Bank6003 Notes

BANK6003 Final Exam Notes TOPIC 4A: Credit Risk – Estimating Default Probabilities Overview * Theory of credit risk less developed than VaR based models of market risk. * Much less amenable to precise measurement than market risk – default probabilities are much more difficult to measure than dispersion of market movements. * Measurement on individual loans is important to FI for pricing and setting limits on credit risk exposure. Default Risk Models 1. Qualitative Models * Assembling relevant information from private and external sources to make a judgement on the probability of default. Borrower specific factors (idiosyncratic or specific to individual borrower) include: reputation, leverage, volatility of earnings, covenants and collateral. * Market-specific factors (systematic factors that impact all borrowers include): business cycle and interest rate levels. * FI manager weighs these factors to come to an overall credit decision. * Subjective 2. Credit Scoring Models * Quantitative models that use data on observed borrower characteristics to calculate a score that represents borrower’s probability of default or sort borrowers into different default risk categories.
Linear Probability Models (LPMs) * Econometric model to explain repayment experience on past/old loans. * Regression model with a “dummy” dependent variable Z; Z = 1 default and Z=0 no default. * Weakness: no guarantee that the estimated default probabilities will always lie between 0 and 1 (theoretical flaw) Logit and Probit Models * Developed to overcome weakness of LPM. * Explicitly restrict the estimated range of default probabilities to lie between 0 and 1. * Logit: assumes probability of default to be logistically distributed. Probit: assumes probability of default has a cumulative normal distribution function. Linear Discriminant Analysis * Derived from statistical technique called multivariate analysis. * Divides borrowers into high or low default risk classes. * Altman’s LDM = most famous model developed in the late 1960s. Z < 1. 8 (critical value), there is a high chance of default. * Weaknesses * Only considers two extreme cases (default/no default). * Weights need not be stationary over time. 3. New Credit Risk Evaluation Models Newer models have been developed – use financial theory and financial market data to make inferences about default probabilities. * Most relevant for evaluating loans to larger corporate borrowers. * Area of very active continuing research by FIs. Credit Ratings * Ratings change relatively infrequently – objective of ratings stability. * Only chance when there is reason to believe that a long-term change in the company’s creditworthiness has taken place. * S&P: AAA, AA, A, BBB, BB, B and CCC * Moody’s: Aaa, Aa, A, Baa, Ba, B and Caa Bonds with ratings of BBB and above are considered to be “investment grade” Estimating Default Probabilities 1. Historical Data * Provided by rating agencies e. g. cumulative average default rates * If a company starts with a: * Good credit rating, default probabilities tend to increase with time. * Poor credit rating, default probabilities tend to decrease with time. * Default Intensity vs Unconditional Default Probability * Default intensity or hazard rate is the probability of default conditional on no earlier default. * Unconditional default probability is the probability of default as seen at time zero. Default intensities and unconditional default probabilities for a Caa rated company in the third year * Unconditional default probability = Caa defaulting during the 3rd year = 39. 709 – 30. 204 = 9. 505% * Probability that Caa will survive until the end of year 2 = 100 – 30. 204 = 69. 796%. * Probability that Caa will default in 3rd year conditional on no earlier default = 0. 09505/0. 69796 = 13. 62% Recovery Rate * Usually defined as the price of the bond 30 days after default as a percent of its face value. * Recovery rate % = 1 – LGD% * Ranking of bonds * Senior Secured * Senior Unsecured Senior Subordinated * Subordinated * Junior Subordinated Credit Default Swaps * Instrument that is very useful for estimating default probabilities is a CDS. * Buyer of the insurance obtains the right to sell bonds issued by the company for their face value when a credit event occurs and the seller of the insurance agrees to buy the bonds for their face value when a credit event occurs. * The total value of the bonds that can be sold is known as the CDS’ notional principal. * Total amount paid per year, as a percent of the notional principal, to buy protection is known as the CDS spread. Buyer of the instrument acquires protection from the seller against a default by a particular company or country (the reference entity) * Example: buyer pays a premium of 90bps per year for $100m of 5-year protection against company X. * Premium is known as the credit default spread. It is paid for the life of contract or until default. * If there is a default, the buyer has the right to sell bonds with a face value of $100m issued by company X for $100m. * Payments are usually made quarterly in arrears * In the event of default, there is a final accrual payment by the buyer * Attractions of the CDS market Allows credit risks to be traded in the same way as market risks * Can be used to transfer credit risks to a third party * Can be used to diversify credit risk Credit Indices * Developed to track credit default swap spreads. * Two important standard portfolios are: * CDX NA IG, portfolio of 125 investment grade companies in North America * iTraxx Europe, portfolio of 125 investment grade companies in Europe * Updated on March 20 and September 20 each year. * Example * 5 year CDX NA IG index is bid 165bp, offer 166bp. Quotes mean that a trader can buy CDS protection on all 125 companies in the index for 166 basis points per company. * Suppose an investor wants $800,000 of protection on each company. * The total cost is 0. 0166 x 800,000 x 125 = $1,660,000. * When a company defaults, the investor receives the usual CDS payoff and the annual payment is reduced by 1,660,000/125 = $13,280. * Index is the average of the CDS spreads on the companies in the underlying portfolio. Use of Fixed Coupons * Increasingly CDS and CDS indices trade like bonds so that the periodic protection payments remain fixed. A coupon and a recovery rate is specified. * Quoted spread > coupon, buyer of protection makes an initial payment. * Quoted spread < coupon, seller of protection makes an initial payment. Credit Spreads * Extra rate of interest required by investors for bearing a particular credit risk. CDS Spreads and Bond Yields * CDS can be used to hedge a position in a corporate bond. * Example: investor buys a 5-year corporate bond yielding 7% per year for its face value and at the same time enters into a 5-year CDS to buy protection against the issuer of the bond defaulting. CDS spread is 2% p. . Effect of the CDS is to convert the corporate bond to a risk-free bond. If the bond issuer does not default, the investor earns 5% per year. If the bond issuer defaults, the investor exchanges the bond for its face value and this can be invested at the risk-free rate for the remainder of the five years. The Risk-Free Rate * The risk-free rate used by bond traders when quoting credit spreads is the Treasury rate. * Traditionally used LIBOR/swap rate * Normal market conditions: risk free rate is 10bp less than the LIBOR/swap * Stressed conditions, the gap is much higher Asset Swaps Provide a direct estimate of the excess of a bond yield over the LIBOR/swap rate. * Example: asset swap spread for a particular bond is quoted as 150 basis points. 3 possible situations: 1. Bond sells for its par value of 100. Company A pays the coupon and Company B pays LIBOR plus 150bp. 2. Bond sells below par, say 95. Company A pays $5 per $100 of principal at the outset. After that, Company A pays the coupon and Company B pays LIBOR plus 150bp. 3. Bond sells above par, say 108. Company B pays $8 per $100 of principal at the outset. After that, Company A pays the coupon and Company B pays LIBOR plus 150bp. Therefore, the present value of the asset swap spread is the present value of the cost of default. CDS-Bond Basis * CDS-Bond Basis = CDS spread minus the bond yield spread * Bond yield spread is usually calculated as the asset swap spread * Should be close to zero, but there are a number of reasons why it deviates: 1. Bond may sell for a price significantly different from par (above par = positive basis, below par = negative basis) 2. There is counterparty risk in a CDS (negative direction) 3. There is a cheapest-to-deliver bond option in a CDS (positive direction) 4.
Payoff in a CDS does not include accrued interest on the bond that is delivered (negative direction) 5. Restructuring clause in a CDS contract may lead to a payoff when there is no default (positive direction) 6. LIBOR is greater than the risk-free rate assumed (positive direction) Estimating Default Probabilities from Credit Spreads * Average hazard rate between time zero and time t * s(t) = credit spread, t = maturity, R = recovery rate * s = 240bps, R = 0. 40, hazard rate = 0. 04 = 4% Real World vs Risk-Neutral Default Probabilities * Real world = backed out of historical data Risk-neutral = backed out of bond prices or credit default swap spreads * Produce very different results. Why? * Corporate bonds are relatively illiquid * Subjective default probabilities of bond traders may be much higher than the estimates from Moody’s historical data * Bonds do not default independently of each other. This leads to systematic risk that cannot be diversified away. * Bond returns are highly skewed with limited upside. The non-systematic risk is difficult to diversify away and may be priced by the market. * Use real world for calculating credit VaR and scenario analysis. Use risk-neutral for valuing for credit derivatives and PV of cost of default Option Models * Based on the idea that equity prices can provide more up-to-date information for estimating default probabilities. * Employ option pricing methods e. g. KMV. * Used by many of the largest banks to monitor credit risk. Merton’s Model * 1974 – company’s equity is an option on the assets of the company. * Equity value at time T as max(VT – D, 0) * VT is value of the firm * D is the debt repayment required * Option pricing model enables value of a firm’s equity today to be related to the value of its assets today and the volatility of its assets.

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Volatilities * Equation together with the option pricing relationship enables value and volatility of assets to be determined from value and volatility of equity. Example * Company equity = $3m * Volatility of equity = 80% * Risk-free rate is 5% * Debt = $10m * Time to debt maturity = 1 year * Value of assets = $12. 40m * Volatility of assets = 21. 23% * Probability of default is 12. 7% * Market value of debt = $9. 40m * PV of payment is 9. 51 * Expected loss 1. 2% * Recovery rate 91% Use of Merton’s Model to estimate real-world default probability (e. g. Moody’s KMV) * Choose time horizon Calculate cumulative obligations to time horizon (D) * Use Merton’s model to calculate a theoretical probability of default * Use historical data to develop a one-to-one mapping of theoretical probability into real-world probability of default. * Distance to default TOPIC 4B: Credit Value at Risk Background * Credit risk is the risk of loss over a certain time period that will not be exceeded with a certain confidence level. * Calculate credit risk to determine both regulatory capital and economic capital. * Time horizon for credit risk VaR is often longer than that for market risk. Market risk usually one-day time horizon and then scaled up to 10 days for the calculation of regulatory capital. * Credit risk VaR, for instruments that are not held for trading, is usually calculated with a one-year time horizon/ * Historical simulation is the main tool used to calculate market risk VaR, but a more elaborate model is usually necessary to calculate credit risk VaR. * Key aspect is credit correlation. Defaults (or downgrades or credit spread changes) for different companies do not happen independently of each other. * Credit correlation increases risks for a financial institution with a portfolio of credit exposures.
Introduction * Internal economic capital allocations against credit risk are based on bank’s estimate of their portfolio’s probability density function of credit losses. * Probability of credit losses exceeding some level, say X, is equal to the shaded area under the PDF. * A risky portfolio is one whose PDF has a relatively long, fat tail i. e. where there is a significant likelihood that actual losses will be substantially larger than expected losses. * Target insolvency rate = shaded area under PDF to right of X * Allocated economic capital = X – expected credit losses Expected vs Unexpected Credit Loss Expected = amount of credit loss expected on credit portfolio over the chosen time horizon * Unexpected = amount by which actual credit losses exceed expected credit loss. Economic Capital Allocation * Economic capital = estimated capital required to support credit risk exposure. * Process is similar to VaR methods used for allocation of capital for market risk. * Probability of unexpected credit loss exhausting economic capital is less than the bank’s target insolvency rate. * Target insolvency rate usually consistent with desired credit rating. * “AA” rating implies a 0. 3% chance of default. Need enough economic capital to be 99. 97% certain that credit losses will not cause insolvency. * Based on two inputs: 1. Bank’s target insolvency rate 2. Bank’s estimated PDF for portfolio credit losses * Two banks with identical portfolios could have very different economic capital for credit risk, owing to: 1. Differences in attitudes to risk taking (reflected in target insolvency rates) 2. Differences in methods of estimating PDFs (reflected in credit risk models) Measuring Credit Losses * Credit loss = current value –future value at the end of some time horizon. Precise definition of current/future values contingent on specific credit loss paradigm. * Current generation of credit risk models employ either of two conceptual paradigms: 1. Default-Mode (DM) Paradigm * Most common. * Credit loss arises only if default occurs within the time horizon. * “Two-state” model – only two outcomes, default and non-default. * If borrower defaults, credit loss = bank’s credit exposure – present value of future net recoveries (cash payments less workout expenses). * Current values are known but future values are uncertain. Estimate joint probability distribution with respect to 3 types of random variables: 1. Associated credit exposure 2. Indicator denoting whether facility defaults during planning horizon 3. In the event of default, the loss given default (LGD). Unexpected losses approach: * Assumption that PDF is well-approximated by mean and standard deviation. * Set capital at some multiple of estimated standard deviation of losses. * Requires estimates of expected and unexpected credit loss from default. * Expected loss (? ) depends on 3 key components: 1. LGD = loss given default, expressed as a decimal . PD = probability of default 3. EAD = expect credit exposure at default. * Standard deviation of portfolio credit losses * i = stand-alone standard deviation of credit losses from ith facility; * i = correlation between credit losses from ith facility and those on the overall portfolio; 2. Mark-to-Market (MTM) Paradigm * Credit loss can arise in response to decline in credit risk quality. * “Multi-state” model: default is only one of several possible credit ratings a loan could ‘migrate’ to over the horizon. * Credit portfolio marked to market at the beginning and end of planning horizon. Likelihood of a customer migrating from its current risk rating to any other category within the planning horizon is typically expressed in terms of a rating transition matrix. Row = current rating Column = prob of migrating to another risk grade * Complex estimation – need to estimate credit risk migrations at end of horizon as well as future credit spreads (risk-premium associated with end-of-period credit rating). * Two approaches: 1. Discounted contractual cash flow (DCCF) approach 2. Risk-neutral valuation (RNV) approach: an option valuation framework. In each methodology, a loan’s value is constructed as a discounted PV of its future cash flows. * Approaches differ mainly in how discount factors and yield spreads are estimated or calculated. TOPIC 5: OPERATIONAL RISK Overview * Definition: the risk of loss resulting from inadequate of failed internal processes, people and systems or from external events. * Harder to quantify and manage operational risk than credit or market risk. * FIs make a conscious decision to take a certain amount of credit and market risk but operational risk is a necessary part of doing business. Operational risk has become a more significant issue as a result of: * Increased use of highly automated technology and sophisticated systems * Growth of e-commerce * New wave of M&A * Increased risk mitigation techniques that may produce other risks * Increased prevalence of outsourcing * Over 100 operational loss events exceeding USD 100m since the end of the 1980s: * Internal fraud * External fraud * Employment practices and workplace safety * Clients, products and business practices * Damage to physical assets * Business disruption and system failures Execution, delivery and process management Regulatory Capital for Operational Risk * Three methods which represent a continuum of approaches characterised by increasing sophistication and risk sensitivity: 1. Basic Indicator Approach (15% of gross income) 2. Standardised Approach (different % for each business line) 3. Advanced Measurement Approach 1. Basic Indicator Approach * KBIA=GI ? ? GI = average annual gross income (net interest income + non-interest income) ? = 15% 2. Standardised Approach Bank activities divided into 8 business lines.
Capital charge for each line is calculated by multiplying its gross income by the denoted beta. Total capital charge: KTSA= (GI1-8 ? ?1-8) To qualify for use of this approach, a bank must satisfy, at a minimum: – Its board of directors and senior management, as appropriate, are actively involved in the oversight of the operational risk management framework – It has an operational risk management system that is conceptually sound and implemented with integrity. – It has sufficient resources in the use of the approach in the major business lines as well as the control and audit areas. 3.
Advanced Measurement Approach (AMA) * Regulatory capital requirement is determined using the quantitative and qualitative criteria for the AMA. * Banks can only use this approach if their local regulators/supervisory authorities have provided approval. * Qualitative Standards 1. Bank must have independent operational risk management function that is responsible for the design and implementation of banks’ operational risk management framework. 2. Bank’s internal operational risk measurement system must be closely integrated into the day-to-day risk management processes of the bank. 3.
There must be regular reporting of operational risk exposures and loss experience to business unit management, senior management, and to the board of directors. 4. Bank’s operational risk management system must be well documented. 5. Internal and/or external auditors must perform regular reviews of the operational risk management processes & measurement systems. * Quantitative Standards 1. Banks must demonstrate that its approach captures potentially severe tail loss events. 2. Required to calculate regulatory capital requirement as the sum of expected loss (EL) and unexpected loss (UL) 3.
Must be sufficiently ‘granular’ to capture the major drivers of operational risk. 4. Operational risk measurement system must include the use of internal data, relevant external data, scenario analysis and factors reflecting the business environment and internal control systems. Distributions important in estimating potential operational risk losses: 1. Loss frequency distribution * Distribution of number of losses observed during the time horizon (usually 1 year). * Loss frequency should be estimated from the banks own data as far as possible. One possibility is to assume a Poisson distribution: only need to estimate an average loss frequency. 2. Loss severity distribution * Distribution of the size of a loss given that a loss has occurred. * Based on both internal and external historical data. * Lognormal probability distribution is often used: only need to estimate mean and SD. AMA * The two distributions above are combined for each loss type and business line to determine the total loss distribution. * Monte Carlo simulation can be used to combine the two distributions. Four elements specified by the Basel Committee 1. Internal Data Operational risk losses have not been recorded as well as credit risk losses * Important losses are low-frequency high-severity losses * Loss frequency should be estimated from internal data 2. External Data * Data sharing or data vendors * Data from vendors: * Based on publicly available information biased towards large losses * Only be used to estimate the relative size of the mean losses and SD of losses for different risk categories. 3. Scenario Analysis * Aim is to generate scenarios covering all low frequency high severity losses * Can be based on both internal and external experience Aggregate scenarios to generate loss distributions 4. Business Environment and Internal Control Factors * Takes account of: * Complexity of business line * Technology used * Pace of change * Level of supervision * Staff turnover rates Power Law * Prob (v > x) = Kx-a * Power law holds well for the large losses experienced by banks. * When loss distributions are aggregated, the distribution with the heaviest tails tends to dominate. This means that the loss with the lowest alpha defines the extreme tails of the total loss distribution. Insurance * Important decision re operational risk is the extent to which it should be insured against.
Moral Hazard * Risk that the existence of the insurance contract will cause the bank to behave differently than it otherwise would. * Example: a bank insures itself against robberies. As a result of the insurance policy, it may be tempted to be lax in its implementation of security measures – making a robbery more likely than it would otherwise have been. * Solution * Deductible – bank is responsible for bearing the first part of any loss * Coinsurance provision – insurance company pays a predetermined percentage of losses in excess of the deductible. * Policy limit – on total liability of the insurer.
Adverse Selection * This is where an insurance company cannot distinguish between good and bad risks. * To overcome this, an insurance company must try to understand the controls that exist within banks and the losses that have been experienced. Sarbanes-Oxley * Sarbanes-Oxley Act passed in the US in 2002. * Requires board of directors to become much more involved with day-to-day operations. They must monitor internal controls to ensure risks are being assessed and handled well. * Gives the SEC the power to censure the board or give it additional responsibilities. A company’s auditors are not allowed to carry out any significant non-auditing services. * Audit committee of the board must be made aware of alternative accounting treatments. * CEO and CFO must return bonuses in the event that financial statements are restated. TOPIC 6: LIQUIDITY RISK Overview * Liquidity refers to the ability to make cash payments as they become due. * Solvency refers to having more assets than liabilities, so that equity value is positive. Types of Liquidity Risk * Liquidity trading risk – markets can become illiquid very quickly.
Cannot unwind asset position at a fair price fire sale prices. * Liquidity funding risk – risk of being unable to service cash flow obligations. Liquidity needs are uncertain. Liquidity Trading Risk * Price received for an asset depends on: * The mid market price * How much is to be sold * How quickly it is to be sold * The economic environment Bid-Offer Spread as a Function of Quantity * Dollar bid – offer spread, p = Offer price – Bid price * There is a spread which is constant up to some quantity. After a critical level (size limit of market makers), the spread widens.
Proportional bid-offer spread= Offer price-bid priceMid-market price * Cost of liquidation in normal markets i=1n12si? i * N is the number of positions, alpha is the position of the instrument, s is the proportional bid-offer spread for the instrument. * Spread widens if market is in stressed conditions. * Cost of liquidation in stressed markets i=1n12(? i+ ?? i)? i * Mean and SD, lambda is required confidence level Liquidity Adjusted VaRLiquidity-Adjusted Stressed VaR VaR+i=1n12si? i VaR+i=1n12(? i+ ?? i)? i Unwinding a Position Optimally (Two Options) Unwind quickly: trader will face large bid-offer spreads, but the potential loss from the mid-market price moving against the trader is small. * Unwind over several days: bid-offer spread each day will be lower, but the potential loss from the mid-market price moving against the trader is larger. Liquidity Funding Risk * Sources of liquidity * Liquid assets * Ability of liquidate trading positions (funding risk and trading risk are interrelated) * Wholesale and retail deposits * Lines of credit and the ability to borrow at short notice * Securitisation * Central bank borrowing (lender of last resort) Basel III Regulation * Liquidity Coverage Ratio: designed to make sure that the bank can survive a 30 day period of acute stress * Net Stable Funding Ratio: a longer term measure designed to ensure that stability of funding sources is consistent with the permanence of the assets that have to be funded. Liquidity Black Holes * Occurs when most market participants want to take one side of the market and liquidity dries up. Positive and Negative Feedback Trading * Exacerbates the direction of price movements * Positive feedback trader buys after a price increase and sells after a price decrease. Negative feedback trader buys after a price decrease and sells after a price increase. * Positive feedback trading can create or accentuate a black hole. Reasons for Positive Feedback Trading * Computer models incorporating stop-loss trading. Stop-loss trading = discarding position to prevent further losses. * Dynamic hedging a short option position. Example: if you have “sold an option” – cover yourself by going long i. e. buy underlying asset when price rises and sell when price decreases. * Creating a long option position synthetically * Margin calls The Leveraging CycleThe Deleveraging Cycle
Is Liquidity Improving? * Spreads are narrowing but arguably the risks of liquidity black holes are now greater than they used to be. * We need more diversity in financial markets where different groups of investors are acting independently of each other. Principles for Sound Liquidity Risk Management and Supervision (June 2008) * GFC regulators responded by undertaking a fundamental review of existing guidance of liquidity management and issued a revised set of principles on how banks should manage liquidity. Fundamental Principle for the Management and Supervision of Liquidity Risk 1.
Sound management of liquidity risk – robust risk management framework. Governance of Liquidity Risk Management 2. Clearly articulate a liquidity risk tolerance 3. Strategy, policies and practices to manage liquidity risk 4. Incorporate liquidity costs, benefits and risks for all significant business activities. Measurement and Management of Liquidity Risk 5. Framework for comprehensively projecting cash flows arising from assets, liabilities and OBS items. 6. Actively monitor and control liquidity risk exposures and funding needs within and across legal entities. 7.
Establish a funding strategy that provides effective diversification. 8. Effectively manage intraday liquidity positions and risks to meet payment and settlement obligations. 9. Actively manage collateral positions. 10. Conduct stress tests on a regular basis. 11. Formal contingency funding plan (CFP) in case of emergency. 12. Maintain a cushion of unencumbered, high quality liquid assets in case of stress scenarios. Public Disclosure 13. Publicly disclose information on a regular basis The Role of Supervisors 14. Regularly perform a comprehensive assessment of a bank’s overall liquidity risk management framework. 15.
Supplement point 14 by monitoring a combination of internal reports, prudential reports and market information. 16. Should intervene to require effective and timely remedial action to address liquidity deficiencies. 17. Should communicate with other regulators e. g. central banks – cooperation TOPIC 7: CORE PRINCIPLES OF EFFECTIVE BANKING SUPERVISION Overview * Most important global standard for prudential regulation and supervision. * Endorsed by vast majority of countries. * Provides benchmark against which supervisory regimes can be assessed. * 1995: Mexican and Barings Crises Lyon Summit in 1996 for G7 Leaders. 1997: Document drafted and endorsed at G7 meeting. Final version presented at annual meetings of World Bank and IMF in Hong Kong. * 1998: G-22 endorsed * 2006: Revision of the Core Principles * 2011: Basel Committee mandates a major review, issues revised consultative paper. The Core Principles (2006) * 25 minimum requirements that need to be met for an effective regulatory system. * May need to be supplemented by other measures. * Seven major groups * Framework for supervisory authority – Principle 1 * Licensing and structure – Principles 2-5 * Prudential regulations and requirements – Principles 6-18 *
Methods of ongoing banking supervision – Principles 19-21 * Accounting and disclosure – Principle 22 * Corrective and remedial powers of supervisors – Principle 23 * Consolidated and cross-border banking – Principles 24-25. * Explicitly recognise: * Effective banking supervision is essential for a strong economic environment. * Supervision seeks to ensure banks operate in a safe and sound manner and hold sufficient capital and reserves. * Strong and effective supervision is a public good and critical to financial stability. * While cost of supervision is high, the cost of poor supervision is even higher. Key objective of banking supervision: * Maintain stability and confidence in the financial system * Encourage good corporate governance and enhance market transparency Revised Core Principles (2011) * Core Principles and assessment methodology merged into a single document. * Number of core principles increased to 29. * Takes account of several key trends and developments: * Need to deal with systemically important banks * Macroprudential focus (system-wide) and systemic risk * Effective crisis management, recovery and resolution measures. Sound corporate governance * Greater public disclosure and transparency enhance market discipline. * Two broad groups: 1. Supervisory powers, responsibilities and functions. Focus on effective risk-based supervision, and the need for early intervention and timely supervisory actions. Principles 1-13. 2. Prudential regulations and requirements. Cover supervisory expectations of banks, emphasising the importance of good corporate governance and risk management, as well as compliance with supervisory standards. Supervisory powers, responsibilities and functions 1.
Clear responsibilities and objectives for each authority involved. Suitable legal framework. 2. Supervisor has operational independence, transparent processes, sound governance and adequate resources, and is accountable. 3. Cooperation and collaboration with domestic authorities and foreign supervisors. 4. Permissible activities of banks is controlled. 5. Assessment of bank ownership structure and governance. 6. Power to review, reject and impose prudential conditions on any changes in ownership or controlling interests. 7. Power to approve or reject major acquisitions. 8.
Forward-looking assessment of the risk profile of banks and banking groups. 9. Uses appropriate range of techniques and tools to implement supervisory approach. 10. Collects, reviews and analyses prudential reports and statistical returns. 11. Early address of unsafe and unsound practices. 12. Supervises banking group on consolidated basis (including globally) 13. Cross-border sharing of information and cooperation. Prudential regulations and requirements 14. Robust corporate governance policies and processes. 15. Banks have a comprehensive risk management process, including recovery plans. 6. Set prudent and appropriate capital adequacy requirements. 17. Banks have an adequate credit risk management process. 18. Banks have adequate policies and processes for the early identification and management of problems assets, and maintain adequate provisions and reserves. 19. Banks have adequate policies re concentration risk. 20. Banks required to enter into any transactions with related parties on an arm’s length basis. 21. Banks have adequate policies re country and transfer risk. 22. Banks have an adequate market risk management process. 23.
Banks have adequate systems re interest rate risk in the banking book. 24. Set prudent and appropriate liquidity requirements. 25. Banks have an adequate operational risk management framework. 26. Banks have adequate internal controls to establish and maintain a properly controlled operating environment for the conduct of their business. E. g. delegating authority and responsibility, separation of the functions that involve committing the bank. 27. Banks maintain adequate and reliable records, prepare financial statements in accordance with accounting policies etc. 8. Banks regularly publish information on a consolidated and solo basis. 29. Banks have adequate policies and processes e. g. strict customer due diligence. Preconditions for Effective Banking Supervision 1. Provision of sound and sustainable macroeconomic policies. 2. A well established framework for financial stability policy formulation. 3. A well developed public infrastructure 4. A clear framework for crisis management, recovery and resolution 5. An appropriate level of systemic protection (or public safety net) 6. Effective market discipline 001: IMF and World Bank Study on Countries’ Compliance with Core Principles * 32 countries are compliant with 10 or few BCPs * Only 5 countries were assessed as fully compliant with 25 or more of the BCPs. * Developing countries less compliant than advanced economies. * Advanced economies generally possess more robust internal frameworks as defined by the ‘preconditions’ 2008: IMF Study on BCP Compliance * Based on 136 compliance assessments. * Continued work needed on strengthening banking supervision in many jurisdictions, particularly in the area of risk management. More than 40% of countries did not comply with the essential criteria of principles dealing with risk management, consolidated supervision and the abuse of financial services. * More than 30% did not possess the necessary operational independence to perform effective supervision nor have adequate ability to use their formal powers to take corrective action. * On average, countries in Western Europe demonstrated a much higher degree of compliance (above 90%) with BCP than their counterparts in other regions. * Africa and Western Hemisphere weak. Generally, high-income countries reflected a higher degree of compliance. TOPIC 8: CAPITAL ADEQUACY Overview * Adequate capital better able to withstand losses, provide credit through the business cycle and help promote public confidence in banking system. Importance of Capital Adequacy * Absorb unanticipated losses and preserve confidence in the FI * Protect uninsured depositors and other stakeholders * Protect FI insurance funds and taxpayers * Protect deposit insurance owners against increases in insurance premiums * To acquire real investments in order to provide financial services e. . equity financing is very important. Capital Adequacy * Capital too low banks may be unable to absorb high level of losses. * Capital too high banks may not be able to make the most efficient use of their resources. Constraint on credit availability. Pre-1988 * Banks regulated using balance sheet measures e. g. ratio of capital to assets. * Variations between countries re definitions, required ratios and enforcement of regulations. * 1980s: bank leverage increased, OBS derivatives trading increased. * LDC debt = major problem 1988 Basel Capital Accord (Basel I) * G10 agreed to Basel I Only covered credit risk * Capital / risk-adjusted assets > 8% * Tier 1 capital = shareholders equity and retained earnings * Tier 2 capital = additional internal and external resources e. g. loan loss reserves * Tier 1 capital / risk-adjusted assets > 4% * On-balance-sheet assets assigned to one of four categories * 0% – cash and government bonds * 20% – claims on OECD banks * 50% – residential mortgages * 100% – corporate loans, corporate bonds * Off-balance-sheet assets divided into contingent or guarantee contracts and FX/IR forward, futures, option and swap contracts. Two step process (i) derive credit equivalent amounts as product of FV and conversion factor then (ii) multiply amount by risk weight. * OBS market contracts or derivative instruments = potential exposure + current exposure. * Potential exposure: credit risk if counterparty defaults in the future. * Current exposure: cost of replacing a derivative securities contract at today’s prices. 1996 Amendment * Implemented in 1998 * Requires banks to measure and hold capital for market risk. * k is a multiplicative factor chosen by regulators (at least 3) VaR is the 99% 10-day value at risk SRC is the specific risk charge Total Capital = 0. 08 x [Credit risk RWA + Market risk RWA] where market risk RWA = 12. 5 x [k x VaR + SRC] Basel II (2004) * Implemented in 2007 * Three pillars 1. New minimum capital requirements for credit and operational risk 2. Supervisory review: more thorough and uniform 3. Market discipline: more disclosure * Only applied to large international banks in US * Implemented by securities companies as well as banks in EU Pillar 1: Minimum Capital Requirements * Credit risk measurement: * Standardised approach (external credit rating based risk weights) * Internal rating based (IRB) Market risk = unchanged * Operational risk: * Basic indicator: 15% of gross income * Standardised: multiplicative factor for income arising from each business line. * Advanced measurement approaches: assess 99. 9% worst case loss over one year. * Total capital = 0. 08 x [Credit risk RWA + market risk RWA + Operational risk RWA] Pillar 2: Supervisory Review * Importance of effective supervisory review of banks’ internal assessments of their overall risks. Pillar 3: Market discipline * Increasing transparency – public disclosure Basel 2. 5 (Implemented 2011) * Stressed VaR for market risk * Incremental risk charge Ensures products such as bonds and derivatives in the trading book have the same capital requirement that they would if they were in the banking book. * Comprehensive risk measure (re credit default correlations) Basel III (2010) * Considerably increase quality and quantity of banks capital * Macroprudential overlay – systemic risk * Allows time for smooth transition to new regime * Core capital only retained earnings and common shares * Reserves increased from 2% to 4. 5% * Capital conservation buffer – 2. 5% of RWA * Countercyclical capital buffer * Tracing/monitoring of liquidity funding Introduction of a maximum leverage ratio Capital Definitions and Requirements * Common equity > 4. 5% of RWA * Tier 1 > 6% of RWA * Phased implementation of capital levels stretching to Jan 1, 2015 * Phased implementation of capital definition stretching to Jan 1, 2018 Microprudential Features * Greater focus on common equity * Loss-absorbing during stress/crisis period capital conservation buffer * Promoting integrated management of market and counterparty credit risk. * Liquidity standard introduced introduced Jan 1, 2015 Introduced Jan 1, 2018 Available Stable Funding Factors
Required Stable Funding Factors Macroprudential Factors * Countercyclical buffer * Acts as a brake in good times of high credit growth and a decompressor to restrict credit during downturns. * Within a range of 0-2. 5% * Left to the discretion of national regulators * Dividends restricted when capital is below required level * Phased in between Jan 1, 2016 – Jan 1, 2019 * Leverage Ratio * Target 3% * Ratio of Tier 1 capital to total exposure > 3% * Introduced on Jan 1, 2018 after a transition period * SIFIs * Required to hold additional loss absorbency capital, ranging from 1-2. 5% in common equity