CASE STUDY DEPOSIT GUARANTEE FRAMEWORKS

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1 CASE STUDY DEPOSIT GUARANTEE FRAMEWORKS 19 NOVEMBER 2014 FINANCIAL SERVICES

2 Section 1 Introduction to Oliver Wyman

3 Oliver Wyman serves a wide range of industries, but focus in the Nordics is Financial Services Industries Financial Services Practice Industry groups Corporate & Institutional Banking Retail & Business Banking Wealth & Asset Management Insurance Public Policy Corporate Strategy Corporate Finance and Advisory Finance & Risk Strategic IT & Operations Key capabilities 2

4 Oliver Wyman is an international firm, with a network of offices across 50 countries More than 50 offices in 25 countries Our global staffing model supported projects in over 60 countries last year Countries where Oliver Wyman has an office Additional countries with Oliver Wyman projects since

5 Examples of recent project experiences in the Nordic region Credit process redesign Performance and strategy review Oliver Wyman supported a major Nordic bank in redesigning the credit process for retail customers and small and medium-sized corporates The objectives of the project was to Increase efficiency in the credit process through standardisation and automation Reduce headcount Increase harmonisation in credit decision making The project designed a new end-to-end credit process, including: Credit decisioning Risk scoring Pricing A European bank that experienced a significant drop in profitability and ROE during the financial crisis asked for Oliver Wyman s support in improving its financial performance Oliver Wyman performed an assessment of the situation and developed a range of recommendations that the client should take to improve its performance All initiatives were prioritised and detailed in an implementation roadmap, taking into account: Regulatory impact Business value Cost of implementation Organizational constraints (e.g. resource capacity, infrastructure, capabilities, etc.) 4

6 Globally, Oliver Wyman works with central banks and governments; including preparing the ECB to take over the supervision of 128 Eurozone banks 5

7 Section 2 Case study Deposit guarantee framework

8 Deposit guarantee systems protect bank depositors from losses during bank failures and thereby work as a safety net and promote financial stability What it is Deposit guarantee schemes reimburse deposits to depositors whose bank has failed From the depositors point of view, this protects a part of their wealth from bank failures From a financial stability perspective, this prevents depositors from making panic withdrawals from their bank, thereby preventing severe economic consequences Why it is needed Gives depositors comfort that their funds are not at risk, thus reducing the risk for bank runs Reduces volatility among depositors as they do not need to assess bank riskiness when depositing money Provides countries with an orderly process for dealing with bank failures and a mechanism for banks to fund the cost of failures How it works Deposit guarantee systems are most often established and managed by a government body Should adhere to a number of principles Membership should be compulsory for all financial institutions that accept deposits Coverage in the EU is currently EUR Pay-out time in case of bank failure is typically around 7 days Source: European Commission, BCBS 7

9 Northern Rock bank run, 14 September

10 A finance minister asked Oliver Wyman to support the structuring of the country s deposit guarantee scheme The project was delivered in three stages 1 Hypothesis generation Diagnostic 2 3 and analysis Provide recommendations Understand the country s specific rules and regulations Understand the client situation and context (how does the current system work?) Understand how other countries have designed their deposit guarantee frameworks Based on the outcome from the diagnostic, develop a hypothesis for the target structure of the guarantee scheme Discuss hypothesis with client to get input and challenge Conduct analysis to test the hypothesis Provide recommendations for a target structure based on the findings from the analysis Develop an implementation roadmap for the client Socialise findings and recommendations with relevant stakeholders 9

11 Deposit guarantee frameworks are typically set up in one of two ways Description Pros Cons Public treasury 1 2 Taxpayer money is used to pay out to depositors in case of bank failure Simple system as the government pays out from public treasury in case of emergency Not a popular approach with the public as taxpayer money is used to reimburse depositors Banks don t bear the costs for potential failures Reactive approach that draws funds from public treasury in case of emergency rather than proactively building up funds Guarantee fund A fund is set up which can be used to draw funds from in case of bank failure All banks that hold deposits must pay in to the fund Banks bear the risk of failure themselves Popular approach with the public as it doesn t burden public treasury Proactive approach as it collects funds before they are needed More complex system than pulling funds from the public treasury; a number of practicalities must be agreed, e.g.: What should the target size of the fund be? How much should banks pay in to the fund each year? Should the fund take risk with its assets? Selected project approach 10

12 Banks use an Expected Loss framework to estimate losses due to e.g. loan impairments Expected Loss (currency) Probability of default (%) Loss given default (%) Exposure at default (currency) The estimated loss on a portfolio The average percentage of borrowers that default in the course of one year The magnitude of likely loss (the percentage of the total loan amount that the bank would lose) in case of default The outstanding amount to which the bank is exposed to at the time of default Example: If Nordea lends out 100 SEKm to Ericsson, the expected loss on that loan is SEK if: PD = 1% (there is a 1% risk that Ericsson defaults within one year) LGD = 50% (if Ericsson defaults within one year, the loss for Nordea will be 50% of the loan size) EAD = 8 SEKm (the loan amount at the time of default is estimated to be 8 SEKm) 11

13 Deposit guarantee funds can use the expected loss framework to estimate a theoretical size of the fund Banks Expected Loss For a guarantee fund, the expected loss is the amount that is expected to be drawn from the fund Probability of default For a guarantee fund, PD is the probability of default for a bank in the financial system Loss given default For a guarantee fund, LGD represents the proportion of the exposure that must be drawn from the fund (In case of default, any assets available in the bank are used first, before the fund is tapped) Exposure at default For a guarantee fund, EAD represents the total amount of insured deposits at the time of default What are the weaknesses with using an Expected Loss framework to estimate the target size of the fund? 12

14 Too connected to fail? Financial institutions are highly interconnected and the failure of one bank can have major contagion effects Illustration of bank inter-connectedness Comments The financial system is deeply interconnected Banks are connected to each other and borrow from, and lend to, each other in increasingly complex, recursive ways One of the fundaments of the banking system today is that banks count on being able to borrow and lend to each other (e.g. overnight funding and derivative transactions) If one bank in the network stumbles, the repercussions ripple out in many directions and a crisis can ensue (this happened to Lehman) The system can stumble based on softer issues too Swedbank was partly shut off from the interbank lending system in 2008 based on rumors that it had liquidity issues Handelsbanken on the contrary did not need to participate in the interbank lending market during that period due to large amounts of excess liquidity Source for image: The Finanser 13

15 Example of PD estimation in a bank context: Both qualitative and quantitative measures are evaluated to calculate the probability of default Example process for calculating the probability of default (generic example) Probability of default Qualitative factors Quantitative factors Financial strength of owner Experience of leadership Number of missed payments Total sales ROE Debt-to-equity ratio Regress using historical data A regression model is built to find statistical relationships between PD and the chosen quantitative and qualitative factors Expert judgment overlay Ultimately, expert judgment is applied to any model output to cater for risk aspects not captured by the model 14

16 Probability of default is inherently difficult to estimate in practice as it is a measure of a potential future event Some of the problems with estimating PD are: Most estimates are based on historical experiences, which may or may not be representative of the future There are correlation effects that are difficult to assess Models may be overly simplistic and make crude assumptions of reality Available information regarding the borrower or institution is not always available, correct or sufficiently detailed Etc. Due to these reasons, many institutions complement their PD models with stress testing, e.g.: Scenario analysis Sensitivity test of input parameters 15

17 There are different types of stress testing approaches Scenario tests Sensitivity tests Historical scenarios Actual historical events are applied to the portfolio to assess implications The purpose of the test is to assess how the portfolio would perform during these scenarios 9/11 terrorist attacks Asian Crisis 1997 Black Monday Oct Hypothetical scenarios Hypothetical scenarios are created to stress the portfolio, e.g.: Banking crisis leading to 10% GDP decrease and 10% increase in income taxes Housing crisis leading to 10% year-on-year property price decreases and doubling of unemployment over a five year period Hypothetical scenarios can be tailored to be more relevant to the portfolio and current market environment than historical scenarios In sensitivity tests, underlying variables are adjusted to investigate portfolio impact, e.g.: Doubling of PD in portfolio for certain customer segments 20% reduction of deposit volumes Sensitivity tests are typically easy to define and implement and are often used at trading desks Reverse tests try to identify how large shocks are required to trigger a predefined event, e.g.: How much must the stock market fall for us to make a 10% loss on the portfolio? 16

18 How much should each bank pay into the fund? The project proposed a fee structure based on three key drivers Size of insured deposit base 1 Forecasting of future deposit base Proportion of insured vs. uninsured deposits 2 3 Bank riskiness (probability of default) E.g. using banks credit ratings (from S&P or other rating agency) Base bank riskiness on amount of capital held for risky assets (according to Basel regulation) Allowed time for fund to reach target size The longer time it takes for the fund to reach its target size, the lower the banks fees will be Bank specific fee Fair system for participating banks as the fee takes into account bank specific characteristics The system results in cheaper deposit funding for safer banks 17

19 Should the fund take risks with its assets? Should the fund invest its assets and aim to get a return? Pros Cons The target size of the fund is (hopefully!) reached faster Participating banks can reduce the size of their fees Risk that size of assets is reduced if investment decisions are poor There is a political risk associated with this strategy and poor investment decisions may result in a political crisis The construction of the guarantee fund led to asymmetric risks that resulted in banks wanting to take more risks with the assets in the fund: If the fund would perform well, banks would reduce their fees If the fund would perform poorly, banks wouldn t have to pay higher fees If banks would default, they wouldn t have to care about the size of the fund; the state would cover any shortcomings of the fund 18

20 On-going management of the fund: If the guarantee fund decides to invest its assets, it will be exposed to a wide range of risks just like any other fund Major risk types for the fund Liquidity Risk Market Risk Concentration risk Interest Rate Risk Credit Risk Operational (Event) Risk Description/examples Will there be a market to sell the assets in? Will the assets lose value due to unfavourable market movements? Will specific losses in one asset have a large impact on the total portfolio? Will the assets lose value due to unfavourable interest rate movements? Will a counterparty default on its obligations? Will we encounter losses due to unforeseen events regarding people, process or system failures? 19

21 If the fund decides to invest its assets, a number of complications need to be addressed Complication How much risk should the fund take with its assets? Implication The fund should agree the level of accepted risk and tolerated losses with relevant stakeholders How can the fund ensure reimbursement to depositors in seven days in case of bank failure? Should the fund support its country s economy by investing in local government bonds? If the fund invests its assets, which risks is it exposed to and how should those risks be managed? The fund should invest in only highly liquid assets that can be sold quickly in case of bank failure If it does, the correlation between country risk and the banking system should be duly assessed and mitigated Market risk (overall performance of financial markets) Liquidity risk (difficulty to sell assets quickly) Credit risk (risk that a counterparty defaults) Etc. All of these issues should be addressed in a Risk Appetite Framework that defines the level of risk that the fund accepts and how they should be mitigated 20

22 Risk Appetite serves as one of the most important mediums for coordinating risk-taking activities across an organisation Strategy and stakeholder expectations Owners return expectations Growth objectives Regulatory risk management expectations Board defined top-down Risk Appetite statement Strategic Liquidity Market Reputational Other Credit risk types Embedding Planning and budgeting Capital allocation Incentives and compensation Cascading appetite / linkages to limits Governance Reporting and tracking New business limits Liquidity risk limits Bottom-up limits framework Market risk limits Reputational risk limits Other Credit Credit Risk limits Early warning indicators Breach management 21

23 Risk Appetite statements should be clearly defined and link to potential management actions that can be used to adjust the risk profile Client example Disguised client example of range of possible Risk Tolerance statements Metric Illustrative definition Green Amber Red Concentration risk We should not have any single exposure >10% of total assets 7% 7% - 10% 10% Quantitative Qualitative Debt rating Our senior debt should at all times stay above a Moody s rating of Baa Earnings volatility We will not miss consensus earnings forecast by more than X % at a YY % confidence level We will aim to consistently target dividend of XXX Maximum loss We do not wish to see a loss of more than XXX at the YY % confidence interval Liquidity headroom Available liquidity resources to meet requirements at XX % confidence interval Reputation Ensure that the highest ethical standards are followed at all times Regulation Have no significant instances of regulatory breach Governance Ensure appropriate policies and processes are followed at all times Growth All new business opportunities to follow appropriate risk controls 22

24 A monthly process should be in place to monitor potential breaches of risk appetite limits Illustrative example of how triggers can be used Projection Capital adequacy ratio Projection indicates imminent breach, requiring the development of an action plan to avoid a breach Warning limit Risk appetite limit Time If ratio enters red territory, the Board must take immediate action 23

25 Appendix Black swan events

26 Black swans are low probability, high impact events that we try to rationalise after the fact Black swan territory Likelihood Impact Black swans are events characterised by: Rarity: Highly unexpected, unlikely to occur and difficult to predict Impact: Massive impact once they occur Retrospective justification: Rationalised in hindsight 25

27 Induction doesn t always work: the future is not always a function of the past Experience bias can have costly consequences The relationship between the chicken and the farmer Days The relationship between the chicken and the farmer Day 1001 This guy is great, he feeds me every day! What happened? I thought we were friends Black swan events depend on your expectations and perspective per definition they must be unexpected 26

28 There are many examples of black swans events, most has had great impact on the industry it occur in or the way we live Failure of hedge fund Long Term Capital Management Hedge fund LTCM was driven into the ground as a result of the ripple effect caused by the Russian government's debt default The Russian government's default represents a black swan event because none of LTCM's computer models could have predicted this event and its subsequent effects Rogue trading events At Société Générale, trader Jérôme Kerviel was convicted in the 2008 for breach of trust, forgery and unauthorized use of the bank's computers, resulting in losses valued at 4.9 billion At Barings Bank, unauthorised speculative trading by Nick Leeson caused the collapse of the bank, triggered by the 1995 Kobe earthquake in Japan Discovery of America The Europeans discovering America was a black swan event from the natives perspective The encounter led to the near extinction of the native population, with an estimated 80% - 90% of the population died after the arrival of the Europeans Natural disasters and terrorist attacks On September 11 th 2001 no one expected the planes to hit the twin towers this triggered the war on terror and a wave of new international legislations In December 2004 an earthquake and subsequent devastating tsunami hit 18 countries, killed people and left 1.7 million people homeless 27

29 Setting up identification and control processes for black swan events is difficult, but an institution s general preparedness can often be improved The cost of mitigating all potential events is too high If black swan events are so difficult to identify, should we even try to set up controls in order to mitigate them? Even if it was possible to identify all possible black swan events, the cost of mitigating all would be momentous, making it implausible in reality E.g. Sweden could improve border control in the Baltic sea to mitigate intrusion from Eastern neighbours, but the cost of 100% control of the border can likely not be motivated a balance must be found against a pre-defined risk appetite Equally, the size of a deposit guarantee fund can be equal to the size of all deposits in the country The CRO is responsible for predicting and mitigating risks In the financial world, it is the role of the CRO to identify and mitigate risks Most CROs are experienced and realistic; they know that black swan events could happen but they equally know it is impossible to build a defence against all potential risks However, they also know that they will likely get fired if a black swan event indeed does happen So what should institutions do? It is impossible to calibrate models to predict all possible black swan events (e.g. terrorist attacks, IT failures, political decisions and epidemics) But the general crisis preparation can be improved, e.g.: A recovery and resolution plan can be defined (if an external event happens that effects our business significantly, the board and senior management will be gathered within 12 hours; systems will auto close all trades; etc.) Events can be bundled into major categories with action plans assigned against each Etc. 28

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