Central banking, liquidity risk, and financial stability Lecture, Summer 2009, Technical University of Berlin, Ulrich Bindseil, European Central Bank

3. Bank default, Capital, and pro-cyclicality A. Bank Default .............................................................................................................1 B. Default probabilities and ratings ...............................................................................4 C. Modelling default risk ...............................................................................................8 D. Basle II – Capital adequacy regulation ...................................................................13 E. Fair value (mark-to-market) accounting..................................................................16 G. Initiatives to reduce pro-cyclicality.........................................................................18 F. Wrap up questions ...................................................................................................20

A. Bank Default

A bank default is the ultimate event of bank stress. It will mean losses for the creditors of the bank, and maybe even cause some creditor default. In any case, some contagion will occur. The anticipation of relatively high probability of defaults will prevent many transactions from taking place, or even the increase of uncertainty alone may lead markets to become inefficient. Consider some examples of definition of default.

Default according to rating agencies

According to the rating agency Standard & Poors. “A default is recorded on the first occurrence of a payment default on any financial obligation, rated or unrated, other than a financial obligation subject to a bona fide commercial dispute; an exception occurs when an interest payment missed on the due date is made within the grace period.” If default occurs, then the part of the claims that can be recovered are reflected in the so-called recovery ratio. A complementary measure is loss given default (LGD), which is equal to 1 – the recovery ratio. For example, according to Moody’s, typical loss rates in the event of default for senior bonds, subordinated bonds and zero coupon bonds are 49%, 68%, and 81%, respectively (but they are expected to be around 10% for Lehman senior bonds). In S&P’s 2008 corporate default study, the following histogram of recovery rates is provided:

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Default according to Basle II

Basle II defines “default” in the context of internal credit risk models of banks. According to the Basle II document, default occurs when “… The obligor is past due more than 90 days on any material credit obligation … Overdrafts will be considered as being past due once the customer has breached an advised limit…” In the Capital Requirements Directive (which determines the transposition of Basle II in the law of EU member states), “default” for the banks’ Internal Rating Based Systems (for calculating capital charges) has been defined in a more flexible way, also referring to the "unlikelyness" to pay.

A default event according to some contractual documentation

A “default event” is also often a concept used in legal documentation underlying a financial contract. This typically allows the claimant to do something, e.g. demand repayment of the loan in advance of its normal due date (also known as accelerating the loan). In a revolving credit facility, the occurrence of an event of default normally also allows the lender to cancel any obligations to make further loan advances. In a repo trade or a collateralised OTC derivative, the event of default allows the receiver of securities to liquidate those.

Default triggered by supervisory authorities

Bank defaults occur typically when a payment moratorium is imposed on a bank by the banking supervisor, for the sake of protection of the creditors of the bank and fairness amongst the creditors. A payment moratorium means by definition that payment duties cannot be met in time, i.e. default occurs. A bank in the US can also take the initiative and file a Chapter 11 Bankruptcy petition,

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Case study: Lehman’s default

Lehman Brothers Holdings Inc. decided in a hopeless situation to take the initiative and file a Chapter 11 Bankruptcy petition: “NEW YORK, September 15, 2008 – Lehman Brothers Holdings Inc. (“LBHI”) announced today that it intends to file a petition under Chapter 11 of the U.S. Bankruptcy Code with the United States Bankruptcy Court for the Southern District of New York.”

The consequence of the Chapter 11 filing is a payment moratorium, that a bankruptcy Judge and the Creditor’s Committee take over, and possibly that liquidation of assets is sought with the aim to maximise value for the creditors. For the by far largest foreign arm of Lehman, the London subsidiary, a moratorium was declared by the FSA. According to Newswire reports: Lehman Brothers Holdings Inc.'s International Europe unit has been placed in administration and PricewaterhouseCoopers LLP will handle the winding down of the business, the accounting firm said today.

Also for Lehman Brothers Bankhaus AG in Frankfurt, a moratorium was declared by the Supervisors on 15 September 2008, also without the bank having defaulted on any obligation. At the same time, the bank was put under receivership. On 28 October, BaFin declares the “Entschädigungsfall” for the depositors who are protected by the deposit insurance (Einlagensicherungsfond). The insolvency proceedings were launched on 12 November 2008. The first meeting of the creditor’s committee took place on 17 March 2009. According to the relevant ad-hoc Information:

Frankfurt am Main, den 17. März 2009; In dem Insolvenzverfahren der Lehman Brothers Bankhaus AG i.Ins. findet heute die erste Gläubigerversammlung statt. Auf der Gläubigerversammlung berichtet der Insolvenzverwalter zum Stand des Insolvenzverfahrens. Danach gibt es folgende wesentlichen Ergebnisse:

1. Es haben 457 Gläubiger Forderungen in einer Gesamthöhe von ca. EUR 38,2 Mrd. zur Insolvenztabelle angemeldet, wobei Forderungen in einer Gesamthöhe von ca. EUR 5 Mrd. doppelt angemeldet worden sind. Der Insolvenzverwalter hat die angemeldeten Forderungen dem Grunde und der Höhe nach noch nicht abschließend geprüft.

2. Als Insolvenzmasse sind insbesondere Guthaben bei Kreditinstituten und der Bundesbank in Höhe von ca. 1,8 Mrd. EUR gesichert.

3. Darüber hinaus besteht die Insolvenzmasse im Wesentlichen aus Wertpapieren, Kreditforderungen und Forderungen aus Derivatgeschäften. Die Werthaltigkeit und Einbringlichkeit dieser Vermögenswerte ist gegenwärtig noch nicht zuverlässig einzuschätzen.

4. Der Insolvenzverwalter geht von einer Insolvenzquote oberhalb von 10 % aus. … Zur voraussichtlichen Dauer des Insolvenzverfahrens und zum Zeitpunkt der Feststellung der endgültigen Insolvenzquote lassen sich derzeit noch keine zuverlässigen Prognosen treffen.

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B. Default probabilities and ratings Prior to default, there is no way to discriminate unambiguously between firms that will default and those that will not – only probabilistic assessments of the likelihood of default are possible.

Rating agencies’ main business is to assign ratings, which are essentially estimated default probabilities. What the ratings mean in terms of default probabilities is revealed at least ex post, by putting up statistics of defaults in the different rating categories. The following qualitative definition of ratings in practice does not mean too much: Investment Grade

• AAA : the best quality borrowers, reliable and stable (e.g. German Government)

• AA : quality borrowers, a bit higher risk than AAA (e.g. Japan, HSBC) • A : economic situation can affect finance (e.g. BASF, Greece, Commerzbank) • BBB : medium class borrowers, which are satisfactory at the moment

(Hungary, ACCOR) Non-Investment Grade

• BB : more prone to changes in the economy (Turkey) • B : financial situation varies noticeably (Eastman Kodak) • CCC : currently vulnerable and dependent on favorable economic conditions

to meet its commitments (Ford) • CC : highly vulnerable, very speculative bonds • C : highly vulnerable, perhaps in bankruptcy or in arrears but still continuing

to pay out on obligations • D : has defaulted on obligations and S&P believes that it will generally

default on most or all obligations

Being more useful than those vague definitions, the following table summarises default and migration probabilities for corporates (including banks, using data from 1981-2008; S&P, 2009; number in parentheses are standard deviations).

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In 2008, the transition probability matrix was much worse, stressing the fact that ratings are a “through the cycle” concept. Also the frequency distribution of ratings in the column “issuer” is of interest.

The following chart and table further confirm the huge differences between actual default rates in good and bad years for a rating category (S&P, 2009).

The following table demonstrates that financial institutions tend to default less often than corporates.

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Finally, the S& P annual corporate default study for 2008 notes that Lehman has been the by far biggest corporate default ever in terms of outstanding debt, the top three being:

1. Lehman, 2008, USD 144 billion

2. WorldCom, 2002, USD 30 billion

3. Enron, 2003, USD 11 billion

Mapping ratings of ECAI ratings into an internal rating scale As there are various different rating scales (e.g. there are three major rating agencies), any single user of ratings must find a way to map all of those into her own rating scale. For instance, the ECB applies the following harmonised rating scale. Credit quality steps 1 and 2 of this Eurosystem harmonised rating scale fulfil the minimum requirements for high credit standards of collateral, i.e. allow for use as EUrosystem collateral.

ECB Credit quality steps 1 2 3

Eligible Non-eligible

DBRS R-1H R-1M, R-1L R-2H, R-2M

FitchRatings F1+ F1 F2 Moody’s P-1 P-2

Short-term

Standard & Poor’s A-1+ A-1 A-2

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ECB Credit quality steps 1 2 3

Eligible Non-eligible DBRS AAA/AAH/AA/AAL AH/A/AL BBBH/BBB

FitchRatings AAA/AA+/AA/AA- A+/A/A- BBB+/BBB/BBB-Moody’s Aaa/Aa1/Aa2/Aa3 A1/A2/A3 Baa1/Baa2/Baa3

Long-term

Standard & Poor’s AAA/AA+/AA/AA- A+/A/A- BBB+/BBB/BBB-

Every investor who relies on ratings of rating agencies and who wants to have rule-based eligibility criteria and limit setting should establish such a mapping. Also, in case of multiple ratings, he or she will have to find an aggregation rule for ratings.

How to measure the predictive power of ratings? We follow again S& P (2009): To measure rating performance, the cumulative share of issuers by rating may be plotted against the cumulative share of defaulters in a Lorenz curve. To build the Lorenz curve, the observations are ordered from the low end of the ratings scale ('CCC'/'C') to the high end ('AAA'). A summary statistics of the Lorenz curve is the Gini-coefficient. The Gini coefficients G may be defined in terms of the chart below by dividing area B by the total area A + B, i.e. G = B / (A+B). In other words, the Gini coefficient captures the extent to which actual ratings accuracy diverges from the random scenario and aspires to the ideal scenario. If Standard & Poor's corporate ratings only randomly captured default risk, the Lorenz curve would fall along the diagonal. The Gini coefficient would thus be zero. If corporate ratings were perfectly rank-ordered so that all defaults occurred only among the lowest-rated entities, the curve would capture all of the area above the diagonal on the graph and its Gini coefficient would be one. One may imagine the Gini co-efficient as a kind of output variable of the rating production function. If x is the money spent on the rating of a set of companies (for data collection, visiting the companies, interviewing the management, analysing the product markets of the companies, analysing the balance sheets and the business models, the competitive conditions, the role of macro-economic factors, the vulnerabilities to exogenous shocks, etc.), then: G = G(x) with G(x) >0 for all x in R+, G(0) = 0, dG/dx > 0, d²G/d²x < 0, and lim 1)( <

→∞xG

x

Indeed, a Gini-co-efficient of 1 normally cannot be reached because some risk factors driving default are purely exogeneous, i.e. even with a hypothetical infinite resource input, it is impossible to predict which company will be hit by a meteor. The Gini-coefficients of S&P are around 0.8 for corporates over a one-year horizon, and around 0.6-0.7 over a seven years horizon.

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C. Modelling default risk How can default risk be estimated? Previously, it was argued that higher default risk is one of the drivers of a liquidity crunch, which, once set off, has a fatal dynamic which may bring to collapse financial institutions which were sound under normal circumstances. Rating agencies have published detailed methodological studies on how they rate various financial instruments like unsecured corporate bonds, covered bonds, ABS, government bonds, etc. In the following, we will only look briefly at the idea of structural credit risk models, as this fits well with the story on what drives financial crisis.

In structural credit risk models, there are three main elements that determine the default probability of a firm:

• Value of firm's assets = the present value of future cash flows produced by the firm's assets discounted at the appropriate discount rate.

• Asset Risk: the volatility of the asset value.

• Leverage: the extent of the firm's contractual liabilities. Whereas the relevant measure of the firm's assets is always their market value, the book value of liabilities relative to the market value of assets is the pertinent measure of the firm's leverage, since that is the amount the firm must repay. Default occurs according to Crosbie and Bohn (2003) not when asset value reaches the book value of their total liabilities. Many firms continue to trade and service their debts. The long-term nature of some of their liabilities provides these firms with some breathing space. The default point, the asset value at which the firm will default, generally lies somewhere between total liabilities and current, or short-term, liabilities. In other words, the default point will depend on solvency and liquidity.

The idea of Moody’s KMV model follows Merton (1974) to estimate default probabilities as follows.

• Estimate asset value and volatility: In this step the asset value and asset volatility of the firm is estimated from the market value and volatility of equity and the book value of liabilities.

• Calculate the distance-to-default: The distance-to-default (DD) is calculated from the asset value and asset volatility (estimated in the first step) and the book value of

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liabilities. The distance to default may be expressed as number of standard deviations of the asset value.

• Calculate the default probability: The default probability is determined directly from the distance-to-default and the default rate for given levels of distance-to-default.

The following figure from Crosbie and Bohn (2003, 13) illustrates the idea of structural credit risk modelling.

The elegant way to model default risk in a structural way is to apply continuous time finance tools and to model asset values as being driven by a Brownian motion process. This allows to use the toolbox of option pricing. Adopt the following notation:

oA = value of firm’s assets at point in time 0

tA = value of firm’s assets at point in time t T = end of the period considered, i.e. when company is dissolved (or maturity of the option)

tD = debt at point t = expected rate of return of the asset Aμ = volatility of the asset Aσ

ε = a standard normal random variable Assume that the asset price follows a process

tdtAdA AA εσμ +=

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Then, using continues times finance tools, and assuming that default occurs when assets fall below the level of debt, one can show that ( ()Φ is the cumulative Gaussian distribution):

⎟⎟⎠

⎞⎜⎜⎝

⎛ −+Φ=<

ttDADAP

A

AAttt σ

σμ )2/()/ln()(2

0

If we go for a less elegant ad hoc modelling (which will cause problems if we want to use the result in a more general context), we could simply say that and the level of debt is D, then

),( 20 tAA At σ≈

⎟⎟⎠

⎞⎜⎜⎝

⎛ −−Φ=<

tDADAP

At σ

)()( 0

Obviously, the PD (probability of default) will be the higher:

• The lower initial capital )( 0 DAC −= , or the higher will be the leverage; • The higher the volatility • The longer the horizon

Examples: (i) A bank has over the one year horizon assets the value of which is normally distributed with expected value of EUR 100 and a volatility of 4%. What capital does it need if it wants to have a S&P rating of AA? (ii) If a bank is leveraged 20 times, what is the maximum asset volatility in % it can bear to maintain a AA rating? Default probabilities and the price of credit-risky debt are linked in fact through the Black-Scholes-Merton option pricing model, as applied by Merton 1974 to the pricing of the liabilities of a company. Assume again the notation above. In fact, owning an equity is nothing else in terms of pay-offs than owning a European call option on the assets A with strike price D. Adopt the further notation:

),( DACt = The value in t of a call option on the asset A with a strike price of D ),( DACT = The pay-offs at maturity of a call option on A with a strike price of D

tE = nominal (book) value of equity at point in time t

tD = nominal (book) value of debt at point in time t ( is the strike price of the option)

TD

)(DVt = Value of debt at point in time t )(EVt = Value of equity at point in time t )(DVT = Pay-off of debt at point in time t )(EVT = Pay-off of equity at point in time t

As is also illustrated in the charts below, the pay-offs in T of holding the firm's debt can be written as follows:

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),0max(),()( TTTT DADACEV −== ),()( DACADV TT −=

Of course, this verifies the equation

)()()( TTTTT DEADVEV ===+ Finally, using the put-call parity (which is immediately obvious), one can also express this in terms of put options:

),0max(),()( TTTTTT ADDDAPDDV −−=−= ),()( DAPDAEV TTTT −−=

The pay-offs depending on asset values are illustrated in the following chart, namely for a company with debt of 4 (for options with a strike price of 4).

0

1

2

3

4

5

6

7

8

9

0

0.4

0.8

1.2

1.6 2

2.4

2.8

3.2

3.6 4

4.4

4.8

5.2

5.6 6

6.4

6.8

7.2

7.6 8

Value of assets at maturity

Pay-

off o

f ins

trum

ents

Pay-off CallPay -off putPay-off APay off D

Moving on to valuation, one uses the principles that packages of financial instruments that have the same pay-offs at some stage must also have the same value at an earlier stage (arbitrage pricing). Moving from asset pay-offs of options to valuation of options requires introducing the stochastics and risk aversion for the period between the valuation and the maturity (the deterministic pay-offs). Naively, one could say that the value of an option (or of equity and debt) should be the discounted expected cash flows. So one would essentially match the pay-off chart above with a density function.

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0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0 1 2 3 4 5 6 7 8

Pay

-off

debt

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

prob

abili

ty

Pay-off debt probability distrib of assets in T

The value of the debt would thus be (with being the density function of the asset value in T):

()ATf

( ) dxxfxDDDV ATTTt )(),0max()(0

−−= ∫+∞

−

Unfortunately, this is neither general, nor correct, nor easy to handle. Instead, an elegant solution under the assumption of normality for the underlying asset pricing process is the Black-Scholes formula. Accordingly:

[ ])()(),()( 201 dNeDAdNDDAPDDV rTTTTTT −+−−−=−= −

TTrDAd T

σσ )5.0()/ln( 2

01

++=

Tdd σ−= 12

Deriving these formulas is more tricky, but not needed here. Instead one can play around with them in excel to get a feeling on how the debt structure of a company affects the valuation of its assets. We will apply the Merton model later on when we establish the inter-linked accounts of the economy (the "financial accounts") to understand how asset shocks propagate through the different sectors. The modelling of the default point and maybe of the pay-offs in the Merton (1974) model appear in fact somewhat simplistic. In reality, we should expect that the default is costly in itself (i.e. asset values drop in a discontinuous manner at default), and that liquidity plays a role, since ultimately, liquidity matters for default. Maybe a company is illiquid although it is solvent (“solvent” means that asset values exceed liabilities), in other words, in the very short term, assets cannot be liquidated at a reasonable price, but in the medium term they could. (in other words, the company is only insolvent under an extreme form of mark-to-market accounting, ignoring completely the time dimension of liquidating assets).

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For instance Davydenko (2004, “When do firms default? A study of the default boundary”, memo, London Business School), studies more deeply the nature of the default point, and in particular looks at the role of liquidity. Only illiquidity, i.e. the inability to obtain liquid funds to fulfil payment duties, necessarily causes default. He finds that, while in general both asset value relative to debt and liquidity are powerful default predictors, their relative importance depends on whether frictions make it difficult for a particular firm to raise outside financing in times of need. Moreover, as neither factor predicts default perfectly, there are many low-value and low-liquidity firms that are able to avoid default. Thus, boundary-based default predictions result in significant errors. These results would suggest that the apparent inability of structural models to explain default probabilities and spreads over short horizons may be attributable to their modelling of the default trigger. As long as default has not occurred, equity holders face a choice between continuing operations under the current regime and substantially reorganizing in bankruptcy. For the firm to continue, its current liabilities must be met or renegotiated, and cash reserves maintained. External financing can be raised if needed, but market frictions make this costly. Current debt can be renegotiated in an informal workout, as long as this yields a positive net surplus. Davydenko’s model predicts that firms with low costs of external financing default when the continuation value of assets is low. By contrast, if external funds are costly, a liquidity crisis may force reorganization even if the going-concern surplus is still substantial. The polar assumptions of purely-value-based and liquidity-based defaults arise as special cases.

D. Basle II – Capital adequacy regulation Capital is a reserve against unexpected losses. It should help banks to continue to provide credit throughout the economic cycle and promotes public confidence in the banking system. It also gives some disincentives against excessive risk taking of the owners of the bank (as the downside is limited by the capital under limited liability, i.e. the higher the capital, the higher the downside of “reckless” risk taking for the equity holders.). The Basle II capital adequacy requirements are relevant in the context of financial stability for a number of reasons:

• Capital adequacy requirements in principle aim at ensuring that the above-mentioned benefits of high capital ratios materialise.

• Capital adequacy requirements, if not met, may lead to a moratorium by supervisors, and hence to a sort of default.

• Capital adequacy requirements may lead to “pro-cycle” behaviour of banks, which is not in line with the idea to promote financial stability and of course not an intention of the Basle rules.

Banks are subject to the Basle Capital adequacy requirements which require banks to hold a minimum capital against risk-weighted assets. Inability to meet capital requirements may lead to the banking licence being withdrawn by the supervisor.

The Basel Accord of 1988 introduced a conceptually simple rules-based system that required internationally active institutions to hold minimum regulatory capital of 8% of risk-weighted assets to offset credit risk. The 1988 Accord was amended in 1996 to broaden the focus of regulatory capital to include market risk. Certain institutions

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were permitted to use their own models to calculate their market risk capital requirements.

The Basle II requirements published in 2004 foresee capital charges for market risk, credit risk, and for operational risk, and innovate substantially on the credit risk charges for more sophisticated institutions by introducing the “Internal Rating Based” approach (but also the so-called “standardised approach” is more risk-sensitive than Basle I). While the Basle II framework is not binding per se, it was implemented in the EU through the Capital Requirements Directive CRD and the subsequent national legislation.

Member States must transpose, and firms should apply, the Directive from the start of 2007. During 2007, credit institutions and affected investment firms can choose between the current Basel 1 approach and the simple or medium sophistication approaches of the new framework. The most sophisticated approaches will be available from 2008.

For market risk, the capital charge (since 1996) essentially reflects a Value-at-Risk estimate of interest rate, equity price, exchange rate, and commodity price risk. The Value at Risk is a quantile of the probability distribution of price changes over a certain horizon. For instance: The 95% one year VaR of a certain portfolio is the loss that will be exceeded with a 5% probability. Let α be the confidence level (e.g. 95%), t=0 the day of the VaR calculation, T the time horizon of the VaR, and x is the change of value of a financial asset over the period T, then:

)(xVaRTα αα −=1))

),0( 2xNx σ≈

is therefore defined by < ()( xVaRTx T(P

Assuming normality, e.g. that over the horizon T, , then

( ) xT xVaR σαα −Φ= − 1)( 1

( )α−Φ− 11

The values of are easily calculated in excel or can be found in any statistics book annex. For instance they are 2.33 and 1.96 for the 99% and 95% confidence level, respectively.

Assuming that innovations to asset prices are i.i.d., and λ is a scaling constant, then,

)()( xVaRtxVaR TTα

λα =

Basle II specifies that the market risk charge is based on a 10 days 99% VaR. The daily regulatory capital requirement corresponds to the maximum of the previous day’s VaR and the average of daily VaR experienced over the preceding 60 business days ("AverageVaR60”), scaled up by a multiplier k between 3 and 4 (depending on the number of outliers in a back-testing exercise). Hence:

Market risk charge (t) = Max (VaR(t-1), k* AverageVaR60)

Volatilities and correlations should be estimated using historical data, with a minimum of 250 days.

For credit risk, Basle II introduced three alternative approaches:

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• The standardised approach which is inspired by Basle I in so far as it sets capital charges depending on the type of exposure and obligor, while it makes capital charges more rating sensitive. Some examples:

• The two IRB (internal rating based) approaches

o The foundations approach;

o The advanced IRB approach, in which the

In the IRB approach, the required minimum capital is based on the distribution of losses due to default in a portfolio of loans or similar instruments. The horizon of the risk assessment is set at one year. The IRB model further assumes a 99.9% confidence level. This means that once in a thousand years the actual loss is expected to exceed the model’s estimate. The calculation of capital requirements for a loan’s default risk under Basel II relies on he following components:

– Probability of default (PD): estimate of the likelihood of the borrower defaulting on its obligations within a given horizon, e.g. one year.

– Loss given default (LGD): loss on the loan following default on the part of the borrower, commonly expressed as a percentage of the debt’s original nominal value.

– Exposure at default (EAD): nominal value of the borrower’s debt.

– Maturity of the loan.

- Correlation to systematic risk: estimate of the link between the joint default of two separate borrowers. The IRB model relies on a single-factor asset value model to describe the co-movement of defaults in a portfolio.

The main difference between the foundation and advanced IRB approaches lies in the definition of the input variables. Both approaches rely on banks’ PD estimates, but banks’ internal estimates of LGD and loan maturity are only taken account of in the advanced IRB approach. (for the theoretical foundations of the IRB approach, see M. Gordy, “A risk-factor model foundation for ratings-based bank capital rules”, Journal of Financial Intermediation, Volume 12, 2003, pp. 199-232).

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Generally, it is expected that sophisticated banks choose the IRB framework, as it allows potentially to save some capital. The IRB is supposed to be more “intelligent”, i.e. more risk sensitive, and less ad-hoc.

Eventually, banks need to meet Capital adequacy ratios, which is the amount of “capital” available to cover “risk”. “Risk” is measured in terms of risk-weighted assets as described for instance above under Basle II. Two types of capital are relevant: tier one and tier two capital. The first is what the bank can absorb without the bank being required to cease trading, and the second which can absorb losses in the event of a winding-up and so provides a lesser degree of protection to depositors. The Basle Accord requires for the capital adequacy ration CAR:

CAR = ( tier 1 capital + tier 2 capital ) / (∑ market, credit and operational risk) > 8%

The constituents of capital are precisely described in paragraph 49 of the new Basle Accord document (Basle Committee, 2006).

E. Fair value (mark-to-market) accounting A danger associated with capital adequacy requirements, in connection with mark-to-market accounting rules, is pro-cyclicality.

Mark-to market accounting or “fair value” accounting means that each financial instrument would be shown at fair value - ‘an estimate of the price an entity would have realised if it had sold an asset or paid if it had been relieved of a liability on the reporting date in an arm’s-length exchange motivated by normal business considerations. That is, it is an estimate of an exit price. While being applied today for the “trading book”, it is not for the “banking book” (the loan portfolio) nor for liability side debt.

Following Jackson and Lodge (2000), historical cost accounting developed not as a means of portraying economic value but as a system for monitoring property and resources entering and exiting firms in order to prevent misuse and theft. For such purposes the accuracy and verifiability of underlying data were paramount concerns, and the original purchase prices sufficed. For banks, too, with business activity that could be represented by simple on-balance sheet receivables and payables, the approximation provided by historical costs seemed acceptable. By the late 1980s, however, there was widespread recognition among bank regulators and accounting standard setters that traditional accounting approaches were obscuring the real value of securities and derivatives and swaps.

In the late 1990s, standard setters moved towards requiring more financial instruments to be included in the accounts at fair value. Enria et al (2004, 30) set the date of introduction of mark-to-market valuation of the trading book of European banks to around 1990 (the range of being between 1987 and 1993).

The International Accounting Standard (IAS) 39 (effective from January 2001) establishes comprehensive fair value accounting requirements for all derivatives and those debt and equity securities held for trading or available for sale.

The advantages of historical cost accounting, at least for items without a readily available market price (such as loans and funding), are that the figures should be unambiguous and that the method is reasonably easy to apply. Many banks also feel

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that, combined with appropriate provisioning policies, historical cost accounting fits with their hold-to maturity approach to these items. The disadvantage is that economic reality is not represented in the sense of a representation of value: change of interest rates and of credit quality of obligors are not reflected.

Enria et al (2004) distinguish between five main potential draw-backs of fair-value accounting, whereby these may be grouped as follows into three: (1) The first focuses on the likely increase in the volatility of income. It can be argued that volatility provides relevant information and should be duly recognised in the financial statements. However, an excessive reliance on fair values, including for assets that are not actively traded on liquid secondary markets, runs the risk that the information disclosed will embody “artificial” volatility, driven by short-term fluctuations in financial market valuations, or caused by market imperfections or by inadequate development of valuation techniques. The cost could be a potential increase in the intrinsic pro-cyclicality of bank lending, as more accentuated increases in bank profits and capital during upturns would support the overextension of credit, that would then create the conditions for a deeper and more long-lasting downturn. This could be exacerbated by the effect that downward adjustments in asset valuations would have on bank profits and capital, which would further restrain their lending (in the context of capital adequacy). Increased pro-cyclicality would emerge in particular if economic agents have an overly optimistic assessment of risks during upturns, reflected in a short-term bias in the calculation of expected cash flows. The upward revaluations of assets in an upswing would be reflected not only in pro-cyclicality of lending, but also bank management could face pressure from shareholders to distribute dividends, including unrealised gains on assets remaining in the bank portfolio. Banks’ ability to smooth inter-temporal shocks would therefore be adversely affected, with a resulting cost in terms of both the efficiency and the stability of the financial intermediation function. (2) The second drawback relates to the role of banks in maturity and liquidity transformation. The joint provision of deposits and loans puts banks in a position to provide liquidity on demand and support the needs of other components of the financial sector and of the economy as a whole, also in times of distress. This role is fundamentally linked to the opaque nature of the value of bank assets resulting from the non-marketability of loan contracts. It is argued that the attempt to introduce fair values for loans fails to recognise a permanent and positive feature of banking, i.e. its contribution to the overcoming of informational asymmetries between lenders and borrowers.

(3) The third drawback is the potential disruption to market discipline caused by the reduction of comparability and reliability of financial statements across financial institutions. Fair values should be based on inputs from liquid markets in order to reduce the scope for possible manipulation. At present a variety of valuation models coexist with varied inputs and assumptions, and this may significantly reduce comparability if used indiscriminately across banks and across balance-sheet items. Indeed, what is the reliability and objectivity of fair values estimated using market credit spreads and internal models? There are limitations on the use of credit market

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information as there is a large dispersion in observed credit spreads for rated debt within each risk grade and for a given maturity for lower-rated debt categories. Even between bank loans and bond obligations with the same obligor, differences in observed credit spreads are large and varied. (This argument basically says that “FFVA intends to make things more transparent, but the opposite is the case”).

G. Initiatives to reduce pro-cyclicality The relevance of the remarks in the previous section on the potential pro-cyclicality of fair-value accounting made in 2004 and 2000 was fully confirmed during the 2007-09 financial turmoil, in which it became clear that pro-cyclicality relating to accounting, capital requirements, and banks’ practices (use of VaR, margin requirements, etc.) had created inherent instability, a “Demon of our own design” to use the title of a book of Richard Bookstaber published in summer 2007.

This was also recognised in the financial crisis related international initiatives as culminating in the G20 meeting in London on 2 April 2009. One of the three sets of recommendations of the Financial Stability Forum issued at the occasion of the G20 meeting (See Press release “FSF issues recommendation and principles to strengthen financial systems”, http://www.fsforum.org/press/pr_090402a.pdf) deals with pro-cyclicality relating to accounting and capital adequacy requirements (full report on the Issue: Financial Stability Forum, 2 April 2009, “Report of the Financial Stability Forum on Addressing Procyclicality in the Financial System”, http://www.fsforum.org/publications/r_0904a.pdf).

Excerpts from Annex 1 of the Press release (bold has been added): “1. Capital 1.1. The BCBS (= Basel Committee on Banking Supervision) should develop mechanisms by which the quality of the capital base and the buffers above the regulatory minimum are built up during periods of strong earnings growth so that they are available to absorb greater losses in stressful environments. As part of this process, the BCBS will assess the appropriate balance between discretionary and non-discretionary measures. It will also develop standards for what constitutes a sound bank capital planning framework. 1.2. The BCBS should carry out a more fundamental review of the market risk framework, including the use of Value-at-Risk (VaR) estimates as the basis for the minimum capital requirement. A key objective should be to find ways to reduce the reliance on cyclical VaR-based capital estimates, for example by expanding the role of stress testing within the framework. 1.3. The BCBS should supplement the risk-based capital requirement with a simple, non-risk based measure to help contain the build-up of leverage in the banking system and put a floor under the Basel II framework. This measure should complement the risk-based approach of Basel II and should be transparent and simple to implement; limit the build-up of leverage in the banking system during booms; put a floor under the risk-based measure that becomes binding if firms take on excessive leverage or attempt to arbitrage the risk-based regime; and not produce adverse incentives. 1.5. The BCBS is tracking the impact of the Basel II framework on the level and cyclicality of capital requirements through regular data collection. In parallel, the BCBS should review mechanisms through which known channels of cyclicality in the minimum Pillar 1 capital

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requirement, such as migrations in credit scores, could be addressed. The BCBS is working to develop concrete proposals to mitigate any excessive impact of ratings migrations on regulatory capital requirements. 1.6. Reflecting the significant capital shortfalls that emerged at a number of banks during the crisis, the risk coverage of the capital framework needs to be improved, and three main areas have been identified: capital requirements for resecuritisations; the standardised capital requirement for short term liquidity lines to asset-backed commercial paper conduits and risk weights for general market disruption lines; and stressed VaR add-on and incremental risk charges to capture default and migration risk for unsecuritised credit products. More broadly, the BCBS should strengthen the Basel II framework in (i) the treatment of counterparty credit risk under the three pillars of Basel II; and (ii) the role of external ratings. 2. Provisioning … 3. Valuation and leverage 3.1. A leverage ratio should be used as a vulnerability indicator and an instrument for supervisory and macro-prudential policy. At the sectoral level, leverage ratios should be computed by national authorities for the main types of financial institutions to the extent they are of systemic importance. Authorities should review enforcing minimum initial margins and haircuts for OTC derivatives and securities financing transactions. Enforcing minimum initial margins for over-the-counter (OTC) derivatives and minimum haircuts or margins for securities financing transactions will reduce leverage in position taking, while requiring margins or haircuts to be relatively stable over the cycle will reduce the tendency for margining and collateral practices to create adverse feedback effects at times of market stress. 3.2. The BCBS and the CGFS should develop a research effort to address funding and liquidity risk, starting in 2009. A key component of this research agenda will be to define robust measures of funding and liquidity risk, which could assist assessments and pricing of liquidity risk by the private sector. 3.4. Standard setters and supervisors should explore whether firms should be required to hold valuation reserves or to otherwise adjust valuations to avoid overstatement of income when significant uncertainty about valuation exists. For financial instruments that are not actively traded, insufficient market depth or reliance on valuation models using unobservable inputs that are difficult to verify may create considerable valuation uncertainty. One solution could be to partially de-link the valuation process (in mark-to-market) from certain aspects of income and profit recognition when significant uncertainty exists. The size of the reserve or adjustment could be based on the degree of uncertainty created by the weakness in the data or underlying modelling approach. 3.5. Accounting standard setters and prudential supervisors should examine possible changes to relevant standards to dampen adverse dynamics potentially associated with fair value accounting. Possible ways to reduce this potential impact include the following: • Enhancing the accounting model so that the use of fair value accounting is carefully

examined for financial instruments of credit intermediaries. • Transfers between financial asset categories. • Simplifying hedge accounting requirements. These efforts from the accounting standard setters should be undertaken in cooperation with prudential supervisors, including the BCBS. The BCBS should consider the implications of standards setters’ efforts on capital measures.”

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As the following organisations are mentioned in this, consider the following Glossary: FSF (FSB) = The FSF brings together national authorities responsible for financial stability in significant international financial centres, international financial institutions, sector-specific international groupings of regulators and supervisors, and committees of central bank experts. It was established by the G7 Finance Ministers and Central Bank Governors in 1999 to promote international financial stability through enhanced information exchange and international cooperation in financial market supervision and surveillance. The FSF decided in March 2009 to expand its membership to all G20 countries as well as Spain and the European Commission. On 2 April 2009, it was also renamed to “Financial Stability Board”. BCBS = Basel Committee on Banking Supervision. The Basel Committee on Banking Supervision provides a forum for regular cooperation on banking supervisory matters. Its objective is to enhance understanding of key supervisory issues and improve the quality of banking supervision worldwide. (G20; central banks and supervisory authorities) CGFS = The Committee on the Global Financial System is a central bank forum for the monitoring and examination of broad issues relating to financial markets and systems with a view to elaborating appropriate policy recommendations to support the central banks in the fulfilment of their responsibilities for monetary and financial stability.

F. Wrap up questions After this lecture, you should be able to answer to the following questions:

1. What typically triggers a default event?

2. What is the idea of the KMV / Merton Model of bank default?

3. What does the distribution of recovery ratios (page 2) tell us about the default point?

4. Why is bank default an important event per se for financial stability?

5. In how far could central bank policies be relevant for bank defaults?

6. Define: probability of default, recovery ratio, loss-given default, exposure at default, credit migration, mark-to-market losses.

7. What are the pros and cons of a “through the cycle” rating concept, as applied by all rating agencies?

8. Why do banks default less often than industrial corporates?

9. Should one consider the Gini-coefficient the sole useful indicator of the quality of ratings?

10. How are regulatory capital charges for market and credit risk calculated?

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11. What are the advantages and potential weaknesses of mark-to-market (fair value) accounting?

12. Why could current accounting and capital adequacy rules promote pro-cyclicality?

13. What initiatives are taken currently to reduce pro-cyclicality?

Literature

Basle Committee on Banking Supervision (2006), International Convergence of capital measurement and capital standards, A revised framework. Comprehensive version. June 2006.

Crosbie, Peter J. and Jeffrey R. Bohn, 2001, Modelling Default Risk (KMV LLC).

Enria et al (2004), Fair value accounting and financial stability, ECB Occasional Paper Nr. 13, April 2004.

Jackson, P. and D. Lodge (2000), Fair value accounting, capital standards, expected loss provisioning, and financial stability, in: Financial Stability Review, Bank of England, London, June, S. 105 – 125

Merton, Robert C. (1974), "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates", Journal of Finance, Vol. 29, No. 2, pp. 449-470

Standard & Poors (2009), “2008 Annual Global Corporate Default Study and Rating Transitions”, New York, February 25, 2009.