmetic-geometrical sequence over time and to adjust then the averages calculated in the Loss Component • Calibration of the coefficients for each business lines the loss considered is related to Main benefit The time weighting provides a stabilization of the LC as it avoids potential jumps in the formula due to the rolling history (if the outgoing amount’s loss is greater than the upcoming one) knowing that the older losses are less weighted over time 𝑡0 : date of actualization (CH&Co assumption : latest quarterly closing date) 𝑡 : loss reference date, (CH&Co assumption : accounting date) t0 > t For a given loss in t0 : Detailed solution 1 | Losses accounting through the bank’s history
  17. 17. 17GRA – Op Risk | Survey | SMA– June 2016 © Chappuis Halder & Co.| 2016 | All rights reserved Adding of new loss classes to precise the losses weighting Proposal: • Calibration by the Basel Committee of new intermediate levels (Loss Classes) based on the data collected through the QIS • The gaps between the different Loss classes have also to be correlated with the losses’ amounts which means that the borders’ classes will be exponentially increasing for higher amounts. Proposals to the Basel Committee: The Basel Committee has to set, according to the risk sensitivity and the data available in the previous QIS, an optimum number of classes with more levels for lower amounts, especially in Loss classes 1 and 2 (cf. illustration). Illustration: for a given profile, the 3 Loss Classes mentioned in the consultative document (in orange) and CH&Co suggestion (in grey) Proposal: Through the cycle Method (in grey + 𝛿 in orange) CH&Co suggests a review of the weighting coefficients regularly based on the evolution of the economic circumstances and the potential distortion of the losses profiles compared to the levels in 2015. • Review each 2/3 years by the Basel Committee of the coefficients through the adjustment of 𝛿 • Calibration of each coefficient based on the Loss class related (3 classes here: Loss class 1 : [0;10M€ [, Loss class 2 :[10;100M€[ et Loss class 3: [100; +∞[ ) Formula: • The adjustment explained above is illustrated by factors 𝛿𝑖 (i, the number of Loss class considered) • Where δ1, δ2, δ3 are positive or negative coefficients depending on the shape of the variations observed by the Basel Committee in the data collected Loss Component 𝑡0 = (7 + 𝛿1) ∗ 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑡𝑜𝑡𝑎𝑙 𝐴𝑛𝑛𝑢𝑎𝑙 𝐿𝑜𝑠𝑠 + (7 + 𝛿2) ∗ 𝐴𝑣𝑒𝑟𝑎𝑔e Total annual Loss only including loss events above > 10 M€ + (5 + 𝛿3) ∗ 𝐴𝑣𝑒𝑟𝑎𝑔e Total annual Loss only including loss events above > 100 M€ New weighting of losses based on the economic circumstances Methodology suggested by the Basel Committee: Point in time Method (in grey) • Description : The coefficients considered in the consultative document are calibrated in accordance with the situation collected by the BIS in 2015, which means that they are point in time on the current QIS. • Bias: The distribution profiles depend on the variation of the systemic risk over time (volatility off the market), economic circumstances ... Thus, the point in time method does not take into account these external time-depending factors Detailed solution 2 | Review of the Loss Component computation Frequency (lossesoccurrence) Severity (losses amount) 10 M€ 100 M€0 M€
  18. 18. 18GRA – Op Risk | Survey | SMA– June 2016 © Chappuis Halder & Co.| 2016 | All rights reserved SMA capital (t0) = 110 + 𝐵𝐼𝐶 − 110 ∗ 𝐼𝐿𝑀 ∗ (1 + 𝐶𝐴𝑅 𝑦−3;𝑦0 𝑡0 ) y0, t0 respectively represent the year and corresponding date of computation (t0 should correspond to the end-of-year account closing date). y-1, y-2, y-3 respectively represent the considered 3-year observation period. Methodology • At the end of a given year y, each bank would consider the year-on-year variations of the 𝑶𝑳 𝒚 𝟏 𝑩𝑰 𝒚 ratio over the past 3 years (i.e. from y0 to y-3). Then the bank would identify the year-on-year trends of each variation over the past 3 years. • From CH&Co’s premises, if all these variations are strictly negative over the past 3 years, then the bank should be rewarded on the SMA Capital at the end of y0. That is to say, if: • The reward to be affected to the SMA capital should be proportional to 𝟏 + 𝑪𝑨𝑹 𝒚−𝟑;𝒚 𝟎 𝒕 𝟎 , the compound annual rate of the ratio over the past 3 years. This rewarding factor would then take into account the speed of the 𝑶𝑳 𝒚 𝟏 𝑩𝑰 𝒚 ratio over the past 3 years and proportionally reward the bank. We assume that Proposal • Description : Offering a reward (reducing the overall SMA Capital amount) for banks demonstrating a significant efficiency of their OR framework, CH&Co considers 3-year period should be sufficiently robust to illustrate this improvement. Theoretically, the efficiency of the OR framework is demonstrated by the capability of the bank to manage their OR exposure (losses observed from Loss class 1 ) in view of its volume of business (BI). • Implementation : Inclusion of a decreasing indicator which is activated if and only if the ratio 𝑂𝑏𝑠𝑒𝑟𝑣𝑒𝑑 𝐿𝑜𝑠𝑠𝑒𝑠 𝐶𝑙𝑎𝑠𝑠 1 𝑩𝑰 has a decreasing tendency during the last 3 years In what circumstances, the ratio is decreasing? The improvements in the bank’s OR management and its risk profile are observed if : • Situation 1: The revenues (BI) grew faster than losses (OL1) over the year. • Situation 2: Losses decreased while the volume of activity stagnated or grew. • Situation 3: In case of loss stagnation, a decreasing variation of the ratio is fully explained by a strong stimulation of the business revenues and volume. Detailed solution 3.1 | Review of the SMA capital formula Evolution of the OR framework efficiency Formula
  19. 19. 19GRA – Op Risk | Survey | SMA– June 2016 © Chappuis Halder & Co.| 2016 | All rights reserved SMA capital (t0) = 110 + BIC − 110 ∗ 𝐼𝐿𝑀 ∗ 𝑔 𝑂𝐿 𝑦0 1 − 𝐸𝐿 𝑦−1 1 𝑂𝐿 𝑦0 1 are the observed losses from Loss Class 1 at the end of the year 𝐸𝐿 𝑦−1 1 are the expected losses from Loss Class 1, estimated in y-1 for the next year y0 • The rewarding function g is activated if and only if the gap between the projections of Loss Class 1 and the observed values are comprised in a confidence interval calibrated by the Committee : with 95% of confidence level and 𝜎 the standard deviation of 𝐸𝐿 𝑦−1 1 Detailed solution 3.2 | Review of the SMA capital formula Quality of the risk appetite projections Methodology Proposal • Description : Introduction of a rewarding function depending on the measurement of the gaps between the projections and the real values of the Loss Class 1. The projections are based on internal and external data and scenarios, estimation of the systemic risk and GNP projections based on macroeconomic indexes • Calibration of the rewarding annually  At the end of each year y, each bank would provide projections of their expected loss amounts belonging to Loss Class 1 for the year to come y+1 (𝐸𝐿 𝑦+1 1 ). This projection would then be compared at the end of y+1 to the observed losses from Loss Class 1 (𝑂𝐿 𝑦+1 1 ). Formula
  20. 20. 20GRA – Op Risk | Survey | SMA– June 2016 © Chappuis Halder & Co.| 2016 | All rights reserved Final combined proposal SMA capital (t0) = 110 + BIC − 110 ∗ 𝐼𝐿𝑀 ∗ 𝛾1 ∗ ∗ 𝛾2 ∗1 2 Indexed rewarding factor on the quality of the risk appetite projections Indexed rewarding on the efficiency of the OR framework Solutions 3.1 et 3.2 combined | Review of the SMA capital formula SMA capital (t0) = 110 + 𝐵𝐼𝐶 − 110 ∗ 𝐼𝐿𝑀 ∗ (1 + 𝐶𝐴𝑅 𝑦−3;𝑦0 𝑡0 ) 𝑟𝑒𝑤𝑎𝑟𝑑𝑖𝑛𝑔 𝑓𝑎𝑐𝑡𝑜𝑟 y0, t0 respectively represent the year and corresponding date of computation (t0 should correspond to the end-of-year account closing date) y-1, y-2, y-3 respectively represent the considered 3-year observation period 𝐶𝐴𝑅 𝑦−3;𝑦0 𝑡0 is the compound annual rate of the ratio over the past 3 years 1 SMA capital (t0) = 110 + BIC − 110 ∗ 𝐼𝐿𝑀 ∗ 𝑔 𝑂𝐿 𝑦0 1 − 𝐸𝐿 𝑦−1 1 𝑟𝑒𝑤𝑎𝑟𝑑𝑖𝑛𝑔 𝑓𝑎𝑐𝑡𝑜𝑟 𝑂𝐿 𝑦0 1 are the observed losses from Loss Class 1 at the end of the year 𝐸𝐿 𝑦−1 1 are the expected losses from Loss Class 1, estimated in y-1 for the next year y0 g is a function to be calibrated by the Basel Committee 2 These 2 solutions remain independent, however it is possible to combine them as long as the indicators’ impacts on the SMA capital are limited based on the impact and sensitivity of each components added Proposal to the Basel Committee γ1,γ2 are weighting coefficients to be calibrated by the Committee in view of the impacts and sensitivity of each rewarding factor on the SMA capital.
  21. 21. 21GRA – Op Risk | Survey | SMA– June 2016 © Chappuis Halder & Co.| 2016 | All rights reserved Objective: Limitation on the capital requirements volatility for a given bank in case of severe losses and ability to hedge all the potential events Illustration of the method proposed based on two simulated banks’ profiles • Simulation of a shock between times t et t+1 : LC is doubled • Estimation of the SMA capital in t (cf. points C1) and t+1 (cf. points C3) with an m factor which equalizes the alternatives of the ILM calculus • Projection of the capital requirements in t+1 calculated through the alternative with the m level of time t (cf. point C2)  The difference between C1 and C3 is the adjustment that has to be considered in order to insure an efficient risk hedging Proposals to the Basel Committee • Adjustment of the m factor each year by the banks based on the simulations of losses with high severity and low occurrence probability • Definition, by the Basel Committee, of the classic and extreme cases that have to be simulated by the banks in order to stress tests their parameters and validate the m level. It is recommended to use external data and extreme scenarios (Robustesse Group for the French banks for example) Solution 4 | Proposal of a calibration for the m factor (1/2) Stand-alone calibration | Considering a given bank Illustration • Simulation of two banks’ profiles, bank A and bank B: BIA = 5000 M €; LCA = 1000 M € BIB = 9500 M €; LCB = 2000 M € • For each bank i (i = A or B) :  Point Ci; 1: Projection of the factor m related to the SMA capital considering the loss distribution profile in time  Point Ci; 2: Estimation of the capital requirements post-shock for the same m level as in time t  Point Ci; 3: Projection of the SMA capital post- shock with an adjustment of the m factor in order to equalize the initial and alternative ILM functions • The capital requirements’ volatility of each bank is illustrated by the distance between mi;1 et mi;3 Factorm SMA capital (M€) BIB = 9 500 M€BIA = 5 000 M€ ∆𝑶𝑹𝑪𝑹𝑨 = 𝟐𝟏𝟖 𝑴€ CA;1 CB;1CA;2 CA;3 Δ𝒎𝑨=0,27 CB;2 CB;3 𝜹𝑶𝑹𝑪𝑹𝑩 = 𝟒𝟕 𝑴€∆𝑶𝑹𝑪𝑹𝑩 = 𝟒𝟖𝟓 𝑴€ Δ𝒎𝑩=0,28 𝜹𝑶𝑹𝑪𝑹𝑨 = 𝟐𝟐 𝑴€ ORCR: Operational Risk Capital Charge
  22. 22. 22GRA – Op Risk | Survey | SMA– June 2016 © Chappuis Halder & Co.| 2016 | All rights reserved Detailed solution 4 | Proposal of a calibration for the m factor (2/2) Global calibration | Considering banks across the European financial market Objective: Homogenization of the m factor adjustments for similar banks by reducing the capital requirements’ variations in case of an extreme shock. This proposal constitutes an extension of the previous one, once the m factor is calibrated by each bank Proposals to the Basel Committee • Simulation of severe shocks and scenarios of all the banks’ profiles collected by the Basel Committee • Classification of the banks regarding their SMA capital sensitivity regarding the occurrence of an extreme loss (distance between points C1 and C3) • Average interval [minf ; msup] for each bank group based on the variations observed to limit the clouds’ distortion • Each bank in a given group, will calibrate its m factor too compute its capital requirements, depending on the interval allowed by the Committee Factorm SMA capital (M€) A given bank class before shock 𝒎𝒊𝒏𝒇; 𝒎 𝒔𝒖𝒑 Interval allowed for m factors After shock Methodology Based on the previous scenario, we suggest a 3-step methodology: • Step 1 | Clustering of the banks according to their stress sensitivity Our proposal is based on the classification of banks per group considering their SMA capital sensitivity for a similar scenario (LC is doubled in this case). This means that – in our proposal – the Committee would analyse, for each bank, the distance between m factor before and after shock. The greater the distance, the higher the sensitivity, and vice versa. • Step 2 | Analysis of the scatterplots (pre- and post-stress) For each group, the Basel Committee will project:  A cloud of points C1 for each bank of a given group  A cloud of points C3 for each bank of a given group • Step 3 | Definition of the m factor and associated confidence interval The Committee calibrates the m factor and defines an optimum interval (maximum and minimum value of the m factor for a given group of banks). Each bank from the same group will have to respect the interval and provide their data to calculate the m factor, so that the Committee can ensure they stick to the required confidence interval. Illustration
  23. 23. 23GRA – Op Risk | Survey | SMA– June 2016 © Chappuis Halder & Co.| 2016 | All rights reserved Table of contents 2 Analysis of the Consultative Document of March 2015 4 Outstanding Issues 3 Detailed solutions to be discussed 1 Introduction on the review of the SMA methodology based on theoretical profiles
  24. 24. 24GRA – Op Risk | Survey | SMA– June 2016 © Chappuis Halder & Co.| 2016 | All rights reserved Why the SMA methodology omits the effects of the plans implemented for hedging the most severe losses? (e.g. reinsurance policies, hedging products like cat-bonds) ? Scope : Risks concerning natural disasters (fire/major flood), pandemics, terrorist attacks etc. which are handled by the banks with restricted flexibility in their anticipation • As it is difficult for banks to mitigate/contain these specific risks, shall we consider an accounting of the hedging costs in the loss data treatment (Gross loss after recoveries and insurance costs) and the LC estimation ? • This suggestion is also based on the current opportunity for AMA banks to reduce their OR capital charge to up to 20%. BCBS considered such policies had beneficial effects on the risk exposure, but also and most importantly, on the quality of the OR framework and risk assessment. (See BCBS 181, Recognising the risk mitigating impact of insurance in operational risk modelling, October 2010) Outstanding Issues Questions addressed to be discussed Why do both BI and LC include provision loss amounts? How should provisioned loss events be considered? As a component of the revenues of the bank (and then included in the BI) or as part of the loss data history (and then included in the LC)? Scope : Provisioned loss events • Provisioned loss amounts are considered as an observed loss in the LC : they are weighted according to their severity • Provisioned loss amounts in the BI : is part of « Other Operating Expenses » (OOE) in the Services Component (cf. Annex 1, Services component bullet points 2 & 3)  provisions for losses on operational risk events  Costs for reserve funds used in the OR hedging of upcoming losses
  • SubhadipBhattacharya2

    Sep. 6, 2016
  • AudreyGhione

    Jun. 16, 2016

Following BCBS publication in March 2016, CH&Co welcomes the opportunity to provide comments on the consultative document covering the Standardised Measurement Approach for Operational Risk. We have though suggested some areas for improvement, based on our research and simulations, which are presented in the exhibits. In this regard, please find in these slides our analysis and suggestions, on what we consider as important points for discussion. For further information on BCBS consultative document, please follow this link : http://www.bis.org/bcbs/publ/d355.htm. For further information on the key impacts of the reviewed SMA, please follow this link and read our contribution : http://fr.slideshare.net/HeleneFreon/chcosma-reviewop-risk-survey-and-challenges. CH&Co is a consulting firm which specializes in supporting clients within the financial services sector. We have developed a strong Risk Management practice, and through our missions and mandates, we have had the chance to build specific expertise in Operational Risk.

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