Articles by kenli

LO 51.4: Assess the role of repo transactions in the collapses of Lehman Brothers and Bear Stearns during the (20072009) credit crisis.
Prior to the 20072009 credit crisis, the repo market was considered relatively liquid with stable demand by both borrowers and lenders. Borrowers often posted weaker quality collateral, including corporate bonds or mortgage-backed securities. This benefited both borrowers, who were able to post less desirable collateral, and lenders, who were able to obtain higher repo rates in exchange for accepting lower quality collateral. However, as the crisis escalated, lenders were reluctant to continue to accept these securities, and were increasingly demanding higher quality collateral and larger haircuts. At the extreme, they simply withdrew liquidity and stopped transacting in the markets. Borrowers that were the worst hit experienced collateral liquidations, capital declines, and ultimately bankruptcies. The case studies of Lehman Brothers and Bear Stearns provide important insights into the role of repo transactions in the demise of these once important institutions.
Repos and Lehman Brothers
JPMorgan Chase & Co. (JPM) was the tri-party repo clearing agent of Lehman Brothers Holdings, Inc. (Lehman). (In a tri-party repo agency arrangement, the repo trades are still executed between two counterparties; however, the collateral selection, payment, settlement, and repo management is outsourced to a third-party agent. Agents are essentially custodians and do not take on the risks of the transactions.) These tri-party repos were traded in the overnight market, and were transacted predominantly between institutional repo lenders and financial institution borrowers (including Lehman). Given that the trades were overnight transactions, they matured each morning, leaving the borrowers without funding during the rest of the day. To bridge this funding gap, JPM, as tri-party agent, was lending directly to Lehman on a secured basis during the day, typically without requiring haircuts on intraday advances. By August 2008, however, due to the increased risk in the repo markets, JPM began to phase in haircuts on intraday loans, with the loan amounts exceeding $ 100 billion in the final week of Lehmans bankruptcy.
Professors Note: Lehman was one o f the largest U.S. investm ent banks. The failure o f Lehman in September 2008 was the largest in U.S. history ($600 billion in assets).
Both Lehman and JPM provide different viewpoints of the events leading up to Lehmans bankruptcy in September 2008. Despite the differing accounts, it is clear that the liquidity and value of collateral pledged in repo transactions declined during the crisis, and additional collateral and additional haircuts were necessary to mitigate the risks in repos.
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According to Lehman, JPM, despite a conflict of interest due to its agent and lender role, breached its duty to Lehman and took advantage of its insider status (being insider to Lehmans internal financial condition and proposed business plans). Lehman accused JPM of using its influence to drain close to $14 billion in collateral from Lehman during the last few days before the bankruptcy, despite already being in an overcollateralized position. Although Lehman agreed at the time to provide additional collateral, it did so unwillingly and simply because there were no viable alternatives.
According to JPM, however, JPM acted in good faith by providing continued funding to Lehman up until the last day, despite Lehmans deteriorating financial condition. When it became clear that the collateral posted to JPM by Lehman was illiquid with apparently overstated values, JPMs exposure to Lehman was growing at a time when Lehmans creditworthiness and financial condition was deteriorating. Nevertheless, JPM continued to lend money despite inadequate haircuts and collateral values. The close to $14 billion in additional collateral requested by JPM was significantly less than what was needed to cover JPMs true exposure.
Repos and Bear Steams
Prior to 2007, Bear Stearns Companies, Inc., (Bear Stearns) relied on funding its borrowings primarily in the form of short-term unsecured commercial paper. By 2007, however, Bear Stearns switched from unsecured borrowing to a more stable form of borrowing through longer term, secured repo financing, which better positioned the firm to withstand market liquidity events. Given the high-quality collateral posted, the firm was able to obtain financing at favorable rates on a term basis.
Given the events of 20072009, lenders during this period became increasingly less willing to provide loans in the form of repo trades, and were especially averse to providing term (rather than overnight) repos. This led to a general shortening of repo terms, requiring larger haircuts, and requesting borrowers to post higher quality collateral. In early March 2008, Bear Stearns experienced a run on the bank that resulted from a general loss of confidence in the firm. This bank run led to a massive withdrawal of cash and unencumbered assets (i.e., assets that have not been committed or posted as collateral), and lenders refused to roll over their repo trades. The rapid decline in market confidence and withdrawal of capital ultimately led to Bear Stearns collapse.
Professors Note: Bear Stearns was a U.S. investm ent bank and brokerage firm that was bailed out by the Federal Reserve Bank o f New York and subsequently sold to JPM in M arch 2008.
C o l
l a t e r a l i n R e p o T r a n s a c t i o n s

LO 51.3: Explain how counterparty risk and liquidity risk can arise through the use of repo transactions.
Repo transactions involve the exchange of cash as well as the exchange of collateral. As a result, both counterparty risk (credit risk) and liquidity risk are present.
Counterparty risk is the risk of borrower default or non-payment of its obligations, and it arises because the lender is exposed to the risk of a failure by the borrower to repay the repo loan and interest. Given, however, that repo loans are secured by collateral, this makes the lender much less vulnerable to a decline in the creditworthiness of the borrower. The lender can recover any amounts owed by simply selling the collateral. As a result, because repos are generally very short-term transactions secured by collateral, counterparty (credit) risk is less of a concern.
Liquidity risk is the risk of an adverse change in the value of the collateral and can be of particular concern to the lender. Even if the lender is less concerned with the credit risk of a counterparty given the security of collateral, the lender is still exposed to the risk of collateral illiquidity and to the value of the collateral declining during the repo term. Especially during times of market turbulence (as we will see in next LO), the value of
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collateral can decline significantly and its liquidity can dry up. This risk can be mitigated with the use of haircuts, margin calls, reducing the term of the repo, and accepting only higher quality collateral.
R e p o s D u r i n g t h e C r e d i t C r i s i s

LO 31.2: Explain common motivations for entering into repos, in cash management and liquidity management.
including their use
B o r r o w e r s i n R e p o s
>From the perspective of the borrower, repos offer relatively cheap sources of obtaining short term funds. Relative to unsecured borrowing, repos allow the borrower to obtain funds at favorable rates because lenders are willing to accept lower returns (relative to unsecured transactions) in favor of the security of collateral.
Bond Financing
Repos can also be used to obtain cash to finance a long security position. Consider a financial institution in the previous example (as counterparty A) that just purchased the same $10 million face value ABC bond from a client in hopes of selling it to another investor for a profit. Until the new buyer is found, however, the financial institution needs to finance the purchase of the bond. It can do so by borrowing cash through an overnight repo trade (from counterparty B) and pledging the ABC bond as collateral, subject to any applicable haircuts. If the financial institution cannot immediately find a buyer, it needs to roll/renew its position. If the initial repo trade and the subsequent rolls are transacted with the same counterparty, the trade flow is similar to Figure 1.
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If the repo is renewed/rolled with a different counterparty, the financial institution first needs to unwind the initial trade (with counterparty B) and then enter into a new repo trade with another counterparty (counterparty C). This is illustrated in Figure 3.
Similar to financing a bond purchase, the financial institution may also use repos to finance proprietary positions or to finance its inventory in order to make markets.
Figure 3: Back-to-Back Repo Trades
$ 10 million face value ABC bond
$ 10 million face value ABC bond
Counterparty C
From the perspective of the lender, repos can be used for either investing or for financing purposes as part of an entitys cash management or financing strategies.
Cash Management (Repos as Investment Vehicles)
Lenders use repos (taking the reverse repo side) for investing when they hold cash either for liquidity or safekeeping reasons and need short-term investing opportunities to generate return on their surplus cash position. For example, money market mutual funds hold cash for safekeeping on behalf of investors and therefore need low risk, short maturity investments to generate return rather than holding idle cash. Municipalities, on the other hand, have significant surplus cash generated from tax revenues. Municipalities are prohibited from investing in high-risk investments, and repos offer a low risk, collateral- secured investment opportunity.
Investors look for liquidity and tend to favor very short-term positions in overnight repos, which provide significant flexibility to the investor. Following each overnight repo
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transaction, the investor could re-evaluate its decision whether to continue lending cash. Investors may also transact in open repos by lending for a day under a contract that renews each day until it is canceled. Repos could have longer maturities out to several months, although typically the longer the maturity, the lower the overall demand.
In addition to liquidity, investors also prefer higher quality collateral. Repo collateral is generally limited to high-quality securities, including securities issued or guaranteed by governments and government-sponsored entities. Because the lender is faced with the risk of a decline in collateral value during the term of the repo transaction, repo agreements often require collateral haircuts. A haircut refers to discounting the value of the collateral posted in relation to its risk. In our earlier repo trade example, counterparty B may only lend $10.5 million against the $11 million market value of the ABC bond collateral received. Finally, repo transactions are also subject to margining and (daily) margin calls. A margin call requires a borrower to post additional collateral in a declining market, but it also allows the borrower to withdraw excess collateral in up markets.
Short Position Financing (Repos as Financing Vehicles)
Lenders may also use repos (as the reverse repo side) to finance short positions in bonds. Consider an investment management firm that has a view that interest rates will rise and bond prices will fall. It can take advantage of this view by obtaining the desired bond collateral through lending cash in a reverse repo trade. It would then short sell the bond received through the reverse repo and buy it back at the market price at a later date, hoping to benefit from the trade from a fall in prices. The transaction flows would be similar to what we previously illustrated in Figure 1 and Figure 2, with the investment management firm as counterparty B.
C o u n t e r p a r t y R i s k a n d L i q u i d i t y R i s k

LO 51.1: Describe the mechanics of repurchase agreements (repos) and calculate the settlement for a repo transaction.
Economically, a repurchase agreement (i.e., repo) is a short-term loan secured by collateral. Mechanically, it is a contract between two parties where one party sells a security at a specified price with a commitment to buy back the security at a future date at another specified (higher) price. The difference between the sell and buy prices of the security is the implied interest (i.e., return) on the transaction. Repos are used by both borrowers needing short-term funds and by lenders needing short-term investments or access to hard-to-find collateral.
The term repo refers to the transaction from the borrowers side; that is, from the side that sold the security with a promise to buy it back. When we examine the same transaction from the lender’s side, the transaction is referred to as a reverse repurchase agreement (i.e., reverse repo). Figures 1 and 2 illustrate an example of a repo trade.
Figure 1: Repo Initiation
$10 million face value ABC bond
Counterparty A
Counterparty B
$ 11,000,000
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Suppose that on May 1, counterparty A wishes to borrow $11 million for 31 days. It therefore sells ABC bonds with a face value of $10 million and a market value of $11 million to counterparty B, with a contract price of $11 million to reflect the bonds market value. Concurrently, counterparty A agrees to buy back the bond in 31 days at the contract price plus 0.3% interest (30 basis points).
Professors Note: Interest rates fo r repos are always quoted at an annualized rate, and the convention fo r most money market securities is to use an actual/360 day count.
The repurchase price in this example is computed as follows:
/
$11,000,000 x 1+
\
0.3% x31
360
,
$11,002,841.67
As illustrated in Figure 2, on the June 1 termination of the repo trade, counterparty A will purchase back the $10 million face value ABC bond for $11,002,842.
Figure 2: Repo Termination (Settlement)
Counterparty A
$ 10 million face value ABC bond
<——————- ——————-
$11,002,842
Counterparty B

LO 50.2: Describe practices that can result in a strong and effective capital adequacy process for a BHC in the following areas: Risk identification
Corporate governance Capital policy, including setting of goals and targets and contingency planning Stress testing and stress scenario design Estimating losses, revenues, and expenses, including quantitative and
Assessing the impact of capital adequacy, including risk-weighted asset (KWA)
qualitative methodologies
and balance sheet projections
For this LO, we detail the seven key practices that can result in a strong and effective capital adequacy process for a BHC.
Risk Identification
BHCs should have a process in place to identify all risk exposures stemming from numerous sources, including stress conditions, changing economic and financial environments, on- and-off balance sheet items, and their impact on capital adequacy. In addition, BHCs should critically scrutinize underlying assumptions regarding risk reduction through risk mitigation or risk transfer techniques. Senior management should regularly update and review the risk identification plan with special consideration for how their risk profiles might change under stress scenarios. Risk identification techniques should be able to detect the changes in the overall risk profile as well as the signs of capital inadequacy in the early stages.
BHCs should integrate the identified risk exposures into their internal capital planning processes. Scenario-based stress testing may not capture all potential risks faced by BHCs, some risks are difficult to quantify or they do not fall into the integrated firm wide scenarios. However, such risks must be included and accounted for in the capital planning processes. These risks are categorized as other risks, and their examples include compliance, reputational, and strategic risks. There are a variety of methods which BHCs can employ, including internal capital targets to incorporate such risks.
Internal Controls
An internal audit team should carefully scrutinize the internal control data for accuracy before submitting to senior management and the board. BHCs should have efficiently running management information systems (MIS) for collecting and analyzing pertinent information set quickly and accurately.
In addition, BHCs should put in place a detailed and organized documentation system fully encompassing all dimensions of capital planning processes, including risk identification, loss estimation techniques, capital adequacy, and capital decision processes.
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There must be a thorough, independent, and regular review and validation of all models used for internal capital planning purposes, including assessment of conceptual soundness of models and verification of processes. A validation team should have a required technical skill set as well as complete independence from all business areas of the BHC and model developers. Such independence is crucial for the validation team to offer an unbiased, independent, and valuable verdict.
BHCs should maintain and update a list of all inputs, assumptions, and adjustments for the models used to generate final projections and estimates, such as income, loss expenses, and capital. These models should be validated for their effective use, not only under normal conditions, but also under stress conditions. BHCs should make full disclosure of their validation process and outcome, and should restrict the use of models which are not validated.
Governance
BHCs should have boards with sufficient expertise and involvement to fully understand and evaluate the information provided to them by senior management regarding their capital planning processes. The board should be furnished with comprehensive information with respect to risk exposures, loss estimates, determinants of revenues and losses, underlying models and assumptions, and weaknesses and strengths of capital planning processes. Also, the boards should be informed about the stress scenarios and any corrective measures undertaken as a result of stress testing outcomes.
Under the Capital Plan Rule, the management of BHCs is required to furnish key information to the board for its approval of internal capital adequacy plans. Such information should include underlying assumptions and results of stress testing and the outcome of internal audits, as well as model review and validation checks.
Senior management should evaluate the internal capital plan on an ongoing basis, focusing on key weaknesses, strengths, assumptions, scenarios, estimates, and models. In addition, senior management should make appropriate adjustments and remediation to the capital plan if the review process reveals shortcomings in the plan.
BHCs should maintain detailed minutes of board meetings, describing the issues raised and discussed, as well as the information used and the recommendations made in these meetings.
Capital Policy
A capital policy should clearly define the principles and guidelines for capital goals, issuance, usage, and distributions. The policy should also fully spell out the details of the BHCs capital planning processes, including the decision rules of capital usage and distribution, financing, and other policies. The capital policy should focus on the unique needs and financial situation of BHCs while taking into consideration the supervisory
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expectations. Policies regarding common stock dividends and repurchase agreements should include the following: Key metrics influencing the size, timing, and form of capital distributions. Materials used in making capital distribution decisions.
Specific scenarios that would cause a distribution to be reduced or suspended. Situations that would cause the BHC to consider replacing common equity with other Specific scenarios that would cause a distribution to be reduced or suspended. Situations that would cause the BHC to consider replacing common equity with other forms of capital.
Key roles and responsibilities of individuals or groups for producing reference materials,
making distribution recommendations and decisions, and reviewing analysis.
Capital goals developed by BHCs should be compatible with their risk tolerance, risk profile, regulatory requirements, and expectations of various stakeholders (e.g., shareholders, creditors, supervisors, and rating agencies). BHCs should establish specific goals for both the level and composition of capital under normal as well as stress conditions. Capital targets, which need to be set above the capital goals for capital adequacy under stress conditions, should take into consideration future economic outlooks, stress scenarios, and market conditions.
While setting capital distribution levels, BHCs must take into consideration numerous factors, including future growth plans (including acquisitions) and associated risk, current and future general economic conditions, in particular the impact of macroeconomic and global events during stress conditions, on their capital adequacy. Capital distribution decisions must be connected to capital goals or capital adequacy requirements.
BHCs should develop strong contingency planning offering numerous options to deal with contingency situations as well as their effectiveness under stress conditions. Contingency plans should be based on realistic assumptions and contain futuristic outlooks, rather than overly relying on history. Contingency actions should be feasible and realistic in the sense that they should be easy to implement when or if the contingency warrants. Capital triggers flagging the early warning of capital deterioration should be based on the projected results, regulatory requirements, and the expectations of various stakeholders, including creditors, shareholders, regulators, investors, and counterparties.
Stress Testing and Stress Scenario Design
Scenario design and stress testing should focus on unique situations of BHCs, their asset and liability mix, portfolio composition, business lines, geographical territory, and revenue and loss factors, while taking into consideration the impact of macroeconomic and firm- specific vulnerabilities and risks. That is, the stress test designing should go above and beyond the general guidelines established by the supervisory authority. Also, a BHCs scenario designing and testing should not employ optimistic assumptions benefiting the BHC.
BHCs should employ both an internal model and expert judgment, an outside experts opinion. If only a third-party model is used, it must be tailored to the unique risk profile and business model of a BHC. The designed scenarios should assume a strong strain on the revenue and income of BHCs.
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Stress testing models should be based on multiple variables encompassing all the risk exposures faced by BHCs on a firm-wide basis. For example, BHCs concentrated in a region, business, or industry should include relevant region, business, or industry-related variables. In addition, the scenarios should clearly spell out how they address specific risks faced by BHCs. The description should also provide explanations of how a scenario stresses specific BHC weaknesses and how variables are related to each other.
Estimating Losses, Revenues, and Expenses
Q uantitative an d Q ualitative Basis
BHCs should prefer using internal data to estimate losses, revenues, and expenses. However, in certain situations, it may be more appropriate to use external data. In these instances, it should be ensured that the external data reflects the underlying risk profile of their business lines, and necessary adjustments should be made to data input or output to make the analysis reflect a true picture of the BHCs unique characteristics.
A range of quantitative methods are available to BHCs for estimating losses, revenues, and expenses. Regardless of which method they use, the final outcome should be identification of key risk factors and impact of changing macro and financial conditions under normal and stress conditions on a firm-wide basis.
In addition, BHCs should segment their line of businesses and portfolios utilizing common risk characteristics showing marked differences in past performances. For example, a borrowers risk characteristics can be segmented by criteria such as credit score ranges. However, each risk segment should have sufficient data observations on losses, revenues, and expenses, (and underlying factors impacting losses, revenues, and expenses) in order to generate meaningful model estimates.
Past relationships between losses, revenues, expenses, and underlying driving factors, and their interrelationships may not hold in the future, thus, necessitating employment of sensitivity analysis (to answer what if questions) when using models based on historical underlying interactions.
BHCs sometimes use qualitative methodologies, like expert judgment or management overlay, as a substitute or a complement to quantitative methods. Qualitative techniques should be based on sound assumptions, and an external reviewer should find these approaches logical, reasonable, and clearly spelled out. A sensitivity analysis should be used for a qualitative approach as well. From a supervisory standpoint, BHCs are expected to use conservative assumptions, not favorable to BHCs, for estimating losses, revenues, and expenses under normal and stress conditions.
Loss Estimation M ethods
BHCs should employ loss estimation methods, which offer theoretical soundness and empirical validity. In addition to using general macroeconomic explanatory variables, the loss estimation models should use specific variables exhibiting a direct link to particular exposures and portfolios.
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BHCs should use uniform, reputable methods to aggregate losses across various lines of business and portfolios for firm-wide scenario analysis. They should also use automated processes, without manual intervention or managerial adjustments showing clear linkage from data sources to loss estimation and aggregation. For estimating retail loan losses, BHCs often use internal data, but for wholesale loss estimation, internal data is supplemented with external data. In the case using external data, BHCs should demonstrate that the data reflects their risk exposures, encompassing geographic, industry, and other key dimensions. Risk segmentation should be supported by the data capturing the unique characteristics of each risk pool.
BHCs can use either an economic loss approach (i.e., expected losses) or an accounting- based loss approach (i.e., charge-off and recovery) to estimate credit losses. For the expected loss approach, BHCs should categorize losses into probability of default (PD), loss given default (LGD), or exposure at default (EAD) and then identify the determinants of each component. Long run averages for PDs, LGDs, and EADs should not be used, as these averages reflect economic downturn and upturn periods not necessarily suitable for scenario testing under stress conditions. LGD should be linked to underlying risk factors, such as a fall in the value of collateralized assets under stress conditions, and it should be estimated at some level of segmentation, such as lending product or type of collateral. EADs should be modeled to exhibit variation depending on changes in macroeconomic conditions.
If BHCs are using rating systems as a key input to estimate expected losses under stress (e.g., on their wholesale portfolios), they should recognize the limitations in rating systems and their data and make necessary adjustments.
BHCs should utilize a robust time series with sufficient granularity while employing role- rate models to estimate the rate at which delinquent and non-delinquent accounts in the current quarter are expected to roll over into default or delinquent status in the next quarter.
If using charge-off models (i.e., accounting models), BHCs should include variables which represent the risk characteristics of an underlying portfolio while estimating the statistical relationship between charge-off rates and macroeconomic variables at the portfolio level.
O perational Risk
In order to determine operational risk, many BHCs estimate correlation between operational risk and macroeconomic factors. If they do not discover a statistically significant relationship between the variables, they employ other methods, including scenario analysis utilizing historical data and management input. BHCs should employ a combination of techniques to develop strong loss estimates under stress conditions, including past loss records, future expected events, macro conditions, and firm-specific risks.
BHCs using regression models to estimate loss frequency and loss severity under stress scenarios should provide statistical support for the period chosen for estimation purposes instead of arbitrary and judgmental selection.
A modified loss distribution approach (LDA) is also used by BHCs to estimate value at risk (VaR) to estimate operational risk losses at a chosen confidence interval (e.g., 90% or 95%).
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To generate a strong and effective process, BHCs should offer a sound justification for their choice and perform a sensitivity analysis around the chosen interval.
Some BHCs use scenario analyses in case they encounter model or data limitations in order to incorporate a wide range of risks (which is not possible otherwise due to data or model limitations). In such events, BHCs should provide a rationale for the chosen scenario in their loss estimation process.
M arket Risk an d Counterparty Credit Risk
BHCs, which are involved in trading, are subject to counterparty credit risk from changes in the value of risk exposure and creditworthiness of the counterparty due to changing macroeconomic conditions.
In order to estimate the potential loss resulting from market credit interaction, BHCs use probabilistic approaches (which produce a probability distribution of expected portfolio losses) and deterministic approaches (which yield point estimates of an expected portfolio loss).
BHCs using probabilistic approaches should clearly offer evidence that such methods can yield more severe risk scenarios compared to historical scenarios. BHCs should also explain how they utilize tail loss scenarios to detect and address firm-specific risks.
BHCs using deterministic approaches should demonstrate that they have employed a wide range of scenarios, adequately covering their key risk exposures, including mark-to-market positions in the event of firm-specific or market-wide stress conditions. In addition, BHCs should clearly spell out the underlying assumptions employed in stress testing scenarios for risk measurement purposes and corrective measures to fix the identified deficiencies.
Market shock scenarios do not directly incorporate the default of the counterparty. Some BHCs explicitly incorporate the scenario of default of key counterparties (including key customers) while using some sort of probabilistic approach involving some estimates of the PD, LGD, and EAD of counterparties. This method allows BHCs to focus exclusively on the defaults of counterparties to which BHCs have large risk exposure.
BHCs also use assumptions about risk mitigation in the future. Such assumptions, if used, should be conservative in nature. In stress scenarios, the ability of BHCs to take desired actions may be limited.
PPNR Projection M ethodologies
PPNR is pre-provision net revenue (i.e., net revenue before adjusting for loss provisions). While estimating revenues and expenses over a planning horizon under stressed conditions (the Capital Plan Rule requires forecasts over the next nine quarters), BHCs should not only take into consideration their current situation, but also the possible future paths of business activities and operational environments related to their on- and off-balance sheet risk exposures, underlying assumptions, and assets and liabilities.
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BHCs should also take into consideration the impact of regulatory changes on their performance and ability to achieve their stated targets and goals. Projections should be based on coherent and clearly defined relationships among numerous, relevant variables, such as revenues, expenses, and balance sheet items within a given scenario. For example, assumptions related to origination should be the same for projections related to loans, fees, costs, and losses.
Underlying assumptions for revenues, expenses, and loss estimates should be theoretically and empirically sound, and the central planning group as well as the corporate planning group should be engaged in aggregating projections on an enterprise-wide basis. In the case of limited data, BHCs should employ external data in conjunction with internal data.
Net interest income projections are not isolated projections; rather, they are entrenched with other items of a capital adequacy plan. Balance sheet assumptions should be consistent while projecting net interest income. For example, balance sheet assumptions for projecting net interest income should be the same when estimating loss. Methods employed for projecting net interest income should incorporate ongoing changes in current and projected balance sheet positions.
BHC projections under various scenarios, based on product characteristics (e.g., a change in deposit mix due to increased demand for time deposits), underlying assumptions, and rationale by product should be carefully explained.
BHCs linking loss projections to net interest income projections should clearly establish this link while using modeling approaches, which incorporate the behavioral characteristics of the loan portfolio.
Net interest income projections should be based on methodologies that incorporate discount or premium amortization adjustments for assets not held at par value that would materialize under different scenarios.
New business pricing projections and underlying assumptions, such as constant add-ons to a designated index value, should be compatible with past data, scenario conditions, and BHCs balance sheet projections.
BHCs should project non-interest income in light of stated scenarios and business strategies. Projection methods should fully encompass underlying major risk exposures and characteristics of a specific business line. For example, an asset management group should project non-interest income using various methods, including brokerage as well as money management revenues.
Additionally, BHCs with trading portfolios should establish a clear link between trading revenue projections to trading assets and liabilities and the compatibility of all the elements of stress scenario conditions.
BHCs with off-balance sheet business items should demonstrate the linkage between revenue projections and changes in on- and off-balance sheet items.
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BHCs should not assume perfect correlation between revenues (generated from trading or private equity activity) and broad indices. BHCs should estimate the sensitivity coefficients for changes in revenue as a result of changes in broad index movements.
Furthermore, BHCs holding mortgage servicing rights assets (MSRAs) should carefully design assumptions regarding default, prepayment, and delinquency rates, ensuring that these assumptions are robust and scenario specific. In addition, BHCs that hedge MSRA risk exposure should generate scenario specific assumptions.
For BHCs, projecting volume increases in mortgage loans while ignoring market saturation or other key factors would be an ineffective and weak process, whereas consideration of individual business models, client profiles, and capacity constraint (while projecting mortgage loan volume) would be an effective and strong capital adequacy process.
Macroeconomic relationships should be based on sound theoretical construct and supported by empirical evidence. For example, BHCs may experience a steep decline in credit card fee revenues in a strong recessionary period because of a decline in consumer spending. .Ail example of a weaker practice of a capital planning process is if a BHC does not show a sufficient decline in revenue in stressed conditions despite obvious macro relationships.
In addition, BHCs should utilize a wide set of explanatory variables to develop statistical relationships. BHCs should take into consideration the impact of macroeconomic conditions, such as an economic downturn, on their non-interest expense projections. Non interest expense projections, like all other projections, should be consistent with revenue and balance sheet estimates and should generate the same underlying strategic assumptions. If projections assume that a decline in revenue (e.g., due to an increase in credit collection costs in an economic downturn) can be offset by some mitigating strategies, BHCs should then clearly demonstrate the feasibility of such actions. Mitigation actions should not be supported by past relationships and actions only because future financial, macro, and global environments may not be as favorable to execute such strategies, as was the case in the past.
Estimation methods to project non-interest expense should focus on uncovering determinants (factors) of individual expense items and how sensitive those factors are to changing macro conditions and business strategies.
Assessing the Impact of Capital Adequacy
BHCs should have a well-defined and well-documented process of generating projections with respect to size and composition of on- and off-balance sheet items and risk-weighted assets (RWA) over a stress horizon period.
Projecting balance sheet items, such as changes in assets and funding, directly without consideration of underlying drivers (of such changes), would be a weak practice. BHCs should identify the impact of changes in key factors on changes in asset and liabilities. Projections should take into consideration these vital relationships.
BHCs should incorporate relationships between revenues, expenses, and balance sheet items into their scenario analyses. Projections about losses, revenues, expenses, and on- and
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off-balance sheet items should not be based on favorable underlying assumptions. These assumptions may not stand the trial of uncertain market conditions under stress conditions.
Projections for RWA should be consistent with the projections for risk exposures of on- and off-balance sheet items. All underlying assumptions used for balance sheet and RWA projections should be clearly documented and critically reviewed and validated.
BHCs with a strong process of implementation should form a centralized group responsible for aggregating loss, revenue, expense, on- and off-balance sheets, and RWA projections for enterprise-wide scenario analysis. In addition, BHCs should establish a strong governance structure to critically scrutinize assumptions, methods, and estimates generated in an enterprise-wide scenario analysis and offer needed adjustments. BHCs should carefully evaluate the validity and relevance of underlying assumptions across business lines, portfolios, loss, expense, and revenue estimates if an enterprise-wide scenario analysis produces post-stress results that are more favorable than the baseline conditions. The outcomes of such analyses should also be reconciled for regulatory as well as management reporting purposes.
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K e y C o n c e p t s
LO 50.1 The Federal Reserves Capital Plan Rule mandates all top-tier, U.S. domiciled bank holding companies with consolidated assets equal to or greater than $50 billion to develop and maintain an effective and robust internal capital plan for evaluating and assessing their capital adequacy.
There are seven principles on which the Federal Reserve assesses the effectiveness of a BHCs internal capital planning, also known as the capital adequacy process (CAP). These seven principles are related to risk management foundation, resource and loss estimation methods, capital adequacy, capital planning and internal controls policies, and governance oversight.
LO 50.2 BHCs should develop a process to effectively identify all of their risk exposures on a firm wide basis. BHCs should establish a mechanism for a comprehensive, independent, and regular review and validation of all the models used for capital adequacy planning purposes. BHCs should have boards actively involved in evaluating and approving their internal capital adequacy plans. BHCs should develop a capital policy that clearly defines the principles and guidelines for capital goals, issuance, usage, and distributions.
Stress testing and stress scenario design should be based on a variety of factors encompassing all the risk exposures faced by BHCs on a firm-wide basis. With the option of utilizing various quantitative and qualitative methods, BHCs should carefully identify key risk exposures on a firm-wide scenario basis. BHCs should use loss estimation methodologies, which are based on sound theoretical and empirical foundations. BHCs should use a combination of inputs in order to develop loss estimates arising from operational risk. In order to estimate the counterparty credit risk, BHCs mostly use probabilistic or deterministic approaches. BHCs using a probabilistic approach should offer evidence of generating probable scenarios stronger than past observed events. BHCs using a deterministic approach should generate a wide range of stress scenarios.
While estimating pro-provision net revenue (PPNR) projection methodologies, BHCs should pay particular attention to interrelationships among numerous relevant variables such as revenues, expenses, and on- and off-balance sheet items within a given scenario. Methodologies used for projecting net interest income should incorporate ongoing, current, and projected balance sheet positions. BHCs should project non-interest income in light of stated risk scenarios and business strategies.
BHCs should have a well-defined process in place to develop projections of revenues, expenses, losses, on- and off-balance sheet items, and risk-weighted assets in an enterprise wide scenario analysis. Projections should be based on sound underlying assumptions, interactions, and factors (main drivers of change), and the estimates should be scrutinized, documented, and reported.
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C o n c e p t C h e c k e r s
1.
2.
3.
4.
5.
The seven principles of an effective capital adequacy process for bank holding companies (BHCs) subject to the Capital Plan Rule include which of the following? I. Oversight from peer BHCs II. Annual reporting to the stock exchange (where their stock is listed) A. I only B. II only. C. Both I and II. D. Neither I nor II.
The Federal Reserves Capital Plan Rule requires BHCs to maintain an effective process for assessing their capital adequacy for: A. BHCs, U.S. or non-U.S. domiciled. B. BHCs with more than five years of operational history. C. BHCs with a net annual income of more than $5 billion. D. BHCs with total consolidated assets of $50 billion or greater.
How many of the following statements is most likely correct? BHCs should have risk identification processes that evaluate: I. On- and off-balance sheet positions. II. Risk transfer and/or risk mitigation techniques. III. Changes in institutions risk profile due to portfolio quality. IV. Reputational risk. A. One statement. B. Two statements. C. Three statements. D. Four statements.
Which of the following statements is most likely correct? A. The internal controls policy of BHCs requires that senior management should furnish the board of directors with sufficient information to comprehend the BHC risk exposures.
B. A governance policy offers fundamental guidelines and principles to BHCs for
the capital issuance, use, distribution, and planning purposes.
C. Suspension or reduction in dividends or repurchase programs do not fall under
the capital policy of BHCs.
D. Designing and testing a scenario-related default of a major counterparty is an
example of BHC stress testing and a stress scenario design policy.
Which of the following statements is most likely correct? I. Under the expected losses methodologies, loss estimation involves three
elements: probability of default, loss given default, and exposure at default. II. Net interest income projections should incorporate changing conditions for balance sheet positions, including embedded options, prepayment rates, loan performance, and re-pricing rates.
A. I only. B. II only. C. Both I and II. D. Neither I nor II.
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C o n c e pt C h e c k e r A n sw e r s
1. D Oversight from peer BHCs and annual reporting to the stock exchange are not included in
the seven principles of an effective capital adequacy process.
2. D BHCs with total consolidated assets of $50 billion or greater. The other answers are not part
of the requirements under the Capital Plan Rule.
3. D All of the statements are correct. BHCs should have risk identification processes effectively
identifying all risk exposures for assessing capital needs. Reputational risk, like strategic risk and compliance risk, falls under the category of other risks and are more difficult to quantify. Nevertheless, there are a wide range of methods BHCs employ to evaluate other risks.
4. D The first statement is the requirement of the governance policy and not the internal control
policy. The second statement falls under capital policy and not the governance policy. Regarding the third statement, capital contingency plans (e.g., suspension or reduction in dividends or repurchase programs) are a key part of capital policies of BHCs detailing the actions intended to be taken under deficiencies in capital position. The fourth statement is correct. Many different scenarios, including counterparty default, fall under the BHCs stress testing and scenario design policy.
5. C Both statements are correct. Loss estimation involves probability of default, loss given default, and exposure at default. Net interest income projections should incorporate changing conditions for balance sheet positions, including embedded options, prepayment rates, loan performance, and re-pricing rates.
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Re pu r c h a s e A g r e e me n t s a n d FINANCING
Topic 51
E x a m F o c u s
Repurchase agreements, or repos, are short-term financing vehicles to borrow/lend funds on a secured basis. The most common repos are for overnight lending. This topic discusses the mechanics of repos, including settlement calculations, the motivations of market participants for entering into repos, as well as the risks (credit risk and liquidity risk) that arise from their use. It also discusses collateral types used in repos, including general collateral and special collateral. For the exam, focus on the characteristics of repo transactions and the primary motivations for using repos. Understanding these motivations should give you a good indication of how and why repos are used in the market, what risks repos hedge, what risks arise from repo trading, and how changes in the market environment affect repos.
M e c h a n i c s o f R e p u r c h a s e A g r e e m e n t s

LO 50.1: Describe the Federal Reserves Capital Plan Rule and explain the seven principles of an effective capital adequacy process for bank holding companies (BHCs) subject to the Capital Plan Rule.
Bank holding companies (BHCs) must have adequate and sufficient capital for their survival and growth. Capital provides a cushion against unexpected losses and allows BHCs to continue to operate. The failure of BHCs (i.e., liabilities exceed assets, resulting in negative capital) would most likely be a burden on taxpayers and deposit insurance funds. An effective and sound capital management policy is critical for the health of BHCs, as well as the smooth functioning and stability of the entire financial system.
The Federal Reserve maintains its interest in survivability and smooth functioning BHCs through its Capital Plan Rule and the annual Comprehensive Capital Analysis and Review (CCAR). The CCAR is the Federal Reserves supervisory program for evaluating capital plans.
The Capital Plan Rule mandates that BHCs develop and put in place a capital plan and a process to evaluate and monitor their capital adequacy. The capital plan covers all U.S. domiciled BHCs with total consolidated assets equal to $50 billion or more.
The Capital Plan Rule lists the principles that the Federal Reserve uses to evaluate the adequacy and appropriateness of a BHCs internal capital planning processes and practices.
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The principles on which the Federal Reserve assesses BHCs for managing and allocating their capital resources is referred to as the capital adequacy process (CAP). The seven principles of the CAP are as follows: 1. Risk management foundation. A BHC has an effective capital risk management plan
to encompass all key risk exposures on a firm-wide basis in terms of identification, evaluation, measurement, and control.
2. Resource estimation methods. A BHC has a capital resource estimation plan to clearly
define and estimate available capital resources over a stress scenario time horizon.
3. Loss estimation methods. A BHC has a process for estimating potential losses and
aggregating them on a firm-wide basis over a given stress scenario time horizon.
4. Impact on capital adequacy. A BHC has a process to evaluate the combined impact on capital adequacygiven loss estimates and capital resources combinedin light of the stated goals with respect to capital level and composition.
5. Capital planning policy. A BHC has a sound capital policy to develop capital goals, determine appropriate capital levels and composition as well as capital distributions (actions) and contingency plans.
6. Internal controls. A BHC has a vigorous internal controls policy in place for
independent review, model validation, documentation, and internal audit of the capital adequacy process.
7. Effective oversight. A BHC has a board and senior management responsible for an effective and thorough oversight of multiple dimensions of the internal capital risk plan, including methods, processes, assessments, validations, reviews, documentation, infrastructure, resources, goals, limitations, and approval of capital decisions.
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C a p i t a l A d e q u a c y P r o c e s s
Internal controls, including model review and valuation

LO 49.4: Describe best practices and assess key concerns for the governance of an economic capital framework.
The soundness of economic capital measures relies on strong controls and governance. Senior management is responsible for making sure these controls are in place and that governance covers the entire economic capital process. Adopting an economic capital framework will improve a banks capital adequacy, strategic planning, risk appetite documentation, and risk-adjusted performance measurement. In order for an economic capital framework to be effective it should include:
Strong controls for changing risk measurements. Comprehensive documentation for measuring risk and allocation approaches.
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Policies for making sure economic capital practices follow outlined procedures. View of how economic capital measures apply to daily business decisions. Best practices for the governance of an economic capital framework cover: 1. Senior management commitment. The successful implementation of an economic capital
framework depends on the involvement and experience of the senior management group. They are one of the main drivers for adopting this framework.
2. The business unit involved and its level o f expertise. Governance structures differ
among banks. Some banks opt for a centralized approach where economic capital responsibilities are assigned to one function (e.g., Treasury), while others opt for a decentralized approach that shares responsibilities between functions (e.g., finance and risk functions). Each business unit within the bank will manage its risk in accordance with the amount of allocated capital. The responsibilities for allocating capital within business units will also vary among banks as will the flexibility to reallocate capital during the budgeting period.
3. The timing o f economic capital measurement and disclosures. Most banks will compute
economic capital on either a monthly or quarterly basis. Pillar 3 of the Basel II Accord encourages the disclosure of information about how capital is allocated to risks.
4. Policies and procedures fo r owning, developing, validating, and monitoring economic
capital models. Formal policies and procedures encourage the consistent application of economic capital across the bank. The owner of the economic capital model will usually oversee the economic capital framework.
Key concerns related to governance and the application of economic capital measures involve: 1. Senior management commitment. The level of management buy-in contributes to
the meaningfulness of the economic capital process. The senior management group must understand the importance of applying economic capital measures for strategic planning purposes.
2. The role o f stress testing. Many banks currently apply stress tests; however, using more
integrating stress tests will allow banks to better assess the impact of a stress scenario on certain economic capital measures.
3. Measuring risk on either an absolute or relative basis. Correctly interpreting economic
capital as an estimate of risk depends on either measuring the absolute level of capital or measuring risk on a relative basis. Some issues within this measurement concern include assumptions regarding diversification and management involvement as well as how the economic model captures risks.
4. Not using economic capital as the only measure that determines required capital. Most
banks align economic capital with external credit ratings. Shareholders desire profitability via lower capital levels while rating agencies encourage solvency via higher capital levels.
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5. Defining available capital resources. Currently, there is no definition for available capital among banks. Most banks adjust Tier 1 capital to determine available capital resources.
6. Transparency o f economic capital measures. Economic capital models are more useful for senior managers when they are transparent. Increased documentation will improve the validity of using the model when making business decisions.
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Ke y C o n c e pt s
LO 49.1 A multitude of challenges exist within the economic capital framework that involve: (1) defining risk measures, (2) risk aggregation, (3) validation of models, (4) dependency modeling in credit risk, (3) evaluating counterparty credit risk, and (6) assessing interest rate risk in the banking book.
LO 49.2 There are ten BIS recommendations that supervisors should consider to make effective use of risk measures.
LO 49.3 A number of specific constraints imposed and opportunities offered by economic capital exist within the areas of credit portfolio management, risk based pricing, customer profitability analysis, and management incentives.
LO 49.4 Best practices for the governance of an economic capital framework cover: (1) senior management commitment, (2) the business unit involved and its level of expertise, (3) the timing of economic capital measurement and disclosures, and (4) policies and procedures for owning, developing, validating, and monitoring economic capital models.
Key concerns related to governance and the application of economic capital measures involve: (1) senior management commitment, (2) the role of stress testing, (3) measuring risk on either an absolute or relative basis, (4) not using economic capital as the only measure that determines required capital, (3) defining available capital resources, and (6) transparency of economic capital measures.
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C o n c e pt Ch e c k e r s
1.
2.
3.
4.
3.
Which of the following risk measures is the least commonly used measure in the practice of risk management? A. Value at risk. B. Standard deviation. C. Expected shortfall. D. Spectral risk measures.
Which of the following aggregation methodologies is characterized by great difficulty in validating parameterization and building a joint distribution? A. Copulas. B. Constant diversification. C. Variance-covariance matrix. D. Full modeling/simulation.
Which of the following model validation processes is specifically characterized by the limitation that it provides little comfort that the model actually reflects reality? A. Backtesting. B. Benchmarking. C. Stress testing. D. Qualitative review.
Which of the following categories of BIS recommendations specifically refers to the need to consider using additional methods, such as stress testing, to help cover all exposures? A. Risk aggregation. B. Counterparty credit risk. C. Dependency modeling in credit risk. D. Interest rate risk in the banking book.
The use of which of the following items is meant more for protecting against risk deterioration? A. Risk based pricing. B. Management incentives. C. Credit portfolio management. D. Customer profitability analysis.
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C o n c e pt C h e c k e r An s w e r s
1. D Spectral and distorted risk measures are the least used of the four measures and are mainly of
academic interest only.
2. A Copulas have two notable disadvantages: (1) parameterization is very difficult to validate, and
(2) building a joint distribution is very difficult.
3. B With benchmarking and hypothetical portfolio testing, the process has its limitations
because it can only compare one model against another and may provide little comfort that the model actually reflects reality. All that the process is able to do is provide broad comparisons confirming that input parameters or model outputs are broadly comparable.
4. B There are trade-offs to be considered when deciding between the available methods of
measuring counterparty credit risk. Additional methods, such as stress testing, need to be used to help cover all exposures.
5. C Credit portfolio management is used as a means to protect against risk deterioration. In
contrast, risk based pricing is used to maximize the banks profitability; customer profitability analysis is used to determine unprofitable or only slightly profitable customers; and management incentives are used to motivate managers to participate in the technical aspects of the economic capital allocation process.
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Ca pi t a l Pl a n n i n g a t La r g e Ba n k Ho l d i n g C o m pa n i e s: Su pe r v i s o r y Ex pe c t a t i o n s a n d Ra n g e o f C u r r e n t Pr a c t i c e
Topic 50
E x a m F o c u s
To protect the smooth functioning of bank holding companies (BHCs), the Federal Reserves Capital Plan Rule requires BHCs to implement an ongoing internal capital plan for thoroughly assessing and enhancing their capital adequacy under stress scenarios on a firm wide basis. For the exam, know the fundamental principles and key practices to develop and implement an effective internal control plan, including: risk identifications, model valuation and review, oversight and governance, contingency planning, stress testing and scenario designing, loss estimation and projections methodologies, and evaluating the impact of capital adequacy, including risk-weighted assets and balance sheet projections.
C a p i t a l P l a n R u l e

LO 49.3: Explain benefits and impacts of using an economic capital framework within the following areas: Credit portfolio management Risk based pricing Customer profitability analysis Management incentives
Credit Portfolio Management
Constraints imposed: Credit quality of each borrower is determined in a portfolio context, not on a stand
A loans incremental risk contribution is used to determine the concentration of the loan
alone basis.
portfolio.
Opportunities offered: The process allows one to determine appropriate hedging strategies to use in reducing
portfolio concentration.
Credit portfolio management becomes a means for protecting against risk deterioration.
Risk-Based Pricing
Constraints imposed: Pricing decisions are based on expected risk-adjusted return on capital (RAROC), so
deals will be rejected if they are lower than a specific RAROC. The proposed interest rate is determined by the amount of economic capital allocated to the deal.
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Pricing decisions include: (1) cost of funding, (2) expected loss, (3) allocated economic
capital, and (4) additional return required by shareholders. Therefore, a minimum interest rate is determined that will increase shareholder value.
Opportunities offered: Can be used to maximize the banks profitability. For example, some pricing decisions may need to be overridden because certain customer relationships are more profitable (at a lower price/interest rate) or desirable from a reputational point of view. Of course, such overrides are not taken lightly and require upper management approval, as well as rigorous subsequent monitoring.
Customer Profitability Analysis
Constraints imposed: The analysis is complicated in that many risks need to be aggregated at the customer
level.
Customers need to be segmented in terms of ranges of (net) return per unit of risk; the
underlying information is difficult to measure and allocate.
Opportunities offered: Assuming that the measurement obstacles have been overcome, the analysis can be easily
used to determine unprofitable or only slightly profitable customers. Such customers could be dropped and economic capital allocated to the more profitable customers.
Economic capital is used in maximizing the risk-return trade-off (through relative risk-
adjusted profitability analysis of customers).
Management Incentives
Constraints imposed:
Studies show that compensation schemes are a minor consideration in terms of the actual uses of economic capital measures at the business unit level.
Opportunities offered:
It is suggested that management incentives is the issue that motivates bank managers to participate in the technical aspects of the economic capital allocation process.
B e s t P r a c t
i c e s a n d C o n c e r n s f o r E c o n o m i c C a p i t a l G o v e r n a n c e

LO 49.2: Describe the BIS recommendations that supervisors should consider to make effective use of internal risk measures, such as economic capital, that are not designed for regulatory purposes.
There are ten Bank for International Settlements (BIS) recommendations to consider: 1. Use of economic capital models in assessing capital adequacy. The bank should show
how such models are used in the corporate decision-making process so as to assess the models impact on which risks the bank chooses to accept. In addition, the board should have a basic understanding of the difference between gross (stand alone) and net (diversified) enterprise-wide risk in assessing the banks net risk tolerance.
2. Senior management. The economic capital processes absolutely require a significant
commitment from senior management. They should understand its importance in the corporate planning process and should ensure that there is a strong infrastructure in place to support the processes.
3. Transparency and integration into decision-making. Economic capital results need to be easy to trace and understand in order to be useful. Careful attention must be given to obtaining reliable estimates on an absolute basis in addition to developing the flexibility to conduct firm-wide stress testing.
4. Risk identification. This is the crucial starting point in risk measurement. The risk measurement process must be very thorough to ensure that the proper risk drivers, positions, and exposures are taken into account in measuring economic capital. That will ensure that there is little variance between inherent (actual) and measured risk. For example, risks that are difficult to quantify should be considered through sensitivity analysis, stress testing, or scenario analysis.
3. Risk measures. No given risk measure is perfect, and a bank must understand the
strengths and weaknesses of its chosen risk measures. No one risk measure for economic capital is universally preferred.
6. Risk aggregation. The reliability of the aggregation process is determined by the
quality of the measurement risk components, plus the interrelationships between such risks. The aggregation process usually requires consistency in the risk measurement parameters. The aggregation methodologies used should mirror the banks business composition and risk profile.
7. Validation. The validation process for economic capital models must be thorough and corroborating evidence from various tests must show that the model works as intended. In other words, within an agreed upon confidence interval and time period, the capital level determined must be enough to absorb the (unexpected) losses.
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8. Dependency modeling in credit risk. Banks must consider the appropriateness of the
dependency structures used within their credit portfolio. Specifically, credit models need to be assessed for their limitations, and such limitations need to be dealt with via appropriate supplementary risk management approaches, such as sensitivity or scenario analysis.
9. Counterparty credit risk. There are trade-offs to be considered in deciding between
the available methods of measuring counterparty credit risk. Additional methods, such as stress testing need to be used to help cover all exposures. Measuring such risk is complicated and challenging. Specifically, the aggregation process needs to be vetted prior to a bank having a big picture perspective of counterparty credit risk.
10. Interest rate risk in the banking book. Specifically, financial instruments with
embedded options need to be examined closely in order to control risk levels. Certainly, there are trade-offs between using earnings-based versus economic value-based models to measuring interest rate risk. For example, the former has aggregation problems because other risks are measured using economic value. Also, using economic value- based models could be inconsistent with business practices.
E c o n o m i c C a p i t a l C o n s t r a i n t s a n d O p p o r t u n i t
i e s

LO 49.1: Within the economic capital implementation framework describe the challenges that appear in: Defining and calculating risk measures Risk aggregation Validation of models Dependency modeling in credit risk Evaluating counterparty credit risk Assessing interest rate risk in the banking book
For this LO, it would be helpful to recall the properties of a coherent risk measure from the Part I curriculum. The properties are as follows: 1. Mono tonicity: A portfolio with greater future returns will likely have less risk.
2. Subadditivity: The risk of a portfolio is at most equal to the risk of the assets within the
portfolio.
3. Positive homogeneity: The size of a portfolio will impact the size of its risk.
4. Translation invariance: The risk of a portfolio is dependent on the assets within the
portfolio.
Defining and Calculating Risk Measures
It is not always apparent how risk should be quantified for a given bank, especially when there are many different possible risk measures to consider. Prior to defining specific measures, one should be aware of the general characteristics of ideal risk measures. They
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should be: intuitive, stable, easy to compute, easy to understand, coherent, and interpretable in economic terms. In addition, the risk decomposition process must be simple and meaningful for a given risk measure.
Standard deviation, value at risk (VaR), expected shortfall (ES), as well as spectral (i.e., coherent) and distorted risk measures could be considered, each with their respective pros and cons. Obviously, no one measure would perfectly consider all of the necessary elements in measuring risk. In practice, VaR and ES are the most commonly used measures. The following section is a summary of challenges encountered when considering the appropriateness of each risk measure.
Standard deviation
Not stable because it depends on assumptions about the loss distribution. Not coherent because it violates the monotonicity condition.
Simple, but not very meaningful in the risk decomposition process.
VaR (the most com m only used measure)
Not stable because it depends on assumptions about the loss distribution. Not coherent because it violates the subadditivity condition (could cause problems in
internal capital allocation and limit setting for sub-portfolios).
Expected shortfall
May or may not be stable, depending on the loss distribution. Not easy to interpret, and the link to the banks desired target rating is not clear.
Spectral and distorted risk measures
Not intuitive nor easily understood (and rarely used in practice). May or may not be stable, depending on the loss distribution. In defining or using such risk measures, banks often consider several of them and for different purposes. For example, absolute risk and capital allocation within the bank are most commonly measured using VaR, but increasingly, the latter is being measured using ES. The VaR measure of absolute risk tends to be easier to communicate to senior management than ES, but ES is a more stable measure than VaR for allocating total portfolio capital. The challenge for the bank is to determine if and when one or the other, or both, should be used.
Amongst the commonly used measures to calculate economic capital, regulators do not have a clear preference for one over another. If different risk measures are implemented by a bank for external versus internal purposes, then there must be a logical connection between the two risk measures. For regulators, merely comparing a banks internal and regulatory capital amounts is insufficient when determining the underlying risks in its portfolio. Therefore, such a task presents an analytical challenge to regulators.
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Risk Aggregation
Risk aggregation involves identifying the individual risk types and making certain choices in aggregating those risk types. Classification by risk types (market, credit, operational, and business) may be approximate and prone to error. For example, the definitions of risk types may differ across banks or within a given bank, which complicates the aggregation process.
Even though one or more of the previously mentioned four risk types may be found at the same time within a given bank portfolio, the portfolio will often be represented by one risk type for the banks classifications purposes. Such a simplistic distinction may result in inaccurate measurements of the risk types and this may bias the aggregation process.
Most banks begin by aggregating risk into silos by risk-type across the entire bank. Other banks prefer using business unit silos, while others combine both approaches. There is no one unanimously accepted method, as each approach has its specific advantages.
Before risk types can be aggregated into a single measure, they must be expressed in comparable units. There are three items to consider: risk metric, confidence level, and time horizon. 1. Risk metric: Relies on the metrics used in the quantification of different risk types.
Must consider whether the metric satisfies the subadditivity condition.
2. Confidence level: Loss distributions for different types of risk are assumed to
have different shapes, which implies differences in confidence intervals. The lack of consistency in choosing confidence levels creates additional complexity in the aggregation process.
3. Time horizon: Choosing the risk measurement time horizon is one of the most
challenging tasks in risk measurement. For example, combining risk measures that have been determined using different time horizons creates problems irrespective of actual measurement methods used. Specifically, there will be inaccurate comparisons between risk types.
A common belief is that combining two portfolios will result in lower risk per investment unit in the combined portfolio versus the weighted average of the two separate portfolios. However, when we consider risk aggregations across different portfolios or business units, such a belief does not hold up with VaR because it does not necessarily satisfy the subadditivity condition. Also, there may be a false assumption that covariance always fully takes into account the dependencies between risks. Specifically, there could be times where the risk interactions are such that the resulting combinations represent higher, not lower, risk. These points highlight an additional challenge in the computation of risk.
There are five commonly used aggregation methodologies. The following is a brief description of them, as well as the challenges associated with using them. 1. Simple summation
Adding together individual capital components.
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Does not differentiate between risk types and therefore assumes equal weighting. Also, does not take into account the underlying interactions between risk types or for differences in the way the risk types may create diversification benefits. In addition, complications arising from using different confidence levels are ignored.
2. Constant diversification
Same process as simple summation except that it subtracts a fixed diversification percentage from the overall amount. Similar challenges as simple summation.

3. Variance-covariance matrix
Summarizes the interdependencies across risk types and provides a flexible framework for recognizing diversification benefits.
Estimates of inter-risk correlations (a bank-specific characteristic) are difficult and
costly to obtain, and the matrix does not adequately capture non-linearities and skewed distributions.
4. Copulas
Combines marginal probability distributions into a joint probability distribution
through copula functions.
More demanding input requirements and parameterization is very difficult to
validate. In addition, building a joint distribution is very difficult.
3. Full modeling/simulation

Simulate the impact of common risk drivers on all risk types and construct the joint distribution of losses.
The most demanding method in terms of required inputs. Also, there are high
information technology demands, the process is time consuming, and it may provide a false sense of security.
The variance-covariance approach is commonly used by banks. Frequently, however, bank- specific data is not available or is of poor quality. As a result, the items in the variance- covariance matrix are completed on the basis of expert judgment. On a related note, banks often use a conservative variance-covariance matrix where the correlations are reported to be approximate and biased upward. In order to reduce the need for expert judgment, banks may end up limiting the dimensionality of the matrix and aggregating risk categories so that there are only a few of them, not recognizing that such aggregations embed correlation assumptions. Clearly, a disadvantage of such a practice is that each category becomes less homogenous and therefore, more challenging to quantify.
One potential disadvantage of the more sophisticated methodologies is that they often lead to greater confidence in the accuracy of the output. It is important to consider robustness checks and estimates of specification and measurement error so as to prevent misleading results.
Validation of Models
Validation is the proof that a model works as intended. As an example, while it is a useful tool to test a models risk sensitivity, it is less useful for testing the accuracy of high quantiles in a loss distribution.
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The validation of economic capital models differs from the valuation of an IRB (internal- ratings based) model because the output of economic capital models is a distribution rather than a single predicted forecast against which actual outcomes may be compared. Also, economic capital models are quite similar to VaR models despite the longer time horizons, higher confidence levels, and greater lack of data.
There are six qualitative validation processes to consider. The following is a brief description of them, as well as the challenges associated with using them (where applicable). 1. Use test

If a bank uses its measurement systems for internal purposes, then regulators could place more reliance on the outputs for regulatory capital.
The challenge is for regulators to obtain a detailed understanding of which models
properties are being used and which are not.
2. Qualitative review
Must examine documentation and development work, have discussions with the
models developers, test and derive algorithms, and compare with other practices and known information.
The challenge is to ensure that the model works in theory and takes into account the
correct risk drivers. Also, confirmation of the accuracy of the mathematics behind the model is necessary.
3. Systems implementation
For example, user acceptance testing and checking of code should be done prior to
implementation to ensure implementation of the model is done properly.
4. Management oversight

It is necessary to have involvement of senior management in examining the output data from the model and knowing how to use the data to make business decisions.
The challenge is ensuring that senior management is aware of how the model is used
and how the model outputs are interpreted.
3. Data quality checks
Processes to ensure completeness, accuracy, and relevance of data used in the
model. Examples include: qualitative review, identifying errors, and verification of transaction data.
6. Examination of assumptionssensitivity testing
Assumptions include: correlations, recovery rates, and shape of tail distributions.
The process involves reviewing the assumptions and examining the impact on model outputs.
There are also six quantitative validation processes to consider. The following is a brief description of them, as well as the challenges associated with using them (where applicable). 1. Validation of inputs and parameters
Validating input parameters for economic capital models requires validation of those
parameters not included in the IRB approach, such as correlations.
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The challenge is that checking model inputs is not likely to be fully effective because
every model is based on underlying assumptions. Therefore, the more complex the model, the more likely there will be model error. Simply examining input parameters will not prevent the problem.
2. Model replication
Attempts to replicate the model results obtained by the bank. The challenge is that the process is rarely enough to validate models and in practice,
there is little evidence of it being used by banks. Specifically, replication simply by re-running a set of algorithms to produce the same set of results is not considered enough model validation.
3. Benchmarking and hypothetical portfolio testing
The process is commonly used and involves determining whether the model
produces results comparable to a standard model or comparing models on a set of reference portfolios.
The challenge is that the process can only compare one model against another and
may provide little comfort that the model reflects reality. All that the process is able to do is provide broad comparisons confirming that input parameters or model outputs are broadly comparable.
4. Backtesting
of outcomes to forecasts.
Considers how well the model forecasts the distribution of outcomescomparison
The challenge is that the process can really only be used for models whose outputs can be characterized by a quantifiable metric with which to compare an outcome. Obviously, there will be risk measurement systems whose outputs cannot be interpreted this way. Also, backtesting is not yet a major part of banks validation practices for economic purposes.
3. Profit and loss attribution

Involves regular analysis of profit and losscomparison between causes of actual profit and loss versus the models risk drivers.
The challenge is that the process is not widely used except for market risk pricing
models.
6. Stress testing

Involves stressing the model and comparing model outputs to stress losses.
Overall, although these validation processes may be highly effective in areas such as risk sensitivity, they may not be effective in areas such as overall absolute accuracy.
Additionally, there is difficulty in validating the conceptual soundness of a capital model. The development of a model almost always requires assumptions to be made. However, some of the assumptions may not be testable, so it could be impossible to be absolutely certain of the conceptual soundness of a model. Even though the underlying points may appear reasonable and logical, that may not be the case in practice.
>From a regulators perspective, some industry validation practices are weak, especially for total capital adequacy of the bank and the overall calibration of models. Such a
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validation project is challenging because it usually requires evaluation of high quantiles of loss distributions over long periods of time. In addition, there are data scarcity problems plus technical difficulties, such as tail estimation. Therefore, it is important for senior management and model users to understand the limitations of models and the risks of using models that have not been fully validated.
Dependency Modeling in Credit Risk
Modeling the dependency structure between borrowers is crucial, yet challenging. Both linear and nonlinear dependency relationships between obligors need to be considered.
In general, dependencies can be modeled using: credit risk portfolio models, models using copulas, and models based on the asymptotic single-risk factor (ASRF) model. With the ASRF approach, banks may use their own estimates of correlations or may use multiple systematic risk factors to address concentrations. Such an approach would result in questioning the method used to calibrate the correlations and the ways in which the bank addressed the infinite granularity and single-factor structure of the ASRF model. ASRF can be used to compute the capital requirement for credit risk under the IRB framework.
There are many issues to consider regarding the challenges in coming up with reliable dependency assumptions used in credit risk portfolio models. Regulators may need to test the accuracy and strength of correlation estimates used by banks given their heavy reliance on model assumptions and the significant impact on economic capital calculations.
In the past, the validity of the following assumptions have been questioned: (1) the ASRF Gaussian copula approach, (2) the normal distribution for the variables driving default, (3) the stability of correlations over time, and (4) the joint assumptions of correctly specified default probabilities and doubly-stochastic processes, which suggest that default correlation is sufficiently captured by common risk factors.
Doubts have been raised about the ability of some models using such assumptions in terms of their ability to explain the time-clustering of defaults that is seen in certain markets. Insufficiently integrating the correlation between probability of default (PD) and loss given default (LGD) in the models, coupled with insufficiently modeling LGD variability, may lead to underestimating the necessary economic capital. Furthermore, it will create challenges in identifying the different sources of correlations and the clustering of defaults and losses.
Rating changes are greatly impacted by the business cycle and are explained by different models during expansionary and recessionary periods. As a result, the sample period and approach used to calibrate the dependency structure could be important in assessing whether correlation estimates are overestimated or underestimated. Furthermore, some models assume that unobservable asset returns may be approximated by changes in equity prices but fail to consider that the relationship between asset returns and equity prices are unobservable and may be non-linear. Also, the use of equity prices to estimate credit default probability is problematic because such prices may include information that is irrelevant for credit risk purposes. As a result, using equity prices may result in some inaccuracy in the correlation estimates.
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In contrast, when banks use a regulatory-type approach, the assumptions of such an approach create other challenges for both banks and regulators: Correlation estimates need to be estimated, but there may be limited historical data
on which to base the correlation estimates. Also, the assumptions used to generate the correlations may not be consistent with the underlying assumptions of the Basel II credit risk model.
A banks use of the Basel II risk weight model requires concentration risk to be accounted
for by other measures and/or management methods. It will also require regulators to evaluate such measures/methods.
A key challenge to overcome is the use of misspecified or incorrectly calibrated correlations and the use of a normal distribution (which does not replicate the details of the distribution of asset returns). This may lead to large errors in measuring portfolio credit risk and economic capital.
Evaluating Counterparty Credit Risk
Such a task is a significant challenge because it requires: obtaining data from multiple systems, measuring exposures from an enormous number of transactions (including many that exhibit optionality) spanning a wide range of time periods, monitoring collateral and netting arrangements, and categorizing exposures across many counterparties. As a result, banks need to have well-developed processes and trained staff to deal with these challenges.
Market-risk-related challenges to counterparty exposure at default (EAD) estimation. Counterparty credit exposure requires simulation of market risk factors and the Market risk VaR models combine all positions in a portfolio into a single simulation. revaluation of counterparty positions under simulated risk factor shocks, similar to VaR models. Consider the following two challenges that occur when attempting to use VaR model technology to measure counterparty credit exposure. Market risk VaR models combine all positions in a portfolio into a single simulation.
Therefore, gains from one position may fully offset the losses in another position in the same simulation run. However, counterparty credit risk exposure measurement does not allow netting across counterparties. As a result, it is necessary to compute amounts at the netting set level (on each set of transactions that form the basis of a legally enforceable netting agreement), which increases computational complexity.
Market risk VaR calculations are usually performed for a single short-term holding
period. However, counterparty credit exposure measurement must be performed for multiple holding periods into the future. Therefore, market risk factors need to be simulated over much longer time periods than in VaR calculations, and the revaluation of the potential exposure in the future must be done for the entire portfolio at certain points in the future.
Credit-risk-related challenges to PD and LGD estimation.
Some material transactions are performed with counterparties with which the bank does not have any other exposures. Therefore, the bank must calculate a probability of default (PD) and loss given default (LGD) for the counterparty and transaction.
For hedge funds, the measurement challenge occurs when there is little information
provided on underlying fund volatility, leverage, or types of investment strategies employed.
Even for counterparties with which the bank has other credit exposures, the bank still
needs to calculate a specific LGD for the transaction.
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Interaction between market risk and credit riskwrong-way risk.
Identifying and accounting for wrong-way risk (exposures that are negatively correlated with the counterpartys credit quality) is a significant challenge because it requires an understanding of the market risk factors to which the counterparty is exposed. That would be difficult to do in the case of a hedge fund, for example, which would be less transparent. It also requires a comparison of those factor sensitivities to the factor sensitivities of the banks own exposures to the counterparty.
The magnitude of wrong-way risk is difficult to quantify in an economic capital model

since it requires a long time horizon at a high confidence level.
Operational-risk-related challenges in managing counterparty credit risk. The challenge is that managing such risk requires specialized computer systems and
people. Complicated transactions, such as daily limit monitoring, marking-to-market, collateral management, and intraday liquidity and credit extensions, increase the risk of measurement errors.
The quantification of operational risks is a significant challenge, especially when it pertains to new or rapidly growing businesses, new products or processes, intraday extensions of credit, and infrequently occurring but severe events.
Differences in risk profiles between margined and non-margined counterparties. The modeling difference between the two types of counterparties is primarily concerned with the future forecasting period. For margined counterparties, the forecasting period is short, and for non-margined counterparties, it is usually much longer.
As a result of the difference in time periods, the aggregation of risk between these two types of counterparties is a challenge because the usual procedure is to use a single time period for all positions.
Aggregation challenges.
In general, the challenges are increased significantly when moving from measuring credit risk of one counterparty to measuring credit risk of the firm in general for economic capital purposes.
When counterparties have both derivatives and securities financing activities, the
problem is especially challenging because the systems in place may not be able to handle such aggregation.
Further aggregation challenges exist when high-level credit risk measures are required to be aggregated with high-level market risk and operational risk measures in order to calculate economic capital.
Breaking down counterparty credit risk into detailed component parts (as is often
done with market risk) is another challenge. The sheer computational complexities and enormous amounts of data required would generally be cost prohibitive to perform on a frequent basis. The challenge still remains for many banks due to outdated or ineffective computer systems.
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Assessing Interest Rate Risk in the Banking Book
The computation challenge arises from the long holding period assumed for a banks balance sheet and the need to model indeterminate cash flows on both the asset and liability side due to the embedded optionality of many banking book items.
Optionality in the hanking book. A major measurement challenge is found with non-linear risk from long-term fixed-
income obligations with embedded options for the borrower to prepay and from embedded options in non-maturity deposits. In considering the asset side of the balance sheet, prepayment risk options (i.e., mortgages, mortgage-backed securities, and consumer loans) are the main form of embedded options. The prepayment option results in uncertain cash flows and makes interest rate risk measurement a difficult task. In considering the liability side, there are two embedded options in non-maturity deposits: (1) the bank has an option to determine the interest rate paid to depositors and when to amend the rate, and (2) the depositor has the option to withdraw up to the entire balance with no penalty. The interaction between these two embedded options creates significant valuation and interest rate sensitivity measurement problems. Sufficiently modeling optionality exposures requires very complex stochastic-path evaluation techniques.

Banks pricing behavior. This factor contributes to the challenges in measuring the interest rate risk of banking
book items. For example, it would require a model to analyze the persistence of the many different non-maturity banking products, as well as a model to determine bank interest rates that consider general market conditions, customer relationships, bank commercial power, and optimal commercial policies.
Determining bank interest rates would require the pricing of credit risk. The price
of credit risk applied to different banking products creates a challenge because it would require a pricing rule that links the credit spread to changes in macroeconomic conditions and interest rate changes. Also, it means that interest rate stress scenarios should consider the dependence between interest rate and credit risk factors.
The choice o f stress scenarios. The drawbacks of using simple interest rate shocks pose interest rate measurement Are not based on probabilities and, therefore, are difficult to integrate into economic challenges because the shocks:
Are not based on probabilities and, therefore, are difficult to integrate into economic
capital models based on VaR. Are not necessarily sensitive to the current rate or economic environment.Do not allow for an integrated analysis of interest rate and credit risks on banking
Are not necessarily sensitive to the current rate or economic environment.
Do not take into account changes in the slope or curvature of the yield curve.
Do not allow for an integrated analysis of interest rate and credit risks on banking
book items.
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BIS R e c o m m e n d a t i o n s f o r S u p e r v i s o r s