Business Plans, ortfolio Management and Basel II

Business Plans, Portfolio Management and Basel II By William C. Handorf and Morgane Suriray

How risk measurement under Basel II affects business planning and the competitive environment.

William C. Handorf is a Professor of Finance at the George Washington University, Washington, D.C. Contact him at wchandorf@https://www.wendangku.net/doc/ea9734636.html,. Morgane Suriray recently completed her M.S. in Finance at the George Washington University.A business plan is a formal document that

requires bank management to establish the or-

ganization’s mission, vision, goals, objectives,

performance measures and milestones for implement-

ing the plan. Exhibit 1 describes each of the plan’s

elements and illustrates their relationship to each other.

The planning document normally includes a descrip-

tion of the business, marketing plan, management

plan, risk management system plan, asset/liability and

capital plan for at least three years and a process by

which to monitor and revise the plan as needed. Bank

regulators typically require newly chartered banks,

banks changing ownership or mission and banks with

low capital to develop a new business plan or update

a prior plan. Bankers often voluntarily update a plan

if the equity market assigns their bank a low price/

book ratio relative to peers or the national credit rating

agencies assign the institution a credit rating below

investment grade. Regulatory initiatives also provide

a catalyst for reviewing a business plan.

Exhibit 1

Key Deliverables

The B asel Committee on B anking Supervision released a report in 2004 entitled International Con-vergence of Capital Measurement and Capital Standards: A Revised Framework. The regulatory report is known as Basel II. The regulatory program, proposed to be implemented in the United States and other coun-tries later this decade, affects the attractiveness of lending relative to investing, the appeal of high-risk loans to low-risk loans and the pricing of credits by large, internationally active banks and thrift in-stitutions that adopt the system. Most large banks already evaluate the integrated risks of a portfolio to ensure economic capital is suf? cient. However, the regulatory proposal requires adopting banks to disclose more information regarding the credit risk pro? le of key asset groups. Basel II will affect the competitive environment for smaller banks and thrifts that do not adopt the plan if the market changes the pricing of debt and equity issued by adopting banks. Basel II includes three pillars: capi-tal rules, supervisory review and market discipline. Each pillar affects the business-planning process.Basel II attempts to achieve the following results from Pillar I, which focuses on capital rules:Allows banks covered by Basel II to assess inter-nally the risk of assets and assign risk weights based on the bank’s experience that reflects the probability of unexpected default at the 99.9 per-cent confidence level, the stress loss-given default (LGD), the exposure at default and the correlation J UNE –J ULY 2006 B ANK A CCOUNTING & F INANCE 17

Business Plans, Portfolio Management and Basel II

of losses within a credit type, subject to supervi-

sory review and a required statistical model

Requires banks to ensure the allowance for loan

losses equals expected loan losses over the next

year, to maintain capital that reflects the value

at risk of a trading portfolio and to hold capital

against operational risk resulting from failed

systems, dishonest employees or customers and

weak internal and accounting controls—among

other sources of operational loss

Strengthens the soundness and safety of the

international banking system and ensures that

capital adequacy regulation will not provide a

source of competitive inequality among banks

in different countries

The business plan must determine how and

why the bank’s and its competitors’ credit risk

exposure will change, if at all, as a result of the

evolving capital rules.

B asel II may change the actual or perceived

strength or weakness of a bank in different product

markets. For example, some banks with lower capital

requirements than the existing Basel Accord will ? nd

they now have additional capital available to take

on incrementally more credit risk by increasing the

proportion of loans or high-risk loans. Other banks

with higher capital requirements will gravitate to

lower-risk alternatives or seek additional capital.

Basel II also alters actual or perceived opportuni-

ties and threats in the marketplace. For example,

if Basel II allows a bank to originate loans with a

much lower risk weight than competitors, the adopt-

ing institution can price loans cheaper and realize

market penetration opportunities. By contrast, if the

existing Basel Accord allows nonadopting banks to

assign a lower risk weight to an asset than dictated

by covered banks, the adopting banks must attempt

to respond to the threat with pricing that neither

re? ects risks nor costs or simply exit the market.

As illustrated by Exhibit 2, banks will expand

market share for products where the bank retains

competitive strength and excellent opportunities

abound. Banks will retreat from products where the

institution retains a weak position and the market is

considered poor. Adopting banks will ? nd some as-

sets more favorable under Basel II because the loans,

such as commercial business loans and commercial

real estate loans, will likely require less risk-weighted

capital under the strictures of Basel II. Nonadopting

banks may ? nd some assets, such as credit card loans, more favorable because adopting banks must originate the same loan with more required capital under the new regulatory paradigm.

Exhibit 2Business Planning Matrix Products, Delivery Systems, Geographic Regions, Asset/Liability Focus The principal difference between the Basel Accord and Basel II regarding risk-based loan costing is the pro-portion of assets required to be provided by high-cost equity versus lower-cost and tax-deductible liabilities. The cost of equity represents the return a bank is ex-pected to generate on behalf of its shareholders. Most models designed to estimate the required return on equity , such as the bond premium model or the capi-tal asset pricing model, suggest low-risk banks must generate a return on equity of at least eight percent to compensate equity investors while high-risk banks must earn a minimum of 12 percent. Most money-center banks adopting Basel II estimate their cost of equity or required return on equity to be approximately 10 percent given current economic conditions. The cost of equity is much higher than the cost of deposits or debt, and there is no tax shield for equity ? nancing. Banks are able to earn consistently a return on equity greater than their required return on equity trade at a price/book premium; the market lets bankers know if risk is appropriately priced and managed.This article raises questions and provides pre-liminary answers related to the impact of Basel II on business plans and portfolio credit-risk manage-ment. First, what information, if any, is available from market discipline, or Pillar III? How does the market currently view credit risk relative to historical banking experience? Second, why do quantitative impact studies reviewed by regulatory supervisors as required under Pillar II suggest sharply lower risk weights than current standards? Third, what factors will likely cause risk weights to change from that currently modeled? Business plans must derive the 18 B ANK A CCOUNTING & F INANCE J UNE –J ULY 2006

optimal strategy to cope with and take advantage of new internally modeled risk weights, consider the scenarios that would lead to vastly new risk weights in the future and consider the portfolio constraint imposed by existing Tier I leverage capital rules.

Pillar III and

Credit Market Discipline

Pillar III requires adopting banks to consider the feed-back provided by the pricing of stocks and bonds traded in the secondary market and the additional information, if any, supplied by credit-rating agencies. The national credit-rating agencies, such as Moody’s Investors Ser-vice, Standard & Poor’s and Fitch Ratings, all evaluate various factors before assigning an initial credit rating, placing a ? rm or bond under a “credit watch” that alerts the market to a potential change in rating or sub-sequently upgrading or downgrading a bond. Studies of the rating process suggest that approxi-mately 50 percent of a rating is derived from ? nancial ratios depicting the ability of the bank and/or holding company to pay interest and to repay principal on a timely basis. Moody’s recently identi? ed several ra-tios it uses to distinguish risk within banks, including the following: tangible equity to risk-weighted assets, nonperforming assets to core earnings and the net-interest margin. Most studies of credit ratings show higher-rated companies are generally associated with the following attributes: higher capital ratios, higher earnings, lower variability of earnings and larger ? rms.1 The ratios largely affect the probability a bank will default on its obligation. If Basel II allows selected U.S. banks to reduce risk-based capital ratios below the level credit-rating agencies believe appropriate for a given level of risk, the strategic ? nancial shift could adversely affect the credit ratings of banks and increase the cost of both debt and required return on equity. The credit-rating agencies are an important stakeholder within the domain of Pillar III.

About 25 percent of a credit rating re? ects covenants included within the indenture to protect bond inves-tors from managerial moral hazard. Bonds secured by high-grade, marketable collateral receive higher ratings. Bonds subordinated to depositors and other creditors receive lower ratings. The key covenants within the indenture mostly affect the bond’s LGD. Basel II encourages banks to issue subordinated deben-tures to allow the market to gauge the creditworthiness of the institution. High-risk banks will be required to obtain more capital and/or develop and implement a capital plan to manage excessive portfolio risk exposure. Bank management should maintain good communication with the credit-rating agencies and take actions before a regulatory request to augment capital or revise a business plan given a low-grade (BB/Ba or lower) credit assessment. Capital—whether stock or subordinated debt—is most expensive to ob-tain when required to be raised under ? nancial duress or regulatory directive. The market perceives the is-suance of stock to provide a signal that management believes the ? rm’s equity is overvalued or projects the recapitalization will place downward pressure on return on equity from the existence of additional shares outstanding. Either way, the market provides negative, but necessary, feedback to the bank.

The remaining 25 percent of a credit rating is related to the depth and breadth of management, the quality of corporate governance and the existence and imple-mentation of a sound business plan. Basel II requires adopting banks to articulate how management mea-sures, monitors and controls risk. Regulatory rules will require banks to publish their probability of de-fault, exposure at default, correlation of losses within types of loans, LGD and modeled risk weights for key groups of assets. The market will thereby have more information than currently available to judge whether the business strategy pursued by a bank is appropri-ate. The market expects banks to increase exposure to products where the institution has a competitive strength and the market appears excellent and to exit those products where the bank retains competitive weakness and the market appears poor. Bankers can judge the market’s reaction by evaluating trends in stock prices, bond spreads and credit ratings.

To assess the ? nancial consequence, if any, that ? nancial ratios and legal covenants have on the credit ratings of large U.S. banks, the research adopts a multiple linear regression testing framework. The credit ratings assigned on long-term deposits of 27 large U.S. banks are regressed as of the fourth quarter of 2005 against ? nancial ratios that capture various elements of risk common to the banking sector. The credit-rating numerical scale ranges from 26 (AAA/ Aaa) to 2 (C–/C3). The average credit rating for long-term deposits of the U.S. banks tested was between A+/A1 and A/A2 and the range between AA/Aa2

Business Plans, Portfolio Management and Basel II J UNE–J ULY 2006 B ANK A CCOUNTING & F INANCE19

and BBB/Baa2. The ? nancial ratios, which were av-eraged over a ? ve-year period, include capital (Tier I leverage capital), asset quality (noncurrent loans to gross loans), management (logarithm of total assets), earnings (return on assets) and liquidity (proportion of long-term assets funded by net noncore funds). The ratios re? ect the CAMEL regulatory framework. The model also includes a dummy variable, taking the value of “1” if the bond is subordinated and “0” otherwise. Functionally, the regression follows: credit rating = function (capital, asset quality, manage-ment, earnings, liquidity and subordination feature) The results are surprising and very different from prior empirical studies and rational expectations. The mean values of each variable, the expected sign of each independent variable tested and the results are highlighted in Exhibit 3. First, the model is sta-tistically signi? cant at the one-percent level based on the F-ratio; at least one ratio is able to explain why some banks are rated high grade versus others rated medium grade. Second, the regression model explains 60.4 percent of the variability of credit rat-ings based on the R2 of the model. Such results are comparable to prior empirical work. We directed par-ticular attention to potential multicollinearity among independent variables. To do so, we computed the individual variance in? ation factor (VIF), which is an-ticipated to be less than the VIF of the overall model when there is no multicollinearity between variables. The model suffers from multicollinearity, especially for the following variables: bank earnings, the credit rating, capital and the logarithm of total assets. It is important to note that the presence of multicol-linearity does not affect the estimates of the model. However, it in? uences the level of the standard error, which affects our ability to test the signi? cance of a variable. Third, only one independent variable is sig-ni? cant at the one-percent con? dence level: asset size. Large banks, all things equal, are rated higher. Large banks are more easily able to achieve economies of scale and scope within operations. Large banks may also be “too big too fail” (“TBTF”). If banks are viewed as TBTF, market discipline will dissolve. Even though the model only shows one variable statisti-cally important to explaining current credit ratings, it is important to emphasize that the rating agencies do consider a wide range of ? nancial, managerial and legal factors along with economic trends prior to assigning or changing a rating.

Exhibit 3

Credit-Rating Model

Credit rating (26 for AAA/Aaa to 2 for C-/C3) = function (CAMEL ratios and subordination feature)

Ratio

Mean

of

Sample Expected Impact Signi? cance

Rating

21.8

(A+/A1)

Tier 1 capital 7.72%

Positive No

Impact

Noncurrent loans 1.07%

Negative No

Impact

Log of assets 10.4

Positive Signi? cant*

Return on assets 1.50%

Positive No

Impact

Noncore funding 34.84%

Negative No

Impact Subordinated 74% Subordinated Negative No

Impact

R-square of model = 60.4% with an F-statistic of 5.09*

* Signi? cant at one percent.

** Signi? cant at ? ve percent.

*** Signi? cant at 10 percent.

Why don’t ratios that represent capital, asset quality, earnings or liquidity show more statistical prominence in explaining current credit ratings than the 1980s or 1990s? V ery simply, the banking industry today is very strong. For example, almost 400 banks and thrift institutions failed in 1990 versus zero in 2005. There were more than 1,400 banks and thrifts on the Federal Deposit Insurance Corporation’s (FDIC) problem list in 1991 versus fewer than 100 as of 2005. Indeed, the 74 problem-list banks as of late 2005 represented the fewest institutions of concern to the federal banking agency since the list was ? rst published 36 years earlier. As of 2005, 99.2 percent of insured banks are “well capitalized”; 99.9 percent of bank assets are funded by “well-capitalized” banks. Although credit ratings provide bankers an important benchmark to assess their performance and develop a business plan, ratings tend to lag the market’s perception of risk within a bank. Bankers must monitor the ? nancial markets to gauge the ad-ditional information, if any, provided by Pillar III.

Business Plans, Portfolio Management and Basel II

20B ANK A CCOUNTING & F INANCE J UNE–J ULY 2006

The Credit Market

There are many factors that affect the interest rate a bank must pay to issue debt in the primary market. Banks judged to be more risky by the market or the credit-rating agencies must pay higher rates of interest. Earlier empirical studies of the pricing of bank debt between 1983 and 1991 indicate the market encapsulated the default risk exposure of the banks sampled.2 At that time, bank bond prices declined and yields increased for banks with poor asset quality, less capital and lower credit rat-ings. Capital and asset quality were judged to be especially important when hundreds of deposit institutions were being liquidated each year. Pro? t-ability ratios were less important and interest rate risk measures typically insigni?cant to the pricing of bank debt at that time.

To assess the statistical relationship, if any, that ? nancial ratios, credit ratings and legal covenants have on the current market pricing of debt issued by large U.S. banks, the research again adopts a multiple linear regression testing framework. The yield spread derived from bank debt trading in the secondary market is functionally related to factors previously described for the multiple regression applied to credit ratings as well as the credit rating itself. The yield spread represents the yield appli-cable to a ? xed-rate bank bond minus the yield of a U.S. Treasury security with a comparable term to ma-turity. To illustrate, if the ? xed-rate yield on a bond issued by a bank is trading at six percent when the underlying yield on a comparable term U.S. Treasury security is four percent, the absolute yield spread is two percent, or 200 basis points. The absolute yield spread is an acceptable method of evaluating market risk when comparing bonds denominated in one currency for a relatively short period of time comparable to this research. The analysis attempts to determine why some bank bonds trade with a low spread off similar-term U.S. Treasury notes and others trade with a high spread. The average spread of the 27-bank sample is 47 basis points, with some bank bonds trading as much as 159 basis points above the yield of a U.S. Treasury note.

absolute yield spread = function (capital, asset qual-ity, management, earnings, liquidity, subordination feature and credit rating)

The results differ from the credit rating analysis where only bank size is signi? cant. The results are highlighted in Exhibit 4. First, the model is statistically signi? cant at the ? ve-percent level based on the F-ratio; at least one ratio is able to explain why some bank debt trades with a high-yield spread and other bank debt trades with a low-yield spread. Second, the regression model explains 57 percent of the variability of credit spreads based on the R2 of the model. Finally, three independent variables are signi? cant at the ? ve-percent con?dence level. The bond spread is lower if a bank generates higher earnings or return on assets and if the bank’s deposits are more highly rated by a credit-rating agency. The financial market appears to use credit-rating agencies to assess relative risk. In addition, the bond spread is higher for bonds with a subordinated feature. The statistical results are consistent with rational expectations. Yet, capital and asset quality ratios are not found signi? cant.

Exhibit 4

Bond Spread Model

Bond spread = function (CAMEL ratios, rating and subordination feature)

Ratio

Mean

of

Sample Expected Impact Signi? cance

Spread

0.47%

Credit rating 21.8 (A+/A1)

Negative Signi? cant**

Tier 1 capital 7.72%

Negative No

Impact

Noncurrent loans 1.07%

Positive No

Impact

Log of assets 10.4

Negative No

Impact

Return on assets 1.50%

Negative Signi? cant**

Noncore funding 34.84%

Positive No

Impact Subordinated 74% Subordinated Positive Signi? cant**

R-square of model = 57.0% with an F-statistic of 3.60**

* Signi? cant at one percent.

** Signi? cant at ? ve percent

*** Signi? cant at 10 percent.

Business Plans, Portfolio Management and Basel II J UNE–J ULY 2006 B ANK A CCOUNTING & F INANCE21

The credit market and credit ratings provide bankers feedback regarding performance and risk exposure. Current results are quite different than prior empirical studies given the robust nature of the banking industry observed at present. The banking industry was not judged to be healthy 15 years ago when commercial real estate loan losses precipitated losses of suf? cient magnitude to cause hundreds of deposit institutions to fail each year. Pillar II subjects bankers to supervisory review of internal models derived to assess credit risk.

Pillar II and

Supervisory Review

Despite the opportunity to assign new risk weights for all loans under Basel II, there is a strong regula-tory bias by bank regulators against commercial real estate loans. The following quotation from Basel II illustrates the predilection against commercial real estate lending3:

In view of the experience in numerous countries that commercial property lending has been a recurring cause of troubled assets in the banking industry over the past few decades, the Com-mittee holds that mortgages on commercial real estate do not, in principle, justify other than a 100% weighting of the loans secured.

B asel II is consistent with U.S. regulatory expe-rience. After assessing the large number of bank failures from the 1980s, the FDI

C noted, “Compared with surviving banks, banks that subsequently failed in the 1980s had higher ratios of (1) commercial real estate loans, (2) commercial real estate loans to total real estate loans, (3) non-current commercial real estate loans to total commercial real estate loans, and (4) real estate charge-offs to total charge-offs.”4 Regardless of the regulatory prejudice against commercial real estate lending, covered banks are required to develop a system that distinguishes the probability a loan will default within one year. The analysis must be based on at least ? ve years of sta-tistically sound data and allow the bank to assign a loan to a credit class related to the mortgagor’s abil-ity to pay interest and repay principal on a timely basis. The risk class determines the risk weight of a loan and should affect the pricing of the loan and the allowance for loan losses. Banks must estimate the total loss exposure for a credit; the total loss re? ects both expected losses and unexpected losses. Banks are required to ensure management has established an allowance for loan losses equal to credit losses expected over the next year. Capital, by contrast, covers unexpected losses. The risk weight assigned commercial mortgage loans must be calibrated to cover unexpected losses at the 99.9-percent con? -dence level. Once a bank determines the maximum loss for a loan within one year, there ought to be a 0.1-percent probability that it will produce a larger loss than modeled.

Statistical Analysis

Basel II requires covered banks to model the unex-pected probability of default within one year and the LGD. Loans with a higher unexpected probability of default and/or a higher LGD must be assigned higher risk weights that dictate more risk-based capital. The LGD must re? ect the losses incurred during a period of stress when more defaults than normal occur. Basel II also considers the correlation of loan losses within an asset class, the maturity of the loan and the exposure at default.

Each covered bank must determine the likelihood a given loan will default. Invariably, such analysis for commercial real estate loans will assess debt-service coverage (net operating income/annual principal and interest) and loan-to-value ratios, prior experience with the debtor, management experi-ence, property type, age and location of the project, loan seasoning and other factors unique to each bank’s internal assessment model. B asel II sets a 35-percent (30 percent with very high overcollat-eralization) minimum LGD ratio for loans secured by income-producing real estate properties. Fitch Ratings recently determined that the average loss on commercial loans that back defaulting commercial mortgage-backed securities was 40 percent for the 1990 to 2003 time period.5 Many bank models have preliminarily adopted a 45-percent LGD estimate for commercial real estate loans.

Using the historical commercial real estate loan net loss experience of large U.S. banks, it is possible to approximate expected and unexpected probabilities of loss. The analysis is illustrative:

Business Plans, Portfolio Management and Basel II

22B ANK A CCOUNTING & F INANCE J UNE–J ULY 2006

total loss = expected loss + unexpected loss

unexpected loss = probability of unexpected default x LGD The expected loss is comparable to the average or mean loss experience over a given period of time.

The unexpected loss may be approximated by the sample standard deviation of loss over the same

time period multiplied by 3.08 to achieve a 99.9-per-cent con? dence level of recognizing the maximum loss within a year. Stan-dard deviation measures the dispersion of losses above or below the mean. More risky loans possess a higher sample standard deviation of loss and re-quire additional risk-based capital. The probability of unexpected default equals the unexpected loss divided by the LGD. Exhibit 5 illustrates the probability of default for the expected loss, the probability of default for the unexpected loss estimated at the 99.9-per-cent con? dence level and the implied risk weight assuming a 45 percent LGD for commercial real estate loans over various time periods between 1991 and 2004. The early 1990s were bad years for real estate investors and bank lenders alike. By ex-tending the analysis back 14 years, commercial real estate loans are viewed as more risky (135-percent risk weight) than the current Basel Accord (100-percent risk weight). A 12-year period of analysis treats commercial loans roughly equivalent (108-percent risk weight) to the Basel Accord. A more recent investigation over the past 10 years or the minimum ? ve-year period shows commercial real estate loans to be far more favorable; the implied risk weight is 79 percent over the past decade and just 45 percent over the past ? ve years.6 The precise time period analyzed by banks to estimate risk weights is important and will be closely evaluated by bank regulators.

The Economic Environment In part, B asel II requires covered banks to subject their statistical analysis of loss to stress testing. What factors could lead to even higher unexpected losses than modeled? Commer-cial loan net losses vary over time and correlate positively and negatively with a variety of external economic factors. A correlation coef? cient can range from plus one (perfect positive correlation) to nega-tive one (perfect negative correlation). The higher the correlation, the more important the external factor is to the determination of losses:

Gross domestic product (GDP) (–.39). The cor-relation between GDP growth or contraction and net losses on commercial real estate loans is modestly negative. Weak economic growth or a contraction lead to higher loan losses as property vacancy rates rise and effective rental rates fall. Long-term mortgage interest rates (+.60). The correlation between interest rates and net losses is positive. Higher interest rates reduce the opportunity to lower debt-service obliga-tions by refinancing an existing loan and place upward pressure on capitalization rates, which reduce the opportunity to sell the property for a higher value.

Unemployment rate (+.84). The correlation be-tween the unemployment rate and net loan losses

is very positive. As more

people seeking work are unable to obtain a job,

there is less demand for offices, hotels and other space. Higher va-cancy rates, additional bad debts and pressure on rental rates lead to lower net operating in-come needed to cover

principal and interest.Exhibit 5

Large U.S. Bank Commercial Loan Default Experience (Risk weight based on probability of unexpected loss with 45% LGD)Time Period Years Probability of Expected Default Probability of Unexpected Default Implied Risk Weight 1991 to 200414 1.69%8.28%135%1993 to 2004120.78% 4.45%108%1995 to 2004100.20% 1.32%79%2000 to 2004 50.24%0.34%45%Adopting banks will ? nd some assets more favorable under Basel II.Business Plans, Portfolio Management and Basel II

Source: FDIC and Basel II.

J UNE –J ULY 2006 B ANK A CCOUNTING & F INANCE 23

based on recent experience show commercial real estate to have relatively low risk weights. The shift in risk weight for commercial real estate loans and all other bank credits in? uence the attractiveness of lending in general and speci? c types of loans in particular when crafting the business plan. The busi-ness plan must consider how a different economic environment and a vastly dissimilar risk weight modeled would cause the business plan to adjust the optimal amount of portfolio credit risk. The risk weight recently modeled might suggest competitive strength when weakness is warranted. The risk weight recently derived may suggest good mar-ket opportunities when the outlook is truly poor. Pillar I requires banks to model their own risk weight subject to super-visory review and the standard global credit risk statistical model. Newly derived risk weights will in? uence the derivation of optimal portfolio strate-gies developed within the business plan.Pillar I and Capital

Banking organizations need risk-based capital to support credit risk within the portfolio, market risk within the trading portfolio and operational risk. Basel II requires the allowance for loan losses to at least equal expected loan losses over the next year. Assuming subordinated debt or Tier II capi-tal equals the required capital charge for market risk and operational risk, Tier I equity capital is available to support credit risk. The typical large

U.S. bank allocates 66 percent of assets to loans, 30 percent to securities, three percent to cash

and one-percent to premise and other ? xed assets. Assume the risk-based capital function fol-lows for assets other than loans: 10 percent for the investment portfolio, zero percent for cash

and 100 percent for premise. What amount of capital is need-ed to support credit risk within the lending portfolio?Bank failures (+.88). The correlation between annual bank and thrift failure is very high with commercial real estate loan losses given their importance to the last banking debacle. Yet, note that the implied risk weights for commercial real estate mortgages will likely decline under Basel II modeling unless the time period assessed stretches back to the period problematic to the banking sector.Regulatory supervisors will ? nd bank models lead to different perceptions of risk depending on the time period used to develop the model. Longer test hori-zons allow an opportunity to fully capture a more hostile environment that leads to higher expected and unexpected losses and more bank failures.To illustrate how eco-nomic conditions vary, Exhibit 6 illustrates the average economic condition over two ? ve-year periods: 1990 to 1994 and 2000 to 2004. The mean statistics clearly indicate that risk weights are de-pendent on the time period modeled. The early

1990s experienced lower economic growth, higher

levels of in? ation, elevated rates of unemployment

and higher mortgage interest rates than the more recent period. Regulatory supervisors and bankers must balance the importance of testing data in an unusually harsh environment versus the cost of extending data searches to time periods that may or may not be seen again. Commercial real estate loan losses and poor man-agement caused hundreds of banks and thrifts to fail each year in the late 1980s and early 1990s. Models Exhibit 6Economic Indicators (Average annual change)Indicator 1990–19942000–2004Trend GDP 2.34% 2.60%Favorable Consumer price index 3.64% 2.56%Favorable Unemployment rate 6.60% 5.24%Favorable Mortgage interest rate 8.69% 6.65%Favorable Annual bank failures 180/year 6/year Favorable Banks are able to earn consistently a return on equity greater than their required return on equity trade at a price/book premium.Business Plans, Portfolio Management and Basel II

Source: Federal Reserve Board of Governors and FDIC.24 B ANK A CCOUNTING & F INANCE J UNE –J ULY 2006

Tier I capital = loans+ securities + cash+ premise Tier I capital = .66(X%) + .30(10%) + .03(0%) + .01(100%)

As of 2005, large U.S. banks possess Tier I eq-uity capital of approximately 7.7 percent. The Tier I capital adjusted by the

assumed capital charge

required for investments,

cash and premise is 7.3 per-

cent. Based on a minimum

level of risk-based capital

of 10.0 percent to remain

well capitalized, the average risk weight that can be tolerated within the large bank loan portfolio is 110.0 percent [adjusted Tier I capital of 7.3% = .66 (.1X%)]. Preliminary results of quantitative impact studies suggest most loan risk weights will decline under Basel II. Covered banks will ? nd that existing levels of risk-based capital comfortably exceed evolving regulatory rules.

Management can deploy the excess capital to cre-ate value for shareholders by evaluating alternatives to (1) decrease capital by paying higher dividends or repurchasing stock, (2) originate high-risk loans, or (3) acquire high-risk competitors. Banks with too much equity invariably are penalized with lower price/book ratios because their return on equity (net income/equity) is adversely affected by a low equity or leverage multiplier (assets/equity). Most banks will ? nd that Basel II will free up additional capital unless the institution pursues a high-risk portfolio policy. Yet, will the regulatory scheme al-low a low-risk portfolio policy to be implemented? The answer depends on the implied risk weights of bank assets and the existence of a binding Tier I leverage capital rule.

Using the historical net loss experience of large U.S. banks over the 2000 to 2004 time period, it is possible to approximate expected and unexpected probabili-ties of loss. The expected loss is comparable to the average or mean loss experience over the time period sampled. The unexpected loss may be approximated by the sample standard deviation of loss over the same time period multiplied by 3.08 to achieve a 99.9-con? dence level of recognizing the maximum loss within a year. The probability of unexpected default equals the unexpected loss divided by the assumed LGD. Assuming a 45.0-percent LGD for commercial and industrial (C&I) loans, the implied risk weight is 88.0 percent (say, 90.0 percent); as-suming a 25.0-percent LGD for residential mortgage loans, the implied risk weight is 12.0 percent (say, 15.0 percent); and assuming an 85.0-percent LGD for consumer loans, the implied risk weight is 44.0

percent (say, 45.0 percent).

Among the three illus-

trative loan types, large

banks currently commit

about 50.0 percent of their

portfolio to residential

loans, 35.0 percent to C&I loans and the remaining 15.0 percent to consumer loans. The total loan portfolio comprises about 66.0 percent of total assets.

Exhibit 7 illustrates how much capital banks would need to support the typical loan portfolio outlined above and to comply with a 10-percent risk-weighted capital charge.

Exhibit 7

Capital Calculation for the Typical Loan Portfolio Loan (% of assets) x (% of loans) x (implied risk weight) x (10%) = capital

C&I.66 x .35 x 90% x .1= 2.08% Residential.66 x .50 x 15% x .1=0.50% Consumer.66 x .15 x 45% x .1=0.45% Total Capital 3.03% The bank needs risk-based capital of about three percent to support credit risk within the modeled portfolio. Yet, Tier I leverage capital must continue to at least equal ? ve percent. Required Tier I capital adjusted by the assumed capital charge supporting investments, cash and premise is 4.6 percent. Based on a minimum level of Tier I leverage capital of ? ve percent to remain well capitalized, the average risk-weighted loan now tolerated within the loan portfolio is 70.0 percent [adjusted Tier I capital of 4.6 percent = .66 (.1X%)]. Such an average risk weight would accommodate the following portfolios allo-cated between two types of loans considered: Seventy-three–percent high-risk C&I loans and 27-percent low-risk residential loans

Almost 400 banks and thrift institutions

failed in 1990 versus zero in 2005.

Business Plans, Portfolio Management and Basel II J UNE–J ULY 2006 B ANK A CCOUNTING & F INANCE25

Fifty-six–percent high-risk C&I loans and 44-

percent medium-risk consumer loans

Any proportion of low-risk residential loans and

medium-risk consumer loans creates a portfolio

with excess capital

The existence of a minimum Tier I capital rule

of five percent penalizes banks originating and

maintaining a portfolio of

low-risk and medium-risk

loans within the existing

portion of assets allocated

to lending. The Tier I capi-

tal rule is binding rather

than the risk-based capital

ratio. Such a result creates business plan incentives

to gravitate toward high-risk assets under Basel II.

Summary

B asel II changes the amount of risk-based capital

banks will be required by regulation to maintain

against credit risk among other factors. Most quanti-

tative studies for banks in the United States suggest

risk weights will be lower than now required for the

majority of loans. The market and regulators will

need to assess the impact of lower risk weights on

business plans structured to allow banks to remain

viable and to create value for shareholders. Adopt-

ing banks will likely ? nd “excess” capital under

Basel II that will allow the bank to use that capital to

originate higher-risk loans, acquire banks with high-

risk portfolios, pursue a growth strategy or allocate

more assets to lending. There appears to be little

advantage to pursue a low-risk credit portfolio if

minimum Tier I leverage capital rules remain part of

the regulatory landscape. Banks could also consider

strategies that reduce capital by aggressive dividend

or repurchase plans. However, market discipline

provided by the market, credit-rating agencies and

regulatory supervisors all reduce the opportunity to

lower capital by a signi? cant amount. Banks must

still meet Tier I leverage capital rules.

Regardless of how bankers revise their business

plans, it is instructive to emphasize that risk weights

may change over time if a more hostile economic,

? nancial or competitive environment occurs in the

future. Business plans developed today must assess

how a strategy would change, if at all, if risk weights

increased or decreased from those currently estimat-

ed. Bankers once felt very comfortable making loans

to developing countries because “countries do not

go bankrupt.” Later, bankers felt secure allocating

a high proportion of loans

to the agriculture sector

and/or to the petroleum

industry because “we

will always need food

and oil.” More recently,

bankers felt secure mak-

ing loans funding commercial real estate given the

existence of “valuable and tangible collateral unable

to be relocated.” Bankers conversant with history

well remember the losses that followed from each of

the portfolio allocations once considered prudent.

Basel II is model driven. Business plans must in-

corporate the models, apply informed judgment and

consider input from regulatory supervisors and the

market. The business plan must integrate input from

all three pillars of Basel II and stress-test the model’s

results statistically and judgmentally. It is instructive

to remember A T ALE OF T WO C ITIES by Charles Dickens

in relationship to the banking environment. It cur-

rently is the best of times, but the business plan must

consider the consequences of the worst of times.

Endnotes

1James C. Van Horne, F INANCIAL M ARKET R ATES AND F LOWS, Ch. 8 (6th

ed., Upper Saddle River, NJ: Prentice Hall, 2001).

2Mark J. Flannery and Sorin M. Sorescu, Evidence of Bank Market Dis-

cipline in Subordinated Debenture Yields: 1983–1991, 51 J. F IN. 4 (1996).

3 B asel II: International Convergence of Capital Measurement and Capital

Standards: A Revised F ramework, The B asel Committee on B anking

Supervision (2004).

4 FDIC, An Examination of the Banking Crises of the 1980s and Early 1990s,

vol. 1 (1997).

5 Fitch Ratings, Rating Single-borrower Commercial Mortgage Transactions

(2004) and 2003 CMBS Conduit Loan Default Study (2003).

6William C. Handorf, The Commercial Mortgage Market and Basel II, 34

R EAL E STATE R EV. 2 (2005).

The precise time period analyzed by banks

to estimate risk weights is important.

Business Plans, Portfolio Management and Basel II

26B ANK A CCOUNTING & F INANCE J UNE–J ULY 2006

企业该如何有效的利用百科平台来做推广

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社会企业的定义 - Isabelle 20101221

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网络流行语“很X很XX”的语言学解读(定稿)(2)

编号：10001121207 南阳师范学院20 12 届毕业生 毕业论文（设计） 题目：网络流行语“很X很XX”的语言学解读 完成人：黄叶文 班级：2010-12 学制：2年 专业：汉语言文学 指导教师：张春雷 完成日期：2012-03-30

目录 摘要 (1) 一、“很X很XX”流行语的出现及形成原因 (1) （一）产生及发展 (1) （二）形成原因 (2) 二、“很X很XX”的语言学解读 (3) (一）语音特点 (3) （二）语义特点 (3) （三）语法特点 (4) （四）语用分析 (7) （五）“很X很XX”模式的构形理据——能产性理论 (8) 三、“很X很XX”的使用情况 (8) （一）广告中对于产品的宣传 (8) （二）评价社会热点 (9) （三）在标题中运用 (9) 四、“很X很XX”的流行原因 (10) （一）客观原因 (10) （二）主观原因 (11) 1.心理因素 (11) 2.社会共知基础 (12) 五、看待态度及发展前景 (12) 结语 (13) 参考文献 (14) Abstact (14)

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