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LO 17.2: Describe common tasks performed by a banking credit analyst.
There are three main types of banking credit analysts: (1) counterparty credit analyst, (2) fixed-income analyst, and (3) equity analyst. Common tasks for each type of analyst are described in the following.
Counterparty credit analysts perform risk evaluations (reports) for a given entity. The triggering event to perform such evaluations may be an annual review of the entity or an intent to engage in an upcoming transaction with that entity. The tasks might be limited to simply covering certain counterparties or even only certain transactions or might be expanded to include decision making, recommendations on credit limits, and presenting to the credit committee.
Should the duties extend into the decision-making process, responsibilities would include the following: (1) authorizing the allocation of credit limits, (2) approving credit risk mitigants (i.e., guarantees, collateral), (3) approving excesses or exceptions over established credit limits, and (4) liaising with the legal department regarding transaction documentation.
Some analysts may be required to review and propose amendments to the banks existing credit policies. With the implementation of the extensive regulatory requirements of Basel II and Basel III, credit analysts are now responsible for a wider range of regulatory compliance tasks.
Finally, counterparty credit analysts must understand the risks inherent with specific financial products and transactions. Therefore, it is necessary to obtain knowledge of the banks products to supplement their credit decisions.
In an effort to make profits for the entity, fixed-income analysts provide recommendations regarding the decision to buy, sell, or hold debt securities. Therefore, they must ascertain the relative value to determine whether the security is undervalued, overvalued, or correctly valued. Both fundamental and technical analyses are generally performed in arriving at investment decisions. Fundamental analysis focuses on default risk while technical analysis focuses on market timing and pricing patterns. In making an investment decision, fixed- income analysts consider the ratings for specific debt securities issued by the rating agencies. The ratings provide reliable input in computing the relative value of securities.
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Equity analysts analyze publicly traded financial institutions to help in determining whether an investor should buy, sell, or hold the shares of a given financial institution. When performing valuations, there is an emphasis on using return on equity (ROE). ROE takes into account both profitability and leverage. Other types of analysts would look at a wider range of financial ratios dealing with a banks asset quality, capital strength, and liquidity. Equity analysts usually perform company valuations based on unaudited projections (while other analysts usually use audited historical data). Similar to fixed-income analysts, there are two general approaches to equity analysis. Analysts could choose to perform fundamental analysis, technical analysis, or a combination of the two.
B a n k i n g C r e d i t A n a l y s t S k i l l s

LO 17.1: Describe, compare and contrast various credit analyst roles.
There are several methods to describe, compare, and contrast the various credit analyst roles, including:

Job descriptions (e.g., consumer credit analyst, credit modeling analyst, corporate credit analyst, counterparty credit analyst, credit analysts at rating agencies, sell-side/buy-side fixed-income analysts, bank examiners and supervisors). Functional objective (e.g., risk management vs. investment selection, primary vs. secondary research).
Type of entity analyzed (e.g., consumer, corporate, financial institution, sovereign/
municipal).
Classification by employer (e.g., banks and other financial institutions, institutional
investors, rating agencies, government agencies).
Job Description
Brief descriptions of typical analyst roles provide a general understanding and an appreciation for the wide range of available roles.
Consumer Credit Analyst
An administrative role with little opportunity for detailed analysis, data entry duties for
loans that are then scored electronically (i.e., the relative score will determine status as approved or declined). Primarily works with individual consumer mortgages, with a key objective that all documentation is in place for approved loans. Large dollar loans referred by analyst to more senior personnel.

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Credit Modeling Analyst
A more quantitative role focused on the electronic scoring system described previously;
some interaction with risk management personnel.
Developing, testing, implementing, and updating various consumer credit scoring
systems.
Corporate Credit Analyst

Scope of analysis is limited to corporations (i.e., no financial institutions or sovereign credits). Some duties developing credit risk models may be required.
Counterparty Credit Analyst
Analyzes typical counterparties (i.e., banks, nonbanks brokers, insurance companies, hedge funds); usually employed by a financial institution to analyze other institutions with which it contemplates a two-way transaction. Performs credit reviews, approves limits, and develops/updates credit policies and procedures.

Review process is often detailed requiring the following: (1) capital structure

analysis (i.e., debt, equity), (2) financial statement analysis, (3) qualitative analysis of counterparty, and (4) qualitative analysis of the operating sector of counterparty. Finally, an internal rating is assigned and the analyst may also be required to comment on any of the following: (1) recommended limits to set on certain credit risk exposures, (2) approval or denial of a given credit application, and (3) recommended changes to the amounts, tenor, collateral, or other provisions of the transaction.
Credit Analysts at Rating Agencies

Provide unbiased external ratings on bonds and other debt instruments issued by financial institutions, corporations, and governments.
Sell-Side and Buy-Side Fixed-Income Analysts

Employed by financial institutions or hedge funds. In addition to credit risk, there is a focus on the relative value of debt instruments and their attractiveness as investments.
Bank Examiners and Supervisors
Assessing the financial stability of financial institutions within a supervisory (risk
management) role.
Functional Objective
Most credit analysts are employed to evaluate credit risk as part of an entitys overall risk management function. At the same time, others are employed for security selection and investment opportunity purposes. In terms of the amount and nature of work performed by analysts, there is a distinction between performing primary research versus secondary research.
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Risk Management
Credit risk management is the most common functional objective, and it occurs in both the private and public sector. Credit risk analysts in the public sector will perform research on potential counterparties. The output of the research typically consists of internal use credit reports on the counterparties as well as recommendations as to which deals to accept and the appropriate risk limits. Bank examiners operate in the public sector in a regulatory capacity by reviewing the credit risk of certain financial institutions. Within that role, two key risk management objectives for the financial system are to ensure it is robust and to promote depth and liquidity.
Investment Selection
Investment selection is a much less common functional objective. Generally, credit analysts examine fixed-income securities with a focus on the risk of default. Specifically, an analyst must assess the likelihood of a given investment deteriorating in credit quality, thereby increasing credit risk and resulting in a decline in value. Additionally, a fixed-income analyst must also focus on the relative value of the investment. Relative value refers to the attractiveness of a given debt security compared to similar securities (e.g., other debt issues with the same asset class or same rating).
Rating Agency
The work of rating agency analysts is used for both risk management and investment selection purposes. The analysts examine issuers, counterparties, and debt in generally the same manner as credit risk analysts in the public sector.
Primary Research
Primary research refers to analyst-driven credit research or fundamental credit analysis. This is usually detailed (and often time-consuming) research with human effort that is both quantitative and qualitative in nature. The analysis looks at microeconomic factors (specific to the entity) and macroeconomic factors (e.g., political, industry). Rating agency analysts provide value by performing detailed credit analysis and arriving at independent conclusions, all of which is subsequently relied upon by other analysts. One of the disadvantages of primary research is its high cost; as a result, some financial institutions have an automated credit scoring system for simpler and less expensive transactions.
Secondary Research
It is often difficult for the credit analyst to perform detailed first-hand analysis (e.g., in- person visits), especially if the counterparty is very large or is located in a foreign country. An alternative is to perform secondary research, which involves researching the ratings provided by other rating agency analysts. Such information is combined with other relevant information sources, current information about the counterparty, and the analysts own research, to conclude the counterpartys credit risk assessment. Given the reliance on other research, secondary research reports tend to be much shorter than primary research reports.
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The goal of using secondary research is for a financial institution to perform counterparty credit analysis in a quick and efficient manner while maintaining reliability.
Type o f Entity Analyzed
Corporate Credit Analyst
This role focuses on analyzing firms that are not financial institutions, notable examples being manufacturing firms or service providers. The purpose of the analysis is to assess the level of the firms credit risk. That assessment is then used in deciding whether or not an entity would conduct business with, lend money to, or purchase securities of the other firm. In general, such analysis is very specialized based on the industry as well as focused on specific transactions.
Although the basic analytical principles are the same, there is huge diversity in the sectors, products, size, and geographic locations of the firms being analyzed. As a result, the corporate credit analyst must possess specific industry knowledge in order to be effective. An analyst will generally focus on only one or two industries, especially among fixed-income and rating agency analysts (given their need to perform detailed primary research).
Common sectors analyzed include the following: (1) real estate, (2) chemicals, (3) energy, (4) utilities, (3) telecommunications, (6) natural resources, (7) paper and forest products, and (8) automotive.
Another point of consideration is the size of the firm being analyzed. With a large public company, there may be a lot of public information available that would only necessitate secondary research, thereby reducing costs. With a smaller private company, less information is likely available, and as a result, more due diligence and primary research would be required, thereby increasing costs.
Finally, cash flow analysis is key to assessing corporate credit risk, so corporate analysts must also be equipped with strong accounting and financial statement analysis skills.
Bank and Financial Institution Credit Analyst
Counterparty credit analysts are employed by banks and other financial institutions and focus on analyzing the creditworthiness of other banks and other financial institutions. Compared to corporate credit analysis, the objective is not to make a lending decision but to determine whether the entity being analyzed is sufficiently creditworthy to function as a counterparty in future two-way transactions, with the entity requesting the analysis. Counterparty analysts could also establish exposure limits or decide whether to transact with the potential counterparty.
Both the nature of the financial instrument(s) and the length of time (tenor) of proposed contracts have a direct impact on the potential losses, and, as a result, have a direct impact on the type of analysis to be performed. Common financial instruments involved in counterparty transactions include (1) unsecured debt through the interbank market, (2) repurchase (repo) or reverse repurchase (reverse repo) transactions, (3) receivables factoring, (4) foreign exchange, and (3) derivatives.
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Sovereign!Municipal Credit Analyst
Sovereign credit analysts determine the risk of default by foreign governments on borrowed funds. Primarily, sovereign credit analysts need to consider macroeconomic indicators in determining a governments ability to repay its debts. Additionally, political risk is an important consideration; the analyst attempts to gauge political stability and its impact on the ability to repay. Sovereign credit analysts examine the risks involved with specific international transactions or transactions with specific countries, provinces, states, or cities.
The stability of a given countrys banking system strongly correlates with the ability of a countrys government to repay foreign debt. The correlation also means that a governments financial stability impacts its banking system. Therefore, when analyzing the credit risk of foreign banks, analysts must place a lot of emphasis on sovereign risk. The obvious component of sovereign risk would include an analysis of the foreign countrys debt-issuing ability in addition to the securities already issued. Another component would include an analysis of the impact of the countrys general operating environment on its banking environment.
Classification by Employer
Banks, Nonbank Financial Institutions, and Institutional Investors
Credit analysts are most frequently employed by banks. Amongst all three groups, credit analysts usually function either within a risk management or an investment selection role.
Rating Agencies
Credit analysts employed by rating agencies analyze banks, corporations, and governments to determine their creditworthiness. Analysis includes the following steps:
Step 1: A general analysis of the credit risk of the entity. Step 2: An analysis of issued securities and their impact on credit risk. Step 3: An overall rating recommendation for the entity (communicated through rating
symbols that are widely recognized and understood).
The information provided by the rating agencies is used by investors and risk personnel in making decisions regarding lending amounts, lending rates, and investment amounts.
Government Agencies
A typical role is a regulatory one, whereby the credit analyst analyzes a bank or insurance company to determine its level of risk, financial stability, and whether it meets the regulatory requirements to continue operating. A lesser-known role is when the government acts as an investor or lender, whereby the credit analyst has similar functions (i.e., investment selection or a risk management focus) to its counterparts in other organizations.
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Rating Advisor
This is a unique role most frequently found in investment banks. The rating advisor has likely been a rating agency analyst and is now working to help a debt issuer obtain the highest rating possible. The rating advisor would perform an independent credit analysis of the issuer to arrive at a likely rating. The advisor would then provide advice to the issuer on how to mitigate any issues and respond to rating agency questions.
B a n k i n g C r e d i t A n a l y s t T a s k s

LO 16.7: Com pare bank failure and bank insolvency.
Bank insolvency and bank failures are not identical. Banks become insolvent and are often merged into healthier institutions. It is more convenient and less expensive for the government to simply fold a troubled bank into a stronger bank than it is to close the bank. In fact, there is an assumption that bank failures are relatively common, but in reality, it rarely happens in non-crisis periods. Weak banks are merged with healthier banks, and the system avoids outright failures. This is especially true for large, international banks (i.e., banks that are too big to fail). In the United States, only 50 banks failed between 2001 and 2008, half of which failed in 2008. This equates to a rate of approximately 0.1% per year during the period. Following the financial crisis, approximately 2% of banks failed in both 2009 and 2010. An additional 1.2% of banks failed in 2011. Research indicates that bank failures are considerably less likely than nonfinancial firm failures.
In the last few years, beginning with the financial crisis in late 2007, many more large banks in Europe and the United States have suffered from financial stresses. Flowever, it was clear during the crisis that some banks were considered too big to fail. In response, the Financial Stability Board (FSB) created a list of 29 systemically important financial institutions that are required to hold additional loss absorption capacity tailored to the impact of their [possible] default. The concern is systemic risk that spreads to other institutions. There was substantial evidence of this occurrence during the financial crisis.
A bank can remain insolvent (without failing), so long as it has a source of liquidity. The Federal Reserve is one such source and acts as a lender of last resort. A bank failure that results in significant losses to depositors and other creditors is quite rare, although as noted, the incidence increases in times of crisis, such as in 2007. For a credit analyst evaluating a financial institution, the expectation of an outright failure is unlikely. However, because banks are heavily leveraged, the risks cannot be ignored. The analyst must place the bank somewhere on the continuum between pure creditworthiness and bankrupt. At one end of the continuum are banks with AAA-rated debt, and at the other end are banks with default ratings. Thus, thinking about bank risk on a continuum is useful in defining the banks credit risk.
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K e y C o n c e p t s
LO 16.1
Credit risk is the probability that a borrower will not pay back a loan in accordance with the terms of the credit agreement. Credit risk results when an individual or firm defaults on a financial obligation. It arises short of default when there is an increased probability of default on a financial obligation. A more severe loss than expected due to a greater than expected exposure at the time of a default or a more severe loss than expected due to a lower than expected recovery at the time of a default are also components of credit risk. Finally, credit risk can arise from a default on a payment for goods or services that are already rendered (i.e., settlement risk).
LO 16.2
There are four primary components of credit risk evaluation: (1) the borrowers (obligors) willingness and capacity to repay the loan, (2) the effect of external conditions on the borrowers ability to repay the loan, (3) the inherent characteristics of the credit instrument and the extent to which the characteristics affect the borrowers willingness and ability to perform the obligation, and (4) the quality and adequacy of risk mitigants such as collateral and loan guarantees.
LO 16.3
If collateral is used as a credit risk mitigant, a bank or other lender may not have to force a delinquent borrower into bankruptcy but may instead sell the collateral to satisfy the financial obligation. If a loan guarantor is used as a credit risk mitigant, the guarantor accepts liability for debt if the primary borrower defaults. Typically, the guarantor has a greater ability to pay than the primary borrower.
LO 16.4
Qualitative techniques are used primarily to assess the borrowers willingness to repay the loan. Quantitative techniques are used primarily to assess the borrowers ability to repay the loan. Gathering information from a variety of sources about the character and reputation of the potential borrower, face-to-face interviews with potential borrowers, and using past loan payment information to draw conclusions about a borrowers willingness to pay in the future are all qualitative techniques. .Analyzing the borrowers recent and past financial statements is the primary quantitative method used in credit analysis.
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LO 16.5
There are key differences between the analysis of the creditworthiness of consumers, versus that of nonfinancial and financial firms. Individual factors such as a persons net worth, salary, assets, reputation, and credit score are used to evaluate individuals. It is more complex to evaluate firms. Liquidity, cash flow combined with earnings capacity and profitability, capital position (solvency), state of the economy, and strength of the industry are used to evaluate nonfinancial firms. Similar data is used for financial firms in addition to bank-specific measures such as capital adequacy, asset quality, and the banks ability to withstand financial stress. Detailed manual analyses, including financial statement analysis and interviews with management, are used to analyze the creditworthiness of both nonfinancial and financial firms.
LO 16.6
Current measures used to evaluate credit risk are:
The probability of default (PD), which is the likelihood that a borrower will default. The loss given default (LGD), which represents the likely percentage loss if the borrower

does default. Exposure at default (EAD), which can be stated as a dollar amount (e.g., the loan balance outstanding) or as a percentage of the nominal amount of the loan or the maximum amount available on a credit line. Expected loss (EL), which is, for a given time horizon, calculated as the product of the PD, LGD, and EAD (i.e., PD x LGD x EAD).
Time horizon or tenor of the loan. The longer the time horizon, the greater the risk to
the lender.
LO 16.7
Bank insolvency and bank failure are not one in the same. A bank may be insolvent but avoid failure so long as liquidity is available. Also, many insolvent banks are merged with financially sound banks, avoiding outright failure. For the credit analyst, the fact that failure of financial institutions is rare makes analysis easier. However, banks are highly leveraged, placing the bank somewhere on the continuum between fully creditworthy and insolvent.
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C o n c e p t C h e c k e r s
1.
2.
3.
Blackstone Credit, Inc., made a loan to a small start-up firm. The firm grew rapidly, and it appeared that Blackstone had made a good credit decision. However, the firm grew too fast and could not sustain the growth. It eventually failed. Blackstone had initially estimated its exposure at default to be $1,200,000. Because of the firms rapid growth and resulting increases in the line of credit, Blackstone ultimately lost $1,550,000. In terms of credit risk, this is an example of: A. default on payment for goods or services already rendered. B. a more severe loss than expected due to a ratings downgrade by a rating agency. C. a more severe loss than expected due to a greater than expected exposure at the
time of a default.
D. a more severe loss than expected due to a lower than expected recovery at the
time of a default.
Brent Gulick, a credit analyst with Home Town Bank, is considering the loan application of a small, local car dealership. The dealership has been solely owned by Bob Justice for more than 20 years and sells three brands of American automobiles. Because of the rural location, most of the cars sold in the past by the dealership have been large pick-up trucks and sports utility vehicles. However, sales have declined, and gasoline prices have continued to increase. As a result, Justice is considering selling a line of hybrid cars. Justice has borrowed from Home Town Bank before but currently does not have a balance outstanding with the bank. Which of the following statements is not one of the four components of credit analysis Gulick should be evaluating when performing the credit analysis for this potential loan? A. The business environment, competition, and economic climate in the region. B. Justices character and past payment history with the bank. C. The car dealerships balance sheets and income statements for the last few years
as well as Justices personal financial situation.
D. The financial health of Justices friends and family who could be called upon to
guarantee the loan.
Sarah Garrison is a newly hired loan officer at Lexington Bank and Trust. Her boss told her she needs to make five commercial loans this month to meet her sales goal. Garrison talks to friends and hears about a local businessperson with a great reputation. Everyone in town says John Johnson is someone you want to meet. Garrison sets up a meeting with Johnson and is immediately impressed with his business sense. They discuss a loan for a new venture Johnson is considering, and Garrison agrees that it is a great idea. She takes the loan application back to the bank and convinces the chair of the loan committee that Lexington Bank and Trust is lucky to be able to do business with someone with Johnsons reputation. This is an example of: A. historical analysis technique. B. qualitative analysis technique. C. quantitative analysis technique. D. extrapolation analysis technique.
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4.
5.
Stacy Smith is trying to forecast the potential loss on a loan her firm made to a mid- size corporate borrower. She determines that there will be a 75% loss if the borrower does not perform the financial obligation. This is the: A. probability of default. B. C. expected loss. D. exposure at default.
loss given default.
Bank of the Plain States has been struggling with poor asset quality for some time. The bank lends primarily to large farming operations that have struggled in recent years due to a glut of soybeans and corn on the market. Bank regulators have recently required that the bank write off some of these loans, which has entirely wiped out the capital of the bank. However, the bank still has some liquidity sources it can use, including a correspondent bank and the Federal Reserve. Bank of the Plain States is: A. an insolvent but not failed bank. B. both a failed bank and an insolvent bank. C. neither a failed bank nor an insolvent bank. D. a failed bank but not an insolvent bank.
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C o n c e p t C h e c k e r An s w e r s
1. C Blackstone lost more than expected due to greater exposure at the time of default than
initially estimated. The borrowing firm was a small start-up, so it was not likely rated. There were no goods or services rendered in this case. In addition, there is no mention of recovery. This is also an example of credit risk arising from default on a financial obligation.
2. D There are four primary components of credit risk evaluation: (1) the borrowers (obligors)
willingness and capacity to repay the loan, (2) the effect of external conditions on the borrowers ability to repay the loan, (3) the inherent characteristics of the credit instrument and the extent to which the characteristics affect the borrowers willingness and ability to repay the loan, and (4) the quality and adequacy of risk mitigants such as collateral and loan guarantees. In this case, the local business environment, Justices character, his payment history, and the businesss financial positions are all relevant. While risk mitigants such as collateral and loan guarantees are part of credit analysis, it is unlikely that a local car dealer who has been in business for 20 years would be seeking a loan guarantee from a friend or family member. In addition, even if Justice were looking at a potential loan guarantor, Gulick would not simply evaluate his friends and family but would evaluate the specific person or business that intended to guarantee the loan.
3. B Name lending is a qualitative technique that is sometimes used to take the place of financial
analysis. It is a technique used to evaluate the borrowers willingness to repay a financial obligation.
4. B Current measures used to evaluate credit risk include the firms probability of default, which
is the likelihood that a borrower will default; the loss given default, which represents the likely percentage loss if the borrower does default; the exposure at default; and the expected loss, which is, for a given time horizon, calculated as the product of the PD, LGD, and EAD. The stated 75% loss if the borrower defaults is the loss given default or LGD.
5. A Bank of the Plain States is insolvent because capital is wiped out. However, the bank has not failed because it is still operating with liquidity from the correspondent bank and the Federal Reserve. Therefore, the bank is insolvent but not failed.
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The following is a review of the Credit Risk Measurement and Management principles designed to address the learning objectives set forth by GARP. This topic is also covered in:
T h e C r e d i t A n a l y s t
Topic 17
E x a m F o c u s
This topic focuses on the role and tasks performed by a banking credit analyst. For the exam, understand the objectives of the analyst (e.g., risk management, investment selection) as well as the difference between primary and secondary research. In addition, know the quantitative and qualitative skills that an analyst must possess in order to be successful. Finally, be able to recognize and describe the key information sources used by credit analysts such as the annual report, auditors report, and company financial statements.
C r e d i t A n a l y s t R o l e s

LO 16.6: Describe quantitative measurements and factors o f credit risk, including probability o f default, loss given default, exposure at default, expected loss, and time horizon.
Credit risk, the likelihood that a borrower will repay a loan according to the loan agreement, and default risk, the probability that a borrower will default, are essentially the
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same because a default on a financial obligation almost always results in a loss to the lender. In the last decade, there have been significant changes in the financial sector. These changes, combined with regulatory changes in the industry, have resulted in a somewhat revised view of credit and default risks. Current measures used to evaluate creditworthiness are described as follows:
Probability of default (PD): The likelihood that a borrower will default is not necessarily the creditors greatest concern. A borrower may briefly default and then quickly correct the situation by making a payment, paying interest charges or penalties for missed payments. Creditors must rely on other measures of risk in addition to PD.
Loss given default (LGD): LGD represents the likely percentage loss if the borrower defaults. The severity of a default is equally as important to the creditor as the likelihood that the default would occur in the first place. If the default is brief and the creditor suffers no loss as a result, it is less of a concern than if the default is permanent and the creditor suffers significant losses. Both PD and LGD are expressed as percentages.
Exposure at default (EAD): The loss exposure may be stated as a dollar amount (e.g., the loan balance outstanding). EAD can also be stated as a percentage of the nominal amount of the loan or the maximum amount available on a credit line.
Expected loss (EL): Expected loss for a given time horizon is calculated as the product of the PD, LGD, and EAD (i.e., PD x LGD x EAD).
Time horizon: The longer the time horizon (i.e., the longer the tenor of the loan), the greater the risk to the lender and the higher the probability of default. Also, EAD and LGD change with time. The exposure (EAD) increases as the borrower draws on a credit line and falls as the loan is paid down. The LGD can also change as the terms of the loan or credit line change.
Expected loss generally depends on four variables: PD, LGD, EAD, and time horizon. A bank should also consider the correlations between various risk exposures when analyzing credit risk in a portfolio context.
Example: Calculating expected loss
Star City Bank and Trust has examined its loan portfolio over the past year. It has determined that the probability of default was 4%, adjusted for the size of the exposure. The loss given default over the period was 80%. Bank risk managers estimate that the exposure at default was 75% of the potential exposure. Calculate the expected loss given a one-year time horizon.
Answer:
expected loss = 4% x 80% x 75% = 2.4%
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Professors Note: It is straightforward to calculate PD, LGD, and EAD after the fact. As the previous example illustrates, a lender can analyze historical occurrences o f default, loss given default, and loss exposure. However, it is difficult to estimate these measures in advance. A financial institution or other nonhank lender can use historical experience to help predict future losses, but the forecast will not be perfect. Using historical mortgage loss data would have been little help in forecasting actual losses that occurred during the 20072009 financial crisis.
Fa i l u r e v s . I n s o l v e n c y

LO 16.5: Com pare the credit analysis o f consumers, corporations, financial institutions, and sovereigns.
Four basic types of borrowers for which credit analysis must be performed are as follows:
1. Consumers the analyst evaluates the creditworthiness of individuals.
2. Corporations the analyst evaluates the creditworthiness of nonfinancial firms.
Businesses are typically more difficult to analyze than individuals, although the process is similar.
3. Financial institutions the analyst evaluates the creditworthiness of financial
institutions, including banks and nonbank firms such as insurance companies and investment companies.
4. Government or government-related entities (i.e., sovereigns) the analyst evaluates
the creditworthiness of nations, government bodies, and municipalities. Non-state entities in specific locations or jurisdictions are also subject to analysis in the sovereign category.
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There are similarities and differences in the approaches taken to analyze the creditworthiness of the various groups. Figure 1 details some specific aspects of each type of analysis.
Figure 1: Comparison of Borrowers
Corporations Consumers Capacity Wealth (i.e., net Liquidity, cash worth), salary, or flow combined incoming cash per with earnings capacity and period, expenses profitability, per period, assets capital position such as houses and cars, amount (solvency), state of debt (e.g., of the economy, credit card debt), strength of the net cash available industry. to service debt (i.e., cash flow minus household and mortgage expenses).
W
illingness Reputation
of individual, payment history.
Quality of management, historical debt service.
Financial Institutions Similar to nonfinancial firms but bank specific. Liquidity (the banks access to cash to meet obligations), capital position, historical performance including earnings capacity over time (and ability to withstand financial stress), asset quality (affects the banks likelihood of being paid back and by extension the banks lenders likelihood of being paid back), state of the economy, strength of the industry. Quality of management; qualitative analysis is even more important for financial firms than for nonfinancial firms.
Sovereigns Financial factors including the countrys external debt load and debt relative to the overall economy; tax receipts are important.
Credit analysis for sovereigns is often more subjective than for financial and nonfinancial firms because the legal system and the enforcement of creditor rights is critical to the analysis. Sovereign legal risk ratings, as discussed previously, are often considered in the analysis.
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Figure 1: Comparison of Borrowers (Cont.)
Consumers
Corporations
Methods of evaluation
Loan size/type
Credit scoring models that consider income, duration of employment, and amount of debt for unsecured debt like credit cards. Credit scoring and some manual input and review for large exposures such as mortgage loans or automobile loans.
Large exposures are typically secured (e.g., mortgage loans). Smaller exposures are unsecured (e.g., credit card loans).
Financial Institutions Similar to nonfinancial firms.
Detailed manual analysis including financial statement analysis, interviews with management. More complex than consumer analysis because companies are so diverse in terms of assets, cash flow, financial structure, etc. Typically larger exposures (sometimes considerably larger) than firms (i.e., loans to consumers. Debt may be secured or unsecured.
large). Similar to
Sovereigns
Similar to financial and nonfinancial firms but with increased subjective analysis of the political environment. Similar to nonfinancial nonfinancial and
Similar to
financial firms (i.e., large).
The two primary differences between nonfinancial firm credit analysis and financial firm credit analysis are (1) the importance of the quality of assets in financial firms and (2) cash flow as an indicator of capacity to repay for nonfinancial firms but not a key indicator of creditworthiness for financial firms. It is clear from the 20072009 financial crisis that asset quality is a key indicator of a banks financial health. The ability to withstand financial stress is critical for a bank. That is why earnings capacity over time is a more relevant indicator of creditworthiness than cash flow. A bank must be able to withstand periods of financial stress/crisis in order to repay debts.
Professors Note: Sovereign credit analysis is not explicitly discussed in this topic. However, in contrast to consumers and financial and nonfinancial firms, consider the political issues/concerns that would arise when lending to a foreign government. Even a financially healthy sovereign may he a risky loan candidate due to the legal systems strength (or lack thereof); a lack of legal protections for creditors and other factors might negatively affect the lender and the lenders rights. I f you have to compare credit analysis across the four groups (i. e., consumers, nonfinancial firms, financial firms, and sovereigns), think about the differences between the groups and the various factors that explain and/or increase!decrease the lenders risk in each case.
Q u a n t i t a t i v e M e a s u r e s

LO 16.4: Compare and contrast quantitative and qualitative techniques o f credit risk evaluation.
The willingness to repay a loan is a subjective attribute. Lenders must make unverifiable judgments about the borrower. In some cases, intuition, or gut feelings, are necessary to conclude whether a borrower is willing to repay a loan. As such, qualitative credit analysis techniques are largely used to evaluate the borrowers willingness to repay. Qualitative techniques include:

Gather information from a variety of sources about the character and reputation of the potential borrower. Old-fashioned lending relied on first-hand knowledge of the people and businesses in a town. In this case, lenders knew (or thought they knew) potential borrowers. It is more difficult in the modern world, where lending decisions are centralized, to know customers personally. Face-to-face meetings with the potential borrower to assess the borrowers character are routine in evaluating willingness to pay. Name lending involves lending to an individual based on the perceived status of the individual in the business community. Some lenders substitute name lending for financial analysis. Extrapolating past performance into the future. Lenders often assume that a pattern of borrowing and repaying in the past (e.g., a credit record compiled from past history with the borrower and data garnered from credit bureaus) will continue in the future.

Historical lending norms relied on the moral obligation of borrowers who could pay to repay their debts. Thus, gauging the borrowers willingness to pay was a critical component of credit analysis. However, in modern society, the moral obligation to pay if one is capable of paying has been replaced by the legal obligation to pay. In other words, in terms of credit analysis, determining the capacity to pay is more important than determining the willingness to pay because the legal system will force those who can pay to honor their commitment. Courts can seize the assets of those who will not fulfill their financial obligations. In corrupt or ineffective states, a borrower will not suffer, even if able to pay but not doing so.
The willingness to pay is more important in countries with less-developed financial markets and legal systems. Creditors must evaluate the legal system and the strength of creditors rights in emerging markets, along with the prospective borrowers ability and willingness to repay the obligation. This is a qualitative endeavor. Sovereign risk ratings may be used to evaluate the quality of a countrys legal system and, by extension, the legal risk associated with the country or region. The lower the score, the greater the legal risk. For example, in 2010, Finland had a Rule of Law Index of 1.97, the United States had a rating of 1.58, Brazil had a rating of 0.0, and Somalia had a rating of 2.43. However, even in countries with robust legal systems such as Finland and the United States, the creditor must also consider the costs associated with taking legal action against a delinquent borrower. If costs are high, the creditor may be unwilling to take action regardless of the strength of the enforcement of creditor rights. As such, the willingness to pay should never be completely ignored in credit analysis.
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The ability of a borrower to repay a loan is an objective attribute. Quantitative credit analysis techniques are largely used to evaluate the borrowers ability to repay The primary quantitative technique used in financial analysis is examining the past, current, and forecasted financial statements of the prospective borrower. This forms the core of the quantitative credit analysis used to determine a borrowers capacity to meet its financial obligations. There are limitations associated with quantitative data, which include:
Historical nature of the data. Financial data is typically historical and thus may not be up-to-date or representative of the future. Also, forecasted financial data is notoriously unreliable and susceptible to miscalculations and/or misrepresentations.
Difficult to make accurate projections using historical data. Financial statements
attempt to represent the economic reality of a firm in a highly abbreviated report. As such, some information is lost in translation that is critical to the loan decision. The rules guiding financial reporting are created by a diverse group with varying interests and are often decided by compromise. Also, firms are given discretion regarding what and how they report financial information, subject to established accounting rules. Firms may use the latitude in financial reporting to deceive interested parties. Even if the reports are accurate, financial data is subject to interpretation. There can be a range of conclusions drawn from the same data due to the variety of needs, perspectives, and experiences of the various analysts. This means there is a subjective, qualitative component to an objective, quantitative exercise.
Given the shortcomings of financial reporting, lenders should not ignore qualitative analysis. The quality of management, the motivation of the firms management, and the incentives of management are relevant for both nonfinancial and financial firms. Even quantitative analysis is subject to interpretation. In fact, many would argue that financial analysis is much more of an art than a science. Judgment is as important as the quantitative analysis supporting it. The most effective analysis combines quantitative assessments with qualitative judgments.
C r e d i t A n a l y s i s C o m p a r i s o n

LO 16.3: Describe, compare and contrast various credit risk mitigants and their role in credit analysis.
The four primary components of credit risk evaluation are as follows.
1. The borrowers (or obligors) capacity and willingness to repay the loan. Questions the
lender must consider include: What is the financial capacity to pay?
Is it likely the borrower can fulfill its financial obligations through the maturity of the loan?
Are there outside forces that affect the borrowers capacity and/or willingness to pay?
For example, does the ownership structure of the firm, relationships within and outside the firm, and other obligations of the firm affect the borrowers ability to pay?
How does the business itself affect the borrowers capacity to pay? Are there credit risk characteristics tied to this particular industry or sector? Does the firm have a niche within the industry or sector?
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2. The external environment and its effect on the borrowers capacity and willingness to repay the borrowed funds. Factors such as the business climate, country risk, and operating conditions are relevant to the lender. Are there cyclical changes that will affect the level of credit risk? Will political risks affect the likelihood of repayment?
3. The characteristics of the credit instrument. The credit instrument might be a bond issue, a bank loan, a loan from a finance company, trade credit, or other type of debt agreement/security. Concerns include: Risk characteristics that are inherent in the credit instrument, including legal risks
and obligations that are specific to the instrument.
The maturity (also called tenor) of the instrument.
Is the debt secured or unsecured? Is there collateral backing the loan? Are there loan guarantors? Is the debt subordinated or senior to other obligations? What is the priority assigned to the creditor? Flow do loan/bond covenants increase or decrease the credit risk for each party? Can the borrower repay the loan early without penalty? Can the lender call the loan? Can the security be converted to another form (e.g., a convertible bond)?

What is the denominated currency of the obligation? Are there any contingent risks?
4. The quality and adequacy of risk mitigants such as collateral, credit enhancements, and loan guarantees. Secured lending (i.e., using risk mitigants in the lending process) is generally the preferred method of lending. If there is collateral, a bank or other lender may not have to force a delinquent borrower into bankruptcy but may instead sell the collateral to satisfy the financial obligation. Secured lenders are also generally in a better position than unsecured lenders in the event of bankruptcy. The use of collateral not only mitigates losses in the event of default, but also lowers the probability of default because the obligor typically does not want to lose the collateral. Flistorically, banks have substituted collateral for analysis of the borrowers ability to pay. In some sense, the use of collateral eliminates the need for credit analysis, or at the very least makes the credit decision simpler. A lender can normally put a market value on collateral and determine if it is sufficient to cover potential losses. Three issues regarding risk mitigants include:

Is the collateral pledged to, or likely to be pledged to, another loan? lias there been an estimation of the value of the collateral? If there is a loan guarantor, has there been sufficient credit analysis of the third Is the collateral pledged to, or likely to be pledged to, another loan? lias there been an estimation of the value of the collateral? If there is a loan guarantor, has there been sufficient credit analysis of the third partys willingness and ability to pay in the event the borrower does not pay? A guarantor accepts liability for debt if the primary borrower defaults. The bank is able to substitute analysis of the guarantors creditworthiness for that of the primary borrower. Typically, the guarantor has a greater ability to pay than the primary borrower (e.g., a parent guaranteeing a childs car loan or a parent company guaranteeing a loan to a subsidiary).
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Q u a l it a t iv e a n d Q u a n t it a t iv e Te c h n iq u e s

LO 16.2: Explain the components o f credit risk evaluation.

LO 16.1: Define credit risk and explain how it arises using examples.
Credit is an agreement where one party receives something of value and agrees to pay for the good or service at a later date. The word credit is derived from the ancient Latin word credere, which means to believe or to entrust. The creditor must have knowledge of the borrowers character and reputation as well as his financial condition. Generally, there is not a definitive yes or no answer to whether a borrower can and will pay back a loan. As such, the lender must address the question of likelihood. The lender must assess the likelihood that the borrower will pay back the loan in accordance with the terms of the agreement.
Professors Note: Borrower, obligor, counterparty, and issuer are all used to signify the party receiving credit. Lender, creditor, and obligee are primarily used to signify the party granting credit.
Credit risk is the probability that a borrower will not pay back a loan in accordance with the terms of the credit agreement. The risk can result from:
Default on a financial obligation. An increased probability of default on a financial obligation. A more severe loss than expected due to a greater than expected exposure at the time of a
default.
A more severe loss than expected due to a lower than expected recovery at the time of a
default.
Default on payment for goods or services already rendered (i.e., settlement risk).
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Credit risk arises in many personal and business contexts. In fact, nearly all businesses, except small firms that confine their businesses to cash and carry transactions (i.e., a good or service is exchanged simultaneously for cash), incur credit risk. Specific contexts in which credit risks arise include:
A person or company performs a service and sends a bill for payment of the rendered service. For example, a car dealership fixes a persons car and then bills the customer, giving the customer 30 days to pay the bill in full without incurring financing charges. A party pays in advance for goods or services and awaits receipt of the goods or services
(i.e., the settlement of a transaction). For example, a university pays in advance for computer training for its staff and faculty and then receives the training over the course of the following year.
A person or company has provided a product and is awaiting payment for the item.
Trade credit is an example of this type of transaction. The firm selling the product offers terms of credit, allowing the purchaser a reasonable period of time to pay the invoice. Big-ticket items are almost exclusively sold on credit. For example, a chemistry firm buys several powerful microscopes from a supplier and is allowed to pay the full balance in 30 days.
There are two types of risks associated with these transactions. There is settlement risk, the risk that the counterparty will never pay for the good or service, and a more fundamental financial obligation that arises from the loan agreement. Credit risk that arises from trade credit is nearly indistinguishable from the credit risk that banks incur. Financial analysis must be performed in both cases to increase the likelihood that the borrower will fulfill the financial obligation. Banks cannot avoid credit risk; it is central to their business. There is no cash and carry model in banking. Banks accept money from depositors and other sources and lend the money to individuals and firms. Because banks cannot avoid credit risk, they must manage the risk through credit analysis and the use of risk mitigants such as collateral and loan guarantees.
C r e d i t R i s k E v a l u a t i o n C o m p o n e n t s

LO 15.9: Explain the im pact o f a single asset price jum p on a volatility smile.
Price jumps can occur for a number of reasons. One reason may be the expectation of a significant news event that causes the underlying asset to move either up or down by a large amount. This would cause the underlying distribution to become bimodal, but with the same expected return and standard deviation as a unimodal, or standard, price-change distribution.
Implied volatility is affected by price jumps and the probabilities assumed for either a large up or down movement. The usual result, however, is that at-the-money options tend to have a higher implied volatility than either out-of-the-money or in-the-money options. Away-from-the-money options exhibit a lower implied volatility than at-the-money options. Instead of a volatility smile, price jumps would generate a volatility frown, as in Figure 3.
Figure 3: Volatility Smile (Frown) With Price Jump Implied volatility
Strike price
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K e y C o n c e p t s
LO 15.1
When option traders allow implied volatility to depend on strike price, patterns of implied volatility resemble volatility smiles.
LO 15.2
Put-call parity indicates that the deviation between market prices and Black-Scholes-Merton prices will be equivalent for calls and puts. Hence, implied volatility will be the same for calls and puts.
LO 15.3
Currency traders believe there is a greater chance of extreme price movements than predicted by a lognormal distribution. Equity traders believe the probability of large down movements in price is greater than large up movements in price, as compared with a lognormal distribution.
LO 15.4
The volatility pattern used by traders to price currency options generates implied volatilities that are higher for deep in-the-money and deep out-of-the-money options, as compared to the implied volatility for at-the-money options.
LO 15.5
The volatility smile exhibited by equity options is more of a smirk, with implied volatility higher for low strike prices. This has been attributed to leverage and crashophobia effects.
LO 15.6
Alternative methods to studying volatility patterns include: replacing strike price with strike price divided by stock price, replacing strike price with strike price divided by the forward price for the underlying asset, and replacing strike price with option delta.
LO 15.7
Volatility term structures and volatility surfaces are used by traders to judge consistency in model-generated option prices.
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LO 15.8
Volatility smiles that are not flat require the use of implied volatility functions or trees to correctly calculate option Greeks.
LO 15.9
Price jumps may generate volatility frowns instead of smiles.
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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 market price deviations for puts and calls from Black-Scholes-Merton prices indicate: A. equivalent put and call implied volatility. B. equivalent put and call moneyness. C. unequal put and call implied volatility. D. unequal put and call moneyness.
An empirical distribution that exhibits a fatter right tail than that of a lognormal distribution would indicate: A. equal implied volatilities across low and high strike prices. B. greater implied volatilities for low strike prices. C. greater implied volatilities for high strike prices. D. higher implied volatilities for mid-range strike prices.
the same across maturities for given strike prices. the same for short time periods. The sticky strike rule assumes that implied volatility is: A. B. C. the same across strike prices for given maturities. D. different across strike prices for given maturities.
Compared to at-the-money currency options, out-of-the-money currency options exhibit which of the following volatility traits? A. Lower implied volatility. B. A frown. C. A smirk. D. Higher implied volatility.
Which of the following regarding equity option volatility is true? A. There is higher implied price volatility for away-from-the-money equity options. B. Crashophobia suggests actual equity volatility increases when stock prices
decline.
C. Compared to the lognormal distribution, traders believe the probability of large
down movements in price is similar to large up movements.
D. Increasing leverage at lower equity prices suggests increasing volatility.
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C o n c e p t C h e c k e r An s w e r s
1. A Put-call parity indicates that the implied volatility of a call and put will be equal for the same
strike price and time to expiration.
2. C An empirical distribution with a fat right tail generates a higher implied volatility for higher
strike prices due to the increased probability of observing high underlying asset prices. The pricing indication is that in-the-money calls and out-of-the-money puts would be expensive.
3. B The sticky strike rule, when applied to calculating option sensitivity measures, assumes
implied volatility is the same over short time periods.
4. D Away-from-the-money currency options have greater implied volatility than at-the-money
options. This pattern results in a volatility smile.
5. D There is higher implied price volatility for low strike price equity options. Crashophobia is based on the idea that large price declines are more likely than assumed in Black-Scholes- Merton prices, not that volatility increases when prices decline. Compared to the lognormal distribution, traders believe the probability of large down movements in price is higher than large up movements. Increasing leverage at lower equity prices suggests increasing volatility.
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10 Q u e stio n s: 3 0 M in u te s
1.
2.
3.
4.
An analyst for Z Corporation is determining the value at risk (VaR) for the corporations profit/loss distribution that is assumed to be normally distributed. The profit/loss distribution has an annual mean of $3 million and a standard deviation of $3.3 million. Using a parametric approach, what is the VaR with a 99% confidence level? A. $0,775 million. B. $3,155 million. C. $5,775 million. D. $8,155 million.
The Basel Committee requires backtesting of actual losses to VaR calculations. How many exceptions would need to occur in a 250-day trading period for the capital multiplier to increase from three to four? two to five. A. B. five to seven. C. seven to nine. D. ten or more.
The top-down approach to risk aggregation assumes that a banks portfolio can be cleanly subdivided according to market, credit, and operational risk measures. In contrast, a bottom-up approach attempts to account for interactions among various risk factors. In order to assess which approach is more appropriate, academic studies evaluate the ratio of integrated risks to separate risks. Regarding studies of top-down and bottom-up approaches, which of the following statements is incorrect? A. Top-down studies suggest that risk diversification is present. B. Bottom-up studies sometimes calculate the ratio of integrated risks to separate
risks to be less than one.
C. Bottom-up studies suggest that risk diversification should be questioned. D. Top-down studies calculate the ratio of integrated risks to separate risks to be
greater than one.
Commercial Bank Z has a $3 million loan to company A and a $3 million loan to company B. Companies A and B each have a 5% and 4% default probability, respectively. The default correlation between companies A and B is 0.6. What is the expected loss (EL) for the commercial bank under the worst case scenario? a. b. c. d.
$83,700. $133,900. $165,600. $233,800.
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5.
6.
7.
8.
A risk manager should always pay careful attention to the limitations and advantages of applying financial models such as the value at risk (VaR) and Black-Scholes- Merton (BSM) option pricing model. Which of the following statements regarding financial models is correct? a. Financial models should always be calibrated using most recent market data
because it is more likely to be accurate in extrapolating trends.
b. When applying the VaR model, empirical studies imply asset returns closely
follow the normal distribution.
c. The Black-Scholes-Merton option pricing model is a good example of
the advantage of using financial models because the model eliminates all mathematical inconsistences that can occur with human judgment.
d. A good example of a limitation of a financial model is the assumption of
constant volatility when applying the Black-Scholes-Merton (BSM) option pricing model.
Assume that a trader wishes to set up a hedge such that he sells $100,000 of a Treasury bond and buys TIPS as a hedge. Using a historical yield regression framework, assume the DV01 on the T-bond is 0.072, the DV01 on the TIPS is 0.051, and the hedge adjustment factor (regression beta coefficient) is 1.2. What is the face value of the offsetting TIPS position needed to carry out this regression hedge? A. $138,462. B. $169,412. C. $268,499. D. $280,067.
A constant maturity Treasury (CMT) swap pays ($1,000,000 / 2) x (yCMT 9%) every six months. There is a 70% probability of an increase in the 6-month spot rate and a 60% probability of an increase in the 1 -year spot rate. The rate change in all cases is 0.50% per period, and the initial yCMT is 9%. What is the value of this CMT swap? A. $2,325. B. $2,229. C. $2,429. D. $905.
Suppose the market expects that the current 1-year rate for zero-coupon bonds with a face value of $1 will remain at 5%, but the 1-year rate in one year will be 3%. What is the 2-year spot rate for zero-coupon bonds? A. 3.995%. B. 4.088%. C. 4.005%. D. 4.115%.
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9.
An analyst is modeling spot rate changes using short rate term structure models. The current short-term interest rate is 5% with a volatility of 80bps. After one month passes the realization of dw, a normally distributed random variable with mean 0 and standard deviation Vdt, is -0.5. Assume a constant interest rate drift, \ , of 0.36%. What should the analyst compute as the new spot rate? A. 5.37%. B. 4.63%. C. 5.76%. D. 4.24%.
10. Which of the following statements is incorrect regarding volatility smiles?
A. Currency options exhibit volatility smiles because the at-the-money options have
higher implied volatility than away-from-the-money options.
B. Volatility frowns result when jumps occur in asset prices. C. Equity options exhibit a volatility smirk because low strike price options have
greater implied volatility.
D. Relative to currency traders, it appears that equity traders expectations of
extreme price movements are more asymmetric.
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1. B The population mean and standard deviations are unknown; therefore, the standard normal
z-value of 2.33 is used for a 99% confidence level.
VaR(l%) = -5.0 million + ($3.5 million)(2.33) = -5.0 million + 8.155 million = 3.155 million (See Topic 1)
2. D Ten or more backtesting violations require the institution to use a capital multiplier of four.
(See Topic 3)
3. D Top-down studies calculate this ratio to be less than one, which suggests that risk
diversification is present and ignored by the separate approach. Bottom-up studies also often calculate this ratio to be less than one; however, this research has not been conclusive, and has recently found evidence of risk compounding, which produces a ratio greater than one. Thus, bottom-up studies suggests that risk diversification should be questioned. (See Topic 5)
4. C The default probability of company A is 5%. Thus, the standard deviation for company A is:
^0.05(1 0.05) = 0.2179
Company B has a default probability of 4% and, therefore, will have a standard deviation of 0.1960. We can now calculate the expected loss under the worst case scenario where both companies A and B are in default. Assuming that the default correlation between A and B is 0.6, the joint probability of default is:
P(AB) = 0.6^0.05(0.95) x 0.04(0.96) + 0.05 x 0.04 = 0.6V0.001824 + 0.002 = 0.0276
Thus, the expected loss for the commercial bank is $165,600 (= 0.0276 x $6,000,000). (See Topic 6)
5. D The Black-Scholes-Merton (BSM) option pricing model assumes strike prices have a
constant volatility. However, numerous empirical studies find higher volatility for out-of- the-money options and a volatility skew in equity markets. Thus, this is a good example of a limitation of financial models. The choice of time period used to calibrate the parameter inputs for the model can have a big impact on the results. Risk managers used volatility and correlation estimates from pre-crisis periods during the recent financial crisis, and this resulted in significantly underestimating the risk for financial models. All financial models should be stress tested using scenarios of extreme economic conditions. VaR models often assume asset returns have a normal distribution. However, empirical studies find higher kurtosis in return distributions. High kurtosis implies a distribution with fatter tails than the normal distribution. Thus, the normal distribution is not the best assumption for the underlying distribution. Financial models contain mathematical inconsistencies. For example, in applying the BSM option pricing model for up-and-out calls and puts and down-and-out calls and puts, there are rare cases where the inputs make the model insensitive to changes in implied volatility and option maturity. (See Topic 8)
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6. B Defining
and P * as the face amounts of the real and nominal bonds, respectively, and
their corresponding DVOls as DV01R and DV01N, a DV01 hedge is adjusted by the hedge adjustment factor, or beta, as follows:
RF = 100,000 x
xl.2 = 169,412
‘ d v o i n ‘ X 0 R DV01 \ / 0.072 .051 J
/ V0
FR = FN x
(See Topic 10)
7. A The payoff in each period is ($1,000,000 / 2) x (yCMT – 9%). For example, the 1-year payoff of $5,000 in the figure below is calculated as ($1,000,000 / 2) x (10% – 9%) = $5,000. The other numbers in the year one cells are calculated similarly.
In six months, the payoff if interest rates increase to 9.50% is ($1,000,000 / 2 ) x (9.5% – 9.0%) = $2,500. Note that the price in this cell equals the present value of the probability weighted 1 -year values plus the 6-month payoff:
months, U
($5,000×0.6)+ ($0x0.4)
+ 0.095 1
+ $2,500 = $5,363.96
The other cell value in six months is calculated similarly and results in a loss of $4,418.47.
The value of the CMT swap today is the present value of the probability weighted 6-month values:
($5,363.96 x 0.7) + (-$4,418.47 x 0.3)
+ 0.09 1
$2,324.62
yCMT=10% Price = $5,000
yCMT = 9 0 /0 Price = $0
yCMT = 8% Price = -$5,000
yCMT= 8*5% Price = -$4,418
T od ay
6 m on th s
1 year
Thus the correct response is A. The other answers are incorrect because they do not correctly discount the future values or omit the 6-month payoff from the 6-month values.
(See Topic 11)
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8. A The 2-year spot rate is computed as follows:
f (2) = 2/(1.05) (1.03) – 1 = 3.995%
(See Topic 12)
9. B This short rate process has an annualized drift of 0.36%, so it requires the use of Model 2
(with constant drift). The change in the spot rate is computed as:
dr = Xdt + adw
dr = (0.36% / 12) + (0.8% x -0.5) = -0.37% = -37 basis points
Since the initial short-term rate was 5% and dr is -0.37%, the new spot rate in one month
5% – 0.37% = 4.63% (See Topic 13)
10. A Currency options exhibit volatility smiles because the at-the-money options have lower
implied volatility than away-from-the-money options.
Equity traders believe that the probability of large price decreases is greater than the probability of large price increases. Currency traders beliefs about volatility are more symmetric as there is no large skew in the distribution of expected currency values (i.e., there is a greater chance of large price movements in either direction).
(See Topic 15)
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delta-normal VaR: VaR(a%) = (p,r + a r x za ) x Pt_1
lognormal VaR: VaR(a%) = Pt_1 x ^1 e^R aRXZa j
standard error of a quantile: se (q)
V p ( l- p ) /n
f(q)
Topic 2
age-weighted historical simulation: w(i)
x ^ q – x )
l – X ”
Topic 3
model accuracy test: z
x – p T
V p (l- p )T
unconditional coverage test statistic:
LR = 2ln[(1 – p)TNpN] + 2ln{ [1 – (N/T)]t -n (N/T)nJ
Topic 4
V(Rp) is variance of portfolio return: V(Rp) = (3p x V(Rjyj) +
N
i= l
x CT,i
General market risk: (3p x V (Rm)
Specific risk:
N
i=l
wf x
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\ /
/
pt + p t v
pt-i
geometric return: R t = In
i
. i arithmetic return: rt = —————- = ———— 1
P t+ ^ t
Pt-1
p t- i
p t- i
F o r m u l a s
Topic 1
profit/loss data: P/Lt = P + D t Pt l
M arket R isk M easurem ent and M anagem ent
Book 1 Formulas
Undiversified VaR =
N
i=l
x Vj
Diversified VaR
a J x ‘ ^ x = ^ (x x V )’R (x x V )
Topic 6
portfolio mean return: pp = wxpx + wYPy
V 2 2
W
XCTX T Wytty + 2wyW y C O V y y
2 2
covariance: cov^y
n E ( X t – f e ) ( Y t – ^ Y) t=l__________________
n 1
correlation: PxY
CQVXY CTxCTy
realized correlation: Prealized
ZX
2 n – n i>] correlation swap payoff: notional amount x (preaBzej Pfixecj)
joint probability of default: P(AB) = pAB ^/PDA(1 PDa ) x PDb(1 PDB) + PDA x PDB
Topic 7
mean reversion rate: St S
j = a([i S
j)
autocorrelation: AC(pt,pt_i)
cov(pt,pt- 1) ff(pt)xof(Pt-i)
Topic 8
correlation with expectation values: PxY
E(XY) – E(X)E( Y)
E(X2)-(E (X ))2
E(Y2)-(E (Y ))2
n
i=l
n(n2 1)
Spearmans rank correlation: Ps
1 –
Kendalls t : t
n c ~ n d n(n 1) / 2
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Book 1 Formulas
Topic 12
2- year spot rate: r(2) = ^ (l + r1)(l +
~ 1
3- year spot rate: r (3) = ^ (l + q ) (l + ^ ) (l + ) 1
Jensens inequality: E
1 (i+0
1
> E[l + r
Topic 13
Model 1:
dr = crdw = annual basis-point volatility of rate changes where: dr = change in interest rates over small time interval, dt dt = small time interval (measured in years) o r = annual basis-point volatility of rate changes dw = normally distributed random variable with mean 0 and standard deviation Vdt
Model 2: dr = \d t + crdw
Vasicek model:
dr = k(0 – r)dt + crdw
where: k 0 r
= a parameter that measures the speed of reversion adjustment = long-run value of the short-term rate assuming risk neutrality = current interest rate level
long-run value of short-term rate:
X A
0 rj H k
where: ri = the long-run true rate of interest
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Book 1 Formulas
Topic 14
Model 3:
dr = \(t)dt + cre-atdw
where: a = volatility at t = 0, which decreases exponentially to 0 for a > 0
CIR model: dr = k(0 r)dt + a Vr dw
Model 4: dr = ardt + crrdw
Topic 13
put-call parity: c p = S PV(X)
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U s in g t h e C u m u l a t iv e Z-Ta b l e
Probability Example
Assume that the annual earnings per share (EPS) for a large sample of firms is normally distributed with a mean of $5.00 and a standard deviation of $1.50. What is the approximate probability of an observed EPS value falling between $3.00 and $7.25?
If EPS = x = $7.25, then z = (x – p)/a = ($7.25 – $5.00)/$1.50 = +1.50
If EPS = x = $3.00, then z = (x – p)/a = ($3.00 – $5.00)/$1.50 = -1.33
For z-value of 1.50: Use the row headed 1.5 and the column headed 0 to find the value 0.9332. This represents the area under the curve to the left of the critical value 1.50.
For z-value of1.33: Use the row headed 1.3 and the column headed 3 to find the value 0.9082. This represents the area under the curve to the left of the critical value +1.33. The area to the left o f1.33 is 1 0.9082 = 0.0918.
The area between these critical values is 0.9332 0.0918 = 0.8414, or 84.14%.
Hypothesis Testing One-Tailed Test Example
A sample of a stocks returns on 36 non-consecutive days results in a mean return of 2.0%. Assume the population standard deviation is 20.0%. Can we say with 95% confidence that the mean return is greater than 0%?
H q: p < 0.0%, Ha : p > 0.0%. The test statistic = ^-statistic = = (2.0 – 0.0) / (20.0 / 6) = 0.60.
x-po
The significance level = 1.0 0.95 = 0.05, or 5%.
Since this is a one-tailed test with an alpha of 0.05, we need to find the value 0.95 in the cumulative stable. The closest value is 0.9505, with a corresponding critical .z-value of 1.65. Since the test statistic is less than the critical value, we fail to reject H Q.
Hypothesis Testing Two-Tailed Test Example
Using the same assumptions as before, suppose that the analyst now wants to determine if he can say with 99% confidence that the stocks return is not equal to 0.0%.
H q: p = 0.0%, Ha : p ^ 0.0%. The test statistic (z-value) = (2.0 0.0) / (20.0 / 6) = 0.60. The significance level = 1.0 0.99 = 0.01, or 1%.
Since this is a two-tailed test with an alpha of 0.01, there is a 0.005 rejection region in both tails. Thus, we need to find the value 0.995 (1.0 0.005) in the table. The closest value is 0.9951, which corresponds to a critical .z-value of 2.58. Since the test statistic is less than the critical value, we fail to reject H Q and conclude that the stocks return equals 0.0%.
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C u m u l a t iv e Ta b l e P(Z < z) = N(z) for z > 0 P(Z < -z) = 1 - N(z) z 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 0 0.01 0.02 0.5000 0.5040 0.5080 0.03 0.5120 0.04 0.5160 0.05 0.06 0.07 0.08 0.09 0.5199 0.5239 0.5279 0.5319 0.5359 0.5398 0.5438 0.5478 0 .5 5 1 7 0 .5 5 5 7 0 .5 5 9 6 0 .5 6 3 6 0.5675 0 .5 7 9 3 0 .5 8 3 2 0 .6 1 7 9 0 .6 2 1 7 0 .6 5 5 4 0.6591 0.5871 0.6255 0.6628 0.5910 0.5948 0 .5 9 8 7 0 .6 0 2 6 0 .6 0 6 4 0 .6 2 9 3 0.6331 0.6368 0 .6 4 0 6 0.6443 0.6664 0.6 7 0 0 0 .6 7 3 6 0 .6 7 7 2 0.6808 0.6 8 4 4 0 .5 7 1 4 0.6103 0.6480 0.5753 0.6141 0 .6 5 1 7 0 .6 8 7 9 0.6915 0.6950 0.6985 0.7019 0 .7 0 5 4 0 .7 0 8 8 0.7123 0 .7 1 5 7 0 .7 1 9 0 0 .7 2 2 4 0 .7 2 5 7 0.7291 0 .7 3 2 4 0 .7 3 5 7 0 .7 3 8 9 0 .7 4 2 2 0 .7 4 5 4 0 .7 4 8 6 0 .7 5 1 7 0 .7 5 4 9 0.7580 0.7611 0 .7 6 4 2 0.7673 0 .7 7 0 4 0 .7 7 3 4 0 .7 7 6 4 0 .7 7 9 4 0.7823 0 .7 8 5 2 0.7881 0.7910 0 .7 9 3 9 0 .7 9 6 7 0.7995 0.8023 0.8051 0 .8 0 7 8 0 .8 1 0 6 0.8133 0.8159 0 .8 1 8 6 0 .8 2 1 2 0 .8 2 3 8 0 .8 2 6 4 0.8289 0.8315 0.8340 0.8365 0.8389 0.8413 0.8438 0.8461 0.8485 0.8508 0.8531 0 .8 5 5 4 0 .8 5 7 7 0 .8 5 9 9 0.8621 0.8643 0.8665 0 .8 6 8 6 0.8708 0 .8 7 2 9 0 .8 7 4 9 0.8770 0.8790 0.8810 0.8830 0 .8 8 4 9 0 .8 8 6 9 0.8888 0 .8 9 0 7 0.8925 0 .8 9 4 4 0 .8 9 6 2 0 .8 9 8 0 0 .9 0 6 6 0 .9 0 8 2 0 .9 0 9 9 0.9115 0.9131 0 .9 1 4 7 0 .9 2 2 2 0 .9 2 3 6 0.9251 0.9265 0.9279 0 .9 2 9 2 0 .9 3 0 6 0 .8 9 9 7 0 .9 1 6 2 0.9015 0 .9 1 7 7 0.9319 0 .9 0 3 2 0 .9 1 9 2 0 .9 0 4 9 0 .9 2 0 7 0 .9 3 3 2 0 .9 4 5 2 0.9345 0.9463 0 .9 3 5 7 0 .9 3 7 0 .9 3 8 2 0 .9 3 9 4 0 .9 4 0 6 0.9418 0.9429 0.9441 0 .9 4 7 4 0 .9 4 8 4 0.9495 0.9505 0.9525 0.9535 0.9545 0.9515 0.9608 0 .9 5 5 4 0 .9 5 6 4 0 .9 5 7 3 0 .9 5 8 2 0.9591 0 .9 5 9 9 0.9616 0.9625 0.9641 0 .9 6 4 9 0 .9 6 5 6 0 .9 6 6 4 0.9671 0.9678 0.9686 0.9693 0 .9 6 9 9 0.9713 0 .9 7 1 9 0 .9 7 2 6 0 .9 7 3 2 0 .9 7 3 8 0 .9 7 4 4 0 .9 7 5 0 0 .9 7 5 6 0.9761 0 .9 7 7 2 0.9778 0.9783 0.9788 0.9793 0.9798 0.9803 0 .9 8 0 8 0 .9 8 1 2 0.9821 0.9861 0.9893 0 .9 8 2 6 0.983 0 .9 8 3 4 0.9838 0.9 8 4 2 0 .9 8 4 6 0.985 0 .9864 0.9868 0.9896 0.9898 0.9871 0.9901 0.9875 0 .9 9 0 4 0.9878 0.9881 0 .9 8 8 4 0 .9 9 0 6 0.9909 0.9911 0 .9 8 5 4 0 .9 8 8 7 0.9913 0.9633 0 .9 7 0 6 0 .9 7 6 7 0 .9 8 1 7 0 .9 8 5 7 0 .9 8 9 0 .9 9 1 6 0.9918 0.9920 0.9922 0.9925 0 .9 9 2 7 0 .9 9 2 9 0.9931 0 .9 9 3 2 0 .9 9 3 4 0 .9 9 3 6 0.9938 0 .9 9 5 3 0.9965 0 .9 9 7 4 0.9981 0 .9 9 4 0.9955 0 .9 9 6 6 0.9975 0 .9 9 8 2 0.9941 0 .9 9 5 6 0 .9 9 6 7 0 .9 9 7 6 0 .9 9 8 2 0.9943 0 .9 9 5 7 0.9968 0 .9 9 7 7 0.9983 0.9945 0 .9 9 5 9 0.9969 0 .9 9 7 7 0 .9 9 8 4 0.9960 0.9970 0.9978 0 .9 9 8 4 0.9949 0 .9 9 6 2 0 .9 9 7 2 0.9961 0.9971 0 .9 9 7 9 0 .9 9 7 9 0.9951 0.9963 0.9973 0.9980 0 .9 9 5 2 0 .9 9 6 4 0 .9 9 7 4 0.9981 0.9985 0.9985 0 .9 9 8 6 0 .9 9 8 6 0.9946 0.9948 0 .9 9 8 7 0 .9 9 8 7 0 .9 9 8 7 0.9988 0.9988 0.9989 0.9989 0.9989 0 .9 9 9 0 0 .9 9 9 0 Page 210 2018 Kaplan, Inc. 0.00 0.0000 0.01 0.02 0 .0 0 4 0 0 .0 0 8 0 0.03 0 .0 1 2 0 0.04 0 .0 1 6 0 0.05 0.0199 0.06 0.07 0.08 0.09 0.0239 0.0279 0.0319 0.0359 0.0398 0.0438 0.0478 0 .0 5 1 7 0 .0 5 5 7 0 .0 5 9 6 0 .0 6 3 6 0.0675 0 .0 7 9 3 0 .0 8 3 2 0 .1 1 7 9 0 .1 2 1 7 0 .1 5 5 4 0.1591 0.0871 0.1255 0.1628 0.0910 0.0948 0 .0 9 8 7 0 .1 0 2 6 0 .1 0 6 4 0 .1 2 9 3 0.1331 0.1368 0 .1 4 0 6 0.1443 0.1664 0.1700 0 .1 7 3 6 0 .1 7 7 2 0.1808 0.1 8 4 4 0 .0 7 1 4 0.1103 0.1480 0.0753 0.1141 0 .1 5 1 7 0 .1 8 7 9 0.1915 0 .1 9 5 0 0.1985 0.2019 0 .2 0 5 4 0 .2 0 8 8 0.2123 0 .2 1 5 7 0 .2 1 9 0 0 .2 2 2 4 0 .2 2 5 7 0.2291 0 .2 3 2 4 0 .2 3 5 7 0 .2 3 8 9 0 .2 4 2 2 0 .2 4 5 4 0 .2 4 8 6 0 .2 5 1 7 0 .2 5 4 9 0 .2 5 8 0 0.2611 0 .2 6 4 2 0.2673 0 .2 7 0 4 0 .2 7 3 4 0 .2 7 6 4 0 .2 7 9 4 0.2823 0 .2 8 5 2 0.2881 0.2910 0 .2 9 3 9 0 .2 9 6 7 0.2995 0.3023 0.3051 0.3078 0.3106 0.3159 0 .3 1 8 6 0 .3 2 1 2 0.3238 0.3 2 6 4 0.3289 0.3315 0 .3 3 4 0 0 .3 3 5 6 0.3133 0 .3 3 8 9 0.3413 0.3643 0.3438 0.3461 0.3485 0.3508 0.3531 0 .3 5 5 4 0 .3 5 7 7 0 .3 5 9 9 0.3621 0.3665 0 .3 6 8 6 0.3708 0 .3 7 2 9 0 .3 7 4 9 0.3770 0.3790 0.3810 0.3830 0 .3 8 4 9 0 .3 8 6 9 0.3888 0 .3 9 0 7 0.3925 0 .3 9 4 4 0 .3 9 6 2 0 .3 9 8 0 0 .4 0 6 6 0 .4 0 8 2 0 .4 0 9 9 0.4115 0.4131 0 .4 1 4 7 0 .4 2 2 2 0 .4 2 3 6 0.4251 0.4265 0.4279 0 .4 2 9 2 0 .4 3 0 6 0 .3 9 9 7 0 .4 1 6 2 0.4015 0 .4 1 7 7 0.4319 0 .4 3 5 7 0 .4 3 7 0 0 .4 3 8 2 0 .4 3 9 4 0 .4 4 0 6 0.4418 0.4429 0.4441 0 .4 4 7 4 0 .4 4 8 4 0.4495 0.4505 0.4525 0.4535 0.4545 0.4515 0.4608 0 .4 5 5 4 0 .4 5 6 4 0.4573 0 .4 5 8 2 0.4591 0 .4 5 9 9 0.4616 0.4625 0.4641 0 .4 6 4 9 0 .4 6 5 6 0 .4 6 6 4 0.4671 0.4678 0.4686 0.4693 0 .4 6 9 9 0 .4 7 1 3 0.4719 0 .4 7 2 6 0 .4 7 3 2 0 .4 7 3 8 0 .4 7 4 4 0 .4 7 5 0 0 .4 7 5 6 0.4761 0 .4 0 3 2 0 .4 1 9 2 0.4049 0 .4 2 0 7 0 .4 3 3 2 0 .4 4 5 2 0.4345 0.4463 0 .4 7 7 2 0.4778 0.4783 0.4788 0 .4 8 2 6 0 .4 8 3 0 0 .4 8 3 4 0.4864 0.4868 0 .4 8 9 6 0.4898 0.4871 0.4901 0.4793 0.4838 0.4875 0.4798 0 .4 8 0 3 0.4808 0.4812 0.4842 0 .4 8 4 6 0 .4 8 5 0 0 .4 8 5 4 0.4878 0.4881 0 .4 8 8 4 0.4 9 0 4 0 .4 9 0 6 0.4 9 2 0 0 .4 9 2 2 0.4925 0 .4 9 2 7 0 .4 9 2 9 0 .4 9 4 0 0.4941 0.4955 0 .4 9 6 6 0.4975 0 .4 9 8 2 0 .4 9 5 6 0 .4 9 6 7 0 .4 9 7 6 0 .4 9 8 2 0.4943 0 .4 9 5 7 0.4968 0 .4 9 7 7 0.4983 0.4945 0.4959 0.4969 0 .4 9 7 7 0 .4 9 8 4 0.4946 0.4960 0.4970 0.4978 0 .4 9 8 4 0.4909 0.4931 0.4911 0 .4 9 3 2 0.4948 0.4961 0.4971 0 .4 9 4 9 0 .4 9 6 2 0 .4 9 7 2 0 .4 9 7 9 0 .4 9 7 9 0 .4 8 8 7 0.4913 0 .4 9 3 4 0.4951 0.4963 0.4973 0.4980 0.4985 0.4985 0 .4 9 8 6 0 .4 9 8 6 0.4633 0 .4 7 0 6 0 .4 7 6 7 0 .4 8 1 7 0 .4 8 5 7 0.4890 0 .4 9 1 6 0 .4 9 3 6 0 .4 9 5 2 0 .4 9 6 4 0 .4 9 7 4 0.4981 A l t e r n a t iv e .Z-Ta b l e P(Z < z) = N(z) for z > 0 P(Z < -z) = 1 - N(z) z 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.0 0.4821 0.4861 0.4893 0.4918 0.4939 0 .4 9 5 3 0.4965 0 .4 9 7 4 0.4981 0 .4 9 8 7 0 .4 9 8 7 0 .4 9 8 7 0.4988 0.4988 0.4989 0.4989 0.4989 0 .4 9 9 0 0 .4 9 9 0 2018 Kaplan, Inc. Page 211 S t u d e n t s T -D i s t r i b u t i o n df df 1 2 3 4 3 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 40 60 120 o o Level of Significance for One-Tailed Test 0.100 0.050 0.025 0.01 0.005 0.0005 Level of Significance for Two-Tailed Test 0.20 3.078 1.886 1.638 1.533 1.476 1.440 1.415 1.397 1.383 1.372 1.363 1.356 1.350 1.345 1.341 1.337 1.333 1.330 1.328 1.325 1.323 1.321 1.319 1.318 1.316 1.315 1.314 1.313 1.311 1.310 1.303 1.296 1.289 1.282 0.10 6.314 2.920 2 .3 5 3 2 .1 3 2 2.015 1.943 1.895 1.860 1.833 1.812 1.796 1.782 1.771 1.761 1.753 1.746 1.740 1.734 1.729 1.725 1.721 1.717 1.714 1.711 1.708 1.706 1.703 1.701 1.699 1.697 1.684 1.671 1.658 1.645 0.05 12.706 4 .3 0 3 3 .1 8 2 2 .7 7 6 2.571 2 .4 4 7 2.365 2 .3 0 6 2 .2 6 2 2.228 2.201 2.179 2.160 2.145 2.131 2.120 2 .1 1 0 2.101 2 .0 9 3 2 .0 8 6 2 .0 8 0 2 .0 7 4 2 .0 6 9 2 .0 6 4 2.060 2 .0 5 6 2 .0 5 2 2.048 2.045 2 .0 4 2 2.021 2 .0 0 0 1.980 1.960 0.02 31.821 6.965 4.541 3.747 3.365 3.143 2.998 2 .8 9 6 2.821 2.764 2.718 2.681 2.650 2 .6 2 4 2 .6 0 2 2 .5 8 3 2 .5 6 7 2 .5 5 2 2 .5 3 9 2.528 2.518 2.508 2.500 2 .4 9 2 2.485 2.479 2.473 2 .4 6 7 2 .4 6 2 2 .4 5 7 2 .4 2 3 2 .3 9 0 2.358 2 .3 2 6 0.01 6 3 .6 5 7 9.925 5.841 4.604 4 .0 3 2 3.707 3.499 3.355 3.250 3.169 3 .1 0 6 3.055 3.012 2 .9 7 7 2 .9 4 7 2.921 2.898 2.878 2.861 2.845 2.831 2.819 2 .8 0 7 2 .7 9 7 2 .7 8 7 2.779 2.771 2.763 2 .7 5 6 2 .7 5 0 2 .7 0 4 2 .6 6 0 2 .6 1 7 2 .5 7 6 0.001 6 3 6 .6 1 9 31.599 12.294 8.610 6.869 5.959 5.408 5.041 4.781 4 .5 8 7 4 .4 3 7 4.318 4.221 4 .1 4 0 4 .0 7 3 4.015 3.965 3.922 3.883 3.850 3.819 3.792 3.768 3.745 3.725 3.707 3.690 3 .6 7 4 3.659 3 .6 4 6 3.551 3.460 3.373 3.291 Page 212 2018 Kaplan, Inc. 2018 Kaplan, Inc. Page 213 diversified VaR 44 duration mapping 41 DV01 -neutral hedge 121 dynamic financial correlations 65 mbedded options 143 endogenous liquidity 57 exception 26 exogenous liquidity 57 expected shortfall 6, 58, 76 E e actor exposures 38 failure rate 26 filtered historical simulation 19 F f aussian copula 112 Gaussian default time copula 113 generalized extreme value distribution 92 general risk factors 39 G G edge adjustment factor 123 historical scenarios 58 historical simulation approach 2 Ho-Lee model 168 H h ntegrated risk measurement 58 interest rate drift 135 interest rate expectations 149 interest rate tree 132 interest rate volatility 151 I i ensens inequality 153 Johnson SB distribution 92 J J endalls 103 K K everage 192 L l ge-weighted historical simulation 17 arbitrage-free models 168 autocorrelation 91 A a acktesting 25 backward induction 133 balance sheet management 60 Basel penalty zones 33 best-fit distributions 92 binomial interest rate model 132 Black-Karasinski model 184 bootstrap historical simulation 15 bottom-up approach 59 B b allable bonds 143 cash flow mapping 41 Cholesky decomposition 115 cleaned returns 26 coherent risk measure 6 compartmentalized approach 59 concentration ratio 78 concentration risk 78 conditional coverage 32 constant drift 167 constant maturity Treasury swap 139 convexity effect 153 copula function 111 correlation coefficient 67, 100 correlation copula 111 correlation options 68 correlation risk 64 correlation swap 70 correlation trading strategies 68 correlation-weighted historical simulation 18 covariance 67 Cox-Ingersoll-Ross (CIR) model 181 crashophobia 192 credit default swaps 65 cyclical feedback loop 60 c c efault correlation 76 default time 115 D d In d e x pearmans rank correlation 101 specific risks 39 spectral risk measures 58 standard deviation 66 state-dependent volatility 138 static financial correlations 65 statistical correlation measures 100 sticky delta rule 194 sticky strike rule 193 stressed VaR 58 stress testing 58, 99 surrogate density function 16 systemic risk 77 s S ime-dependent volatility 179 time-varying volatility 56 top-down approach 59 tracking error VaR 45 true probabilities 135 Type I error 28 Type II error 28 u unconditional coverage 32 undiversified VaR 43, 44 unified approach 59 T t alue at risk 1, 56, 71 Value at risk (VaR) mapping 38 variance-covariance method 72 Vasicek model 169 volatility skew 190 volatility smiles 190 volatility surface 193 volatility term structure 193 volatility-weighted historical simulation 18 V v wrong-way risk 65 mean reversion 89 mean reversion rate 89 mean-reverting process 169 mechanical-search stress tests 58 migration risk 76 Model 1 164 Model 2 167 Model 3 180 Model 4 182 ean 66 M m egative convexity 143 nonmonotonous 65 nonrecombining trees 138 N n ption-adjusted spread 141 ordinal risk measures 104 o o P Pearson correlation 67, 100 positions 38 predefined scenarios 58 price jumps 194 primitive risk factors 39 principal components analysis 125 principal mapping 41 putable bonds 144 put-call parity 189 uantile 4 quantile-quantile plot 9 quanto option 69 Q q recombining tree 138 regression hedge 122 risk-averse investor 157 risk diversification 59 risk engine 39 risk factors 38 risk-neutral investor 157 risk-neutral pricing 135 risk-neutral probabilities 135 risk premium 157 Book 1 Index lognormal model 182 lognormal VaR 5 Page 214 2018 Kaplan, Inc. Notes Notes Notes Required Disclaimers: CFA Institute does not endorse, promote, or warrant the accuracy or quality of the products or services offered by Kaplan. CFA Institute, CFA, and Chartered Financial Analyst are trademarks owned by CFA Institute. Certified Financial Planner Board of Standards Inc. owns the certification marks CFP, CERTIFIED FINANCIAL PLANNER, and federally registered CFP (with flame design) in the U.S., which it awards to individuals who successfully complete initial and ongoing certification requirements. Kaplan does not certify individuals to use the CFP, CERTIFIED FINANCIAL PLANNER1", and CFP (with flame design) certification marks. CFP certification is granted only by Certified Financial Planner Board of Standards Inc. to those persons who, in addition to completing an educational requirement such as this CFP Board-Registered Program, have met its ethics, experience, and examination requirements. 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Step 5: Perform As part of our Online Review Workshop Package, take a Schweser Mock Exam to ensure you are ready to perform on the actual FRM Exam. Put your skills and knowledge to the test and gain confidence before the exam.. Again, thank you fortrusting Kaplan Schweser with your FRM Exam preparation! Sincerely, Derek Burkett, CFA, FRM, CAIA VP, Advanced Designations, Kaplan Schweser The Kaplan Way Acquire new knowledge through demonstration and examples. Practice < ?xl-----bs Apply new knowledge through simulation and practice. Evaluate mastery of new knowledge and identify achieved outcomes. FRM Instruction Packages: PremiumPlus Package Premium Instruction Package Live Instruction*: Remember to join our Live Online Weekly Class. Register online today at www.schweser.com/frm. *Dates, times, and instructors subject to change MKT-005736 Contact us for questions about your study package, upgrading your package, purchasing additional study materials, or for additional information: www.schweser.com/frm Toll-Free: 888.325.5072 International: +1 608.779.8397 FRM Pa r t II B o o k 2: C r e d i t R i s k M e a s u r e m e n t a n d M a n a g e m e n t R e a d i n g A s s i g n m e n t s a n d L e a r n i n g O b j e c t i v e s C r e d i t R i s k M e a s u r e m e n t a n d M a n a g e m e n t 16: The Credit Decision 17: The Credit .Analyst 18: Classifications and Key Concepts of Credit Risk 19: Rating Assignment Methodologies 20: Credit Risks and Credit Derivatives 21: Spread Risk and Default Intensity Models 22: Portfolio Credit Risk 23: Structured Credit Risk 24: Counterparty Risk 25: Netting, Close-out and Related Aspects 26: Collateral 27: Credit Exposure and Funding 28: Counterparty Risk Intermediation 29: Default Probabilities, Credit Spreads and Funding Costs 30: Credit and Debt Value Adjustment 31: Wrong-way Risk 32: The Evolution of Stress Testing Counterparty Exposures 33: Credit Scoring and Retail Credit Risk Management 34: The Credit Transfer Markets and Their Implications 35: An Introduction to Securitization 36: Understanding the Securitization of Subprime Mortgage Credit S e l f -Te s t : C r e d i t R i s k M e a s u r e m e n t a n d M a n a g e m e n t F o r m u l a s A p p e n d i x In d e x v 1 15 28 38 63 90 107 122 143 153 161 173 193 205 219 231 242 254 265 281 301 311 316 320 323 2018 Kaplan, Inc. Page iii FRM 2018 PART II BOOK 2: CREDIT RISK MEASUREMENT AND MANAGEMENT 2018 Kaplan, Inc. All rights reserved. Published in 2018 by Kaplan, Inc. Printed in the United States of America. ISBN: 978-1-4754-7031-4 Required Disclaimer: GARP does not endorse, promote, review, or warrant the accuracy of the products or services offered by Kaplan of FRM related information, nor does it endorse any pass rates claimed by the provider. Further, GARP is not responsible for any fees or costs paid by the user to Kaplan, nor is GARP responsible for any fees or costs of any person or entity providing any services to Kaplan. FRM, GARP, and Global Association of Risk Professionals are trademarks owned by the Global Association of Risk Professionals, Inc. These materials may not be copied without written permission from the author. The unauthorized duplication of these notes is a violation of global copyright laws. Your assistance in pursuing potential violators of this law is greatly appreciated. Disclaimer: The SchweserNotes should be used in conjunction with the original readings as set forth by GARP. The information contained in these books is based on the original readings and is believed to be accurate. However, their accuracy cannot be guaranteed nor is any warranty conveyed as to your ultimate exam success. Page iv 2018 Kaplan, Inc. R e a d in g A s s i g n m e n t s a n d L e a r n in g O b j e c t i v e s The following material is a review of the Credit Risk Measurement and Management principles designed to address the learning objectives set forth by the Global Association of Risk Professionals. R e a d i n g A s s i g n m e n t s Jonathan Golin and Philippe Delhaise, The Bank Credit Analysis Handbook, 2nd Edition (Hoboken, NJ: John Wiley & Sons, 2013). 16 The Credit Decision, Chapter 1 17. The Credit Analyst, Chapter 2 (page 1) (page 13) Giacomo De Laurentis, Renato Maino, and Luca Molteni, Developing, Validating and Using Internal Ratings (West Sussex, UK: John Wiley & Sons, 2010). 18. Classifications and Key Concepts of Credit Risk, Chapter 2 19. Rating Asignment Methodologies, Chapter 3 (page 28) (page 38) Rene Stulz, Risk Management & Derivatives (Florence, KY: Thomson South-Western, 2002). 20. Credit Risks and Credit Derivatives, Chapter 18 (page 63) Alan Malz, Financial Risk Management: Models, History, and Institutions (Hoboken, NJ: John Wiley & Sons, 2011). 21. Spread Risk and Default Intensity Models, Chapter 7 22. Portfolio Credit Risk, Chapter 8 23. Structured Credit Risk, Chapter 9 (page 90) (page 107) (page 122) Jon Gregory, The xVA Challenge: Counterparty Credit Risk, Funding, Collateral, and Capital, 3rd Edition (West Sussex, UK: John Wiley & Sons, 2013). 24. Counterparty Risk, Chapter 4 25. Netting, Close-out and Related Apects, Chapter 5 26. Collateral, Chapter 6 27. Credit Exposure and Funding, Chapter 7 (page 143) (page 153) (page 161) (page 173) 2018 Kaplan, Inc. Page v Book 2 Reading Assignments and Learning Objectives 28. Counterparty Risk Intermediation, Chapter 9 (page 193) 29. Default Probabilities, Credit Spreads and Funding Costs, Chapter 12 (page 203) 30. Credit and Debt Value Adjustment, Chapter 14 31. Wrong-way Risk, Chapter 17 (page 219) (page 231) Stress Testing: Approaches, Methods, and Applications, Edited by Akhtar Siddique and Iftekhar Hasan (London, UK: Risk Books, 2013). 32. The Evolution of Stress Testing Counterparty Exposures, Chapter 4 (page 242) Michel Crouhy, Dan Galai, and Robert Mark, The Essentials of Risk Management, 2nd Edition (New York, NY: McGraw-Hill, 2014). 33. Credit Scoring and Retail Credit Risk Management, Chapter 9 (page 234) 34. The Credit Transfer Markets and Their Implications, Chapter 12 (page 265) Moo rad Choudhry, Structured Credit Products: Credit Derivatives & Synthetic Securitization, 2nd Edition (New York, NY: John Wiley & Sons, 2010). 35. An Introduction to Securitization, Chapter 12 (page 281) 36. Adam Ashcraft and Til Schuermann, Understanding the Securitization of Subprime Mortgage Credit, Federal Reserve Bank of New York Staff Reports, No. 318 (March 2008). (page 301) Page vi 2018 Kaplan, Inc. Book 2 Reading Assignments and Learning Objectives L e a r n i n g O b j e c t i v e s 16. The Credit Decision After completing this reading, you should be able to: 1. Define credit risk and explain how it arises using examples, (page 1) 2. Explain the components of credit risk evaluation, (page 2) 3. Describe, compare and contrast various credit risk mitigants and their role in credit analysis, (page 2) 4. Compare and contrast quantitative and qualitative techniques of credit risk evaluation, (page 4) 3. Compare the credit analysis of consumers, corporations, financial institutions, and sovereigns, (page 3) 6. Describe quantitative measurements and factors of credit risk, including probability of default, loss given default, exposure at default, expected loss, and time horizon, (page 7) 7. Compare bank failure and bank insolvency, (page 9) 17. The Credit Analyst After completing this reading, you should be able to: 1. Describe, compare and contrast various credit analyst roles, (page 15) 2. Describe common tasks performed by a banking credit analyst, (page 20) 3. Describe the quantitative, qualitative, and research skills a banking credit analyst is expected to have, (page 21) 4. Assess the quality of various sources of information used by a credit analyst. (page 22) 18. Classifications and Key Concepts of Credit Risk After completing this reading, you should be able to: 1. Describe the role of ratings in credit risk management, (page 28) 2. Describe classifications of credit risk and their correlation with other financial risks (page 28) Define default risk, recovery risk, exposure risk and calculate exposure at default, (page 29) Explain expected loss, unexpected loss, VaR, and concentration risk, and describe the differences among them, (page 30) Evaluate the marginal contribution to portfolio unexpected loss, (page 32) Define risk-adjusted pricing and determine risk-adjusted return on risk-adjusted capital (RARORAC). (page 32) 3. 4. 5. 6. 19. Rating Assignment Methodologies .After completing this reading, you should be able to: 1. Explain the key features of a good rating system, (page 38) 2. Describe the experts-based approaches, statistical-based models, and numerical approaches to predicting default, (page 39) 3. Describe a rating migration matrix and calculate the probability of default, cumulative probability of default, marginal probability of default, and annualized default rate, (page 40) 4. Describe rating agencies assignment methodologies for issue and issuer ratings, (page 41) 2018 Kaplan, Inc. Page vii Book 2 Reading Assignments and Learning Objectives 5. Describe the relationship between borrower rating and probability of default. (page 42) 6. Compare agencies ratings to internal experts-based rating systems, (page 42) 7. Distinguish between the structural approaches and the reduced-form approaches to predicting default, (page 43) 8. Apply the Merton model to calculate default probability and the distance to default and describe the limitations of using the Merton model, (page 44) 9. Describe linear discriminant analysis (LDA), define the Z-score and its usage, and apply LDA to classify a sample of firms by credit quality, (page 43) 10. Describe the application of logistic regression model to estimate default probability, (page 48) 11. Define and interpret cluster analysis and principal component analysis, (page 49) 12. Describe the use of a cash flow simulation model in assigning rating and default probability, and explain the limitations of the model, (page 32) 13. Describe the application of heuristic approaches, numeric approaches, and artificial neural network in modeling default risk and define their strengths and weaknesses, (page 53) 14. Describe the role and management of qualitative information in assessing probability of default, (page 56) 20. Credit Risks and Credit Derivatives After completing this reading, you should be able to: 1. Using the Merton model, calculate the value of a firms debt and equity and the volatility of firm value, (page 63) 2. Explain the relationship between credit spreads, time to maturity, and interest rates, and calculate credit spread, (page 68) 3. Explain the differences between valuing senior and subordinated debt using a contingent claim approach, (page 71) 4. Explain, from a contingent claim perspective, the impact of stochastic interest rates on the valuation of risky bonds, equity, and the risk of default, (page 71) 5. Compare and contrast different approaches to credit risk modeling, such as those related to the Merton model, CreditRisk+, CreditMetrics, and the KMV model, (page 75) 6. Assess the credit risks of derivatives, (page 80) 7. Describe a credit derivative, credit default swap, and total return swap, (page 80) 8. Explain how to account for credit risk exposure in valuing a swap, (page 83) 21. Spread Risk and Default Intensity Models After completing this reading, you should be able to: 1. Compare the different ways of representing credit spreads, (page 90) 2. Compute one credit spread given others when possible, (page 90) 3. Define and compute the Spread 01. (page 91) 4. Explain how default risk for a single company can be modeled as a Bernoulli trial, (page 92) 5. Explain the relationship between exponential and Poisson distributions, (page 93) 6. Define the hazard rate and use it to define probability functions for default time and conditional default probabilities, (page 93) 7. Calculate the conditional default probability given the hazard rate, (page 93) 8. Calculate risk-neutral default rates from spreads, (page 95) 9. Describe advantages of using the CDS market to estimate hazard rates, (page 96) Page viii 2018 Kaplan, Inc. Book 2 Reading Assignments and Learning Objectives 10. Explain how a CDS spread can be used to derive a hazard rate curve, (page 97) 11. Explain how the default distribution is affected by the sloping of the spread curve. (page 99) 12. Define spread risk and its measurement using the mark-to-market and spread volatility, (page 100) 22. Portfolio Credit Risk After completing this reading, you should be able to: 1. Define and calculate default correlation for credit portfolios, (page 107) 2. Identify drawbacks in using the correlation-based credit portfolio framework. (page 108) 3. Assess the impact of correlation on a credit portfolio and its Credit VaR. (page 109) 4. Describe the use of a single factor model to measure portfolio credit risk, including the impact of correlation, (page 111) 3. Define and calculate Credit VaR. (page 109) 6. Describe how Credit VaR can be calculated using a simulation of joint defaults. (page 116) 23. Structured Credit Risk After completing this reading, you should be able to: 1. Describe common types of structured products, (page 122) 2. Describe tranching and the distribution of credit losses in a securitization. (page 123) 3. Describe a waterfall structure in a securitization, (page 124) 4. Identify the key participants in the securitization process, and describe conflicts of interest that can arise in the process, (page 127) 3. Compute and evaluate one or two iterations of interim cashflows in a three-tiered securitization structure, (page 128) 6. Describe a simulation approach to calculating credit losses for different tranches in a securitization, (page 131) 7. Explain how the default probabilities and default correlations affect the credit risk in a securitization, (page 132) 8. Explain how default sensitivities for tranches are measured, (page 134) 9. Describe risk factors that impact structured products, (page 134) 10. Define implied correlation and describe how it can be measured, (page 135) 11. Identify the motivations for using structured credit products, (page 135) 24. Counterparty Risk After completing this reading, you should be able to: 1. Describe counterparty risk and differentiate it from lending risk, (page 143) 2. Describe transactions that carry counterparty risk and explain how counterparty 3. risk can arise in each transaction, (page 144) Identify and describe institutions that take on significant counterparty risk. (page 145) 4. Describe credit exposure, credit migration, recovery, mark-to-market, replacement 5. cost, default probability, loss given default, and the recovery rate, (page 146) Identify and describe the different ways institutions can quantify, manage and mitigate counterparty risk, (page 147) 2018 Kaplan, Inc. Page ix Book 2 Reading Assignments and Learning Objectives 25. Netting, Close-out and Related Aspects After completing this reading, you should be able to: 1. Explain the purpose of an ISDA master agreement, (page 153) 2. Summarize netting and close-out procedures (including multilateral netting), explain their advantages and disadvantages, and describe how they fit into the framework of the ISDA master agreement, (page 153) 3. Describe the effectiveness of netting in reducing credit exposure under various scenarios, (page 156) 4. Describe the mechanics of termination provisions and trade compressions and 5. explain their advantages and disadvantages, (page 156) Identify and describe termination events and discuss their potential effects on parties to a transaction, (page 156) 26. Collateral After completing this reading, you should be able to: 1. Describe the rationale for collateral management, (page 161) 2. Describe the terms of a collateral and features of a credit support annex (CSA) within the ISDA Master Agreement including threshold, initial margin, minimum transfer amount and rounding, haircuts, credit quality, and credit support amount, (page 161) 3. Describe the role of a valuation agent, (page 162) 4. Describe the mechanics of collateral and the types of collateral that are typically used, (page 163) 5. Explain the process for the reconciliation of collateral disputes, (page 163) 6. Explain the features of a collateralization agreement, (page 164) 7. Differentiate between a two-way and one-way CSA agreement and describe how collateral parameters can be linked to credit quality, (page 166) 8. Explain how market risk, operational risk, and liquidity risk (including funding liquidity risk) can arise through collateralization, (page 166) 27. Credit Exposure and Funding After completing this reading, you should be able to: 1. Describe and calculate the following metrics for credit exposure: expected mark-to- market, expected exposure, potential future exposure, expected positive exposure and negative exposure, effective exposure, and maximum exposure, (page 173) 2. Compare the characterization of credit exposure to VaR methods and describe 3. 4. additional considerations used in the determination of credit exposure, (page 176) Identify factors that affect the calculation of the credit exposure profile and summarize the impact of collateral on exposure, (page 176) Identify typical credit exposure profiles for various derivative contracts and combination profiles, (page 177) 5. Explain how payment frequencies and exercise dates affect the exposure profile of various securities, (page 180) 6. Explain the impact of netting on exposure, the benefit of correlation, and calculate the netting factor, (page 181) 7. Explain the impact of collateralization on exposure, and assess the risk associated with the remargining period, threshold, and minimum transfer amount, (page 182) Page x 2018 Kaplan, Inc. Book 2 Reading Assignments and Learning Objectives 28. Counterparty Risk Intermediation After completing this reading, you should be able to: 1. Identify counterparty risk intermediaries including central counterparties (CCPs), derivative product companies (DPCs), special purpose vehicles (SPVs), and monoline insurance companies (monolines) and describe their roles, (page 193) 2. Describe the risk management process of a CCP and explain the loss waterfall structure of a CCP. (page 196) 3. Compare bilateral and centrally cleared over-the-counter (OTC) derivative markets, (page 198) 4. Assess the capital requirements for a qualifying CCP and discuss the advantages and disadvantages of CCPs. (page 199) 3. Discuss the impact of central clearing on credit value adjustment (CVA), funding value adjustment (FVA), capital value adjustment (KVA), and margin value adjustment (MVA). (page 200) 29. Default Probabilities, Credit Spreads and Funding Costs After completing this reading, you should be able to: 1. Distinguish between cumulative and marginal default probabilities, (page 203) 2. Calculate risk-neutral default probabilities, and compare the use of risk-neutral and real-world default probabilities in pricing derivative contracts, (page 206) 3. Compare the various approaches for estimating price: historical data approach, equity based approach, and risk neutral approach, (page 207) 4. Describe how recovery rates may be estimated, (page 209) 5. Describe credit default swaps (CDS) and their general underlying mechanics. (page 210) 6. Describe the credit spread curve and explain the motivation for curve mapping, (page 211) 7. Describe types of portfolio credit derivatives, (page 211) 8. Describe index tranches, super senior risk, and collateralized debt obligations (CDOs), (page 212) 30. Credit and Debt Value Adjustments After completing this reading, you should be able to: 1. Explain the motivation for and the challenges of pricing counterparty risk. (page 219) 2. Describe credit value adjustment (CVA). (page 219) 3. Calculate CVA and the CVA spread with no wrong-way risk, netting, or collateralization, (page 219) 4. Evaluate the impact of changes in the credit spread and recovery rate assumptions on CVA. (page 221) 5. Explain how netting can be incorporated into the CVA calculation, (page 222) 6. Define and calculate incremental CVA and marginal CVA, and explain how to convert CVA into a running spread, (page 222) 7. Explain the impact of incorporating collateralization into the CVA calculation. (page 222) 8. Describe debt value adjustment (DVA) and bilateral CVA (BCVA). (page 223) 9. Calculate BCVA and BCVA spread, (page 223) 2018 Kaplan, Inc. Page xi Book 2 Reading Assignments and Learning Objectives 31. Wrong-way Risk After completing this reading, you should be able to: 1. Describe wrong-way risk and contrast it with right-way risk, (page 231) 2. 3. Discuss the impact of wrong-way risk on collateral and central counterparties. Identify examples of wrong-way risk and examples of right-way risk, (page 232) (page 237) 32. The Evolution of Stress Testing Counterparty Exposures After completing this reading, you should be able to: 1. Differentiate among current exposure, peak exposure, expected exposure, and expected positive exposure, (page 242) 2. Explain the treatment of counterparty credit risk (CCR) both as a credit risk and as a market risk and describe its implications for trading activities and risk management for a financial institution, (page 243) 3. Describe a stress test that can be performed on a loan portfolio and on a derivative portfolio, (page 244) 4. Calculate the stressed expected loss, the stress loss for the loan portfolio and the stress loss on a derivative portfolio, (page 243) 3. Describe a stress test that can be performed on CVA. (page 246) 6. Calculate the stressed CVA and the stress loss on CVA. (page 246) 7. Calculate the DVA and explain how stressing DVA enters into aggregating stress tests of CCR. (page 248) 8. Describe the common pitfalls in stress testing CCR. (page 249) 33. Credit Scoring and Retail Credit Risk Management After completing this reading, you should be able to: 1. 2. Explain the differences between retail credit risk and corporate credit risk. .Analyze the credit risks and other risks generated by retail banking, (page 254) (page 255) 3. Discuss the dark side of retail credit risk and the measures that attempt to address the problem, (page 255) 4. Define and describe credit risk scoring model types, key variables, and applications, (page 256) 5. Discuss the key variables in a mortgage credit assessment and describe the use of cutoff scores, default rates, and loss rates in a credit scoring model, (page 257) 6. Discuss the measurement and monitoring of a scorecard performance including the use of cumulative accuracy profile (CAP) and the accuracy ratio (AR) techniques, (page 258) 7. Describe the customer relationship cycle and discuss the trade-off between creditworthiness and profitability, (page 259) 8. Discuss the benefits of risk-based pricing of financial services, (page 260) 34. The Credit Transfer Markets and Their Implications After completing this reading, you should be able to: 1. Discuss the flaws in the securitization of subprime mortgages prior to the financial 2. crisis of 2007. (page 265) Identify and explain the different techniques used to mitigate credit risk, and describe how some of these techniques are changing the bank credit function, (page 267) Page xii 2018 Kaplan, Inc. Book 2 Reading Assignments and Learning Objectives 3. Describe the originate-to-distribute model of credit risk transfer and discuss the two ways of managing a bank credit portfolio, (page 268) 4. Describe the different types and structures of credit derivatives including credit default swaps (CDS), first-to-default put, total return swaps (TRS), asset-backed credit-linked note (CLN), and their applications, (page 269) 3. Explain the credit risk securitization process and describe the structure of typical collateralized loan obligations (CLOs) or collateralized debt obligations (CDOs). (page 273) 6. Describe synthetic CDOs and single-tranche CDOs. (page 273) 7. Assess the rating of CDOs by rating agencies prior to the 2007 financial crisis. (page 275) 35. An Introduction to Securitization After completing this reading, you should be able to: 1. Define securitization, describe the securitization process and explain the role of participants in the process, (page 281) 2. Explain the terms over-collateralization, first-loss piece, equity piece, and cash waterfall within the securitization process, (page 283) 3. Analyze the differences in the mechanics of issuing securitized products using a trust versus a special purpose vehicle (SPV) and distinguish between the three main SPV structures: amortizing, revolving, and master trust, (page 284) 4. Explain the reasons for and the benefits of undertaking securitization, (page 286) 5. Describe and assess the various types of credit enhancements, (page 287) 6. Explain the various performance analysis tools for securitized structures and identify the asset classes they are most applicable to. (page 288) 7. Define and calculate the delinquency ratio, default ratio, monthly payment rate (MPR), debt service coverage ratio (DSCR), the weighted average coupon (WAC), the weighted average maturity (WAM), and the weighted average life (WAL) for relevant securitized structures, (page 290) 8. Explain the prepayment forecasting methodologies and calculate the constant prepayment rate (CPR) and the Public Securities Association (PSA) rate, (page 293) 9. Explain the decline in demand in the new-issue securitized finance products following the 2007 financial crisis, (page 295) 36. Understanding the Securitization of Subprime Mortgage Credit .After completing this reading, you should be able to: 1. Explain the subprime mortgage credit securitization process in the United States. 2. (page 301) Identify and describe key frictions in subprime mortgage securitization, and assess the relative contribution of each factor to the subprime mortgage problems. (page 301) 3. Describe the characteristics of the subprime mortgage market, including the creditworthiness of the typical borrower and the features and performance of a subprime loan, (page 304) 4. Describe the credit ratings process with respect to subprime mortgage backed securities, (page 305) 5. Explain the implications of credit ratings on the emergence of subprime related mortgage backed securities, (page 305) 6. Describe the relationship between the credit ratings cycle and the housing cycle. (page 305) 2018 Kaplan, Inc. Page xiii Book 2 Reading Assignments and Learning Objectives 7. Explain the implications of the subprime mortgage meltdown on portfolio management, (page 306) 8. Compare predatory lending and borrowing, (page 306) Page xiv 2018 Kaplan, Inc. The following is a review of the Credit Risk Measurement and Management principles designed to address the learning objectives set forth by GARP. This topic is also covered in: T h e C r e d i t D e c i s i o n Topic 16 E x a m F o c u s This topic provides an overview of the credit analysis process. Credit risk can arise from multiple sources, including default, an increased probability of default, failure to perform on a prepaid obligation, more severe losses than forecasted resulting from greater exposure than expected, or smaller recoveries than expected given a default. For the exam, be able to compare and contrast the credit analysis process for consumers (i.e., individuals), nonfinancial firms, financial firms, and to a lesser degree sovereigns. Also, be able to distinguish between the probability of default (PD), the loss given default (LGD), the exposure at default (EAD), and the overall expected loss (EL). Understand that it is simple to measure these factors after the fact but difficult to forecast losses in advance. Finally, understand that outside of times of stress or crisis, banks rarely fail. Credit analysts must determine where a financial institution falls on a continuum between perfectly creditworthy and bankrupt. C r e d i t R i s k