Modern Risk Modern Risk Modern Risk Management Modern Risk - - PowerPoint PPT Presentation

Modern Risk Modern Risk Management anagement Concepts:

• ncepts:

Modern Risk Modern Risk Management anagement Concepts:

• ncepts:

a Holistic View, with Implications a Holistic View, with Implications a Holistic View, with Implications a Holistic View, with Implications for a Bond Portfolio for a Bond Portfolio

Sergey Smirnov EFFAS‐EBC Vice Chairman

Director of Financial Engineering and Risk Management Laboratory Head of Risk Management and Insurance Department g p National Research University "Higher School of Economics", Moscow

EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

The aim of the presentation The aim of the presentation

The aim of the presentation is to discuss:

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• Risk management at portfolio level: bonds and

related derivative financial instruments

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related derivative financial instruments

• Recent changes in regulation due to the crisis,

their implications (important in particular for

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their implications (important, in particular, for bond portfolios) and challenges for their implementation in the world and in Europe

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implementation in the world and in Europe

• State of the art risk assessment for interest rate

i k dit i k d li idit i k i risk, credit risk and liquidity risk: an overview, achievements and problems

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学而不思 罔, 思而不学 殆

孔子 ‐‐‐孔子 He who learns but does not think, is lost! is lost! He who thinks but does not learn is in great danger.

Confucius

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TOPICS OF THE PRESENTATION TOPICS OF THE PRESENTATION

• 1. General remarks on risk management issues

2 Risks associated with investing in bonds

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• 2. Risks associated with investing in bonds
• 3. Recent changes in regulation environment

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• 4. Global financial stability and systemic risks
• 5. Traditional credit analysis: expert judgement

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• 6. Credit risk models
• 7. Credit ratings agencies: external credit quality

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• 7. Credit ratings agencies: external credit quality

assessment 8 Internal credit ratings

• 8. Internal credit ratings
• 9. Bond spreads and Risk Free Zero‐Coupon Yield Curve

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1

GENERAL REMARKS ON RISK MANAGEMENT ISSUES ON RISK MANAGEMENT ISSUES

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Hierarchy of risk management y g implementation

From the most primitive to most advanced:

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• 1. Regulatory reporting & compliance

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g y p g p 2. Risk limits set as a device for authorizing specific forms and the levels of risk taking

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forms and the levels of risk taking. 3. Business decision support, at least to make risk t ki d lib t

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taking deliberate

• 4. Strategic tool to enhance firms performance, based
• n Risk Adjusted Performance Measures (RAPM),

including capital allocation for business lines.

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Contemporary paradigm p y p g

• f financial risk management
• Risk management is not the process of controlling and reducing

expected losses (which is essentially a budgeting, pricing, and business efficiency concern) but the process of understanding

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business efficiency concern), but the process of understanding, costing, and efficiently managing unexpected levels of variability in the financial outcomes for a business. E i b i k i ifi f i k

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• Even a conservative business can take on significant amount of risk

quite rationally, in light of

– Its confidence in the way it assesses and measures the unexpected

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y p loss levels associated with its various activities – The accumulation of sufficient capital or the deployment of other risk management techniques to protect against potential unexpected loss

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management techniques to protect against potential unexpected loss levels – Appropriate returns from the risky activities, once the cost of risk capital and risk management is taken into account capital and risk management is taken into account – The needs of funding for business continuity, sufficient liquidity in normal and stress periods

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Risk management background Risk management background

• 1. It is important to make risk management primarily a

positive contributor to better business decisions and

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p to avoid the trap of focusing only on the loss side of the distribution and loss avoidance.

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• 2. Sound risk management background is therefore, first
• f all, common (engineering) sense and

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• f all, common (engineering) sense and

understanding of business. 3 Mathematical models in risk management can

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• 3. Mathematical models in risk management can

provide useful tool, if first two points are taken into account; in this case it serve to quantify risks in account; in this case it serve to quantify risks in consistent manner with reasonable accuracy.

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Emanuel Derman: Th ’ E h M th i Fi Al d There’s Enough Math in Finance Already. What’s Missing is Imagination g g

“In some sense all of finance is about imagination

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In some sense, all of finance is about imagination because finance is about saying, what should something be worth today based on what I think is

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something be worth today based on what I think is going to happen in the future? Nobody knows what’s going to happen in the future.

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going to happen in the future. And so all financial models are specifying in some way

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And so all financial models are specifying in some way an imagined future and then saying, if that future is true what should I pay for something today? true, what should I pay for something today?

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Emanuel Derman continuation Emanuel Derman: continuation…

And so for example, if you’re building an option model, you’re saying, what will volatility be in the future? And given my estimate of volatility in the future I can value an option today

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estimate of volatility in the future, I can value an option today. If you’re looking at CDO’s, which sort of came in a cropper in

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y g pp the big financial crisis, essentially human beings are saying, what will housing prices do in the future?

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What will defaults on loans be and defaults on mortgages be?

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And given my imagined scenario for the future, what should I pay for something today that’s sensitive to that future pay for something today that s sensitive to that future behavior?

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Emanuel Derman end of citation Emanuel Derman: end of citation

The big failures, I think, are failures of imagination not of mathematics The big

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imagination, not of mathematics. The big mistakes are when you don’t’ think of something

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that does come to fruition eventually.

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… And I think that’s sort of what goes wrong with a

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lot of financial models. You can’t really write down one short description of all the things that down one short description of all the things that markets may do in the future”.

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Useful quote ‐ about model q assumptions and data quality

• Mathematics may be compared to a mill of

exquisite workmanship which grinds your stuff to

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exquisite workmanship, which grinds your stuff to any degree of fineness; but, nevertheless, what

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you get out depends on what you put in; and as the grandest mill in the world will not extract

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g wheat flour from peas cods, so pages of formulae will not get a definite result out of loose data ”

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will not get a definite result out of loose data. Huxley, Thomas H.

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2

RISKS ASSOCIATED WITH INVESTING IN BONDS INVESTING IN BONDS

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Main risks for a bond portfolio Main risks for a bond portfolio

• Interest rate risk

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• Credit risk (including sovereign risk)
• Liquidity (market and funding) risk

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• Liquidity (market and funding) risk

Possibly (if there is any exposure to):

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y f y p

• Foreign Exchange Rate Risk

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Interest rate risk Interest rate risk

• Since the price of a bond fluctuates with market

interest rates, the risk that an investor faces is that the f b d h ld f l ll d l f k

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price of a bond held in a portfolio will decline if market interest rates rise. Thi i k hi h i f f k i k i f d

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• This risk, which is a form of market risk, is referred to

as interest rate risk and is the major risk faced by investors in the bond market

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investors in the bond market.

• Bond’s price sensitivity to changes in market interest

rates (i e a bond’s interest rate risk) depends on

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rates (i.e., a bond s interest rate risk) depends on

– various features of the issue, such as maturity, coupon rate, and embedded options , p – Current term structure of interest rates.

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Term structure of interest rates Term structure of interest rates

Term structure of interest rates can be described in several ways: 1 yield curve the relationship between (zero coupon) yield and

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1. yield curve ‐ the relationship between (zero coupon) yield and maturity. 2. Forward curve ‐ (instantaneous) forward rate as a function of

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( ) maturity 3. Discount rate as a function of maturity

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• The important point here is that portfolios have different exposures

to how the yield curve shifts.

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y

• The corresponding risk exposure is sometimes called yield curve
• risk. The implication is that any measure of interest rate risk that

assumes that the interest rates changes by an equal number of assumes that the interest rates changes by an equal number of basis points for all maturities (referred to as a ‘‘parallel yield curve shift’’) is only an approximation.

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Reinvestment risk Reinvestment risk

• Reinvestment risk is the risk that the proceeds received from the

payment of interest and principal (i.e., scheduled payments, called proceeds and principal prepayments) that are available for

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proceeds, and principal prepayments) that are available for reinvestment must be reinvested at a lower interest rate than the security that generated the proceeds. F i i i i (i i i h i i l

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• For amortizing securities (i.e., securities that repay principal

periodically),reinvestment risk is greater than for plain vanilla fixed

• income. Reinvestment risk, in particular, is present when an

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investor purchases a callable or principal prepayable bond. Zero‐ coupon bonds eliminate reinvestment risk up to maturity time.

• Reinvestment risk is related to the volatility of the forward rates

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Reinvestment risk is related to the volatility of the forward rates with corresponding maturities and can therefore be regarded as a sot of interest rate risk.

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The Impact of Embedded Options The Impact of Embedded Options

• a bond may include a provision that allows the issuer to retire, or

call, all or part of the issue before the maturity date. From the investor’s perspective there are disadvantages to call provisions:

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investor s perspective, there are disadvantages to call provisions:

1. The cash flow pattern of a callable bond is not known with certainty because it is not known when the bond will be called. 2 B h i i lik l ll h b d h i h

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2. Because the issuer is likely to call the bonds when interest rates have declined below the bond’s coupon rate, the investor is exposed to reinvestment risk, i.e., the investor will have to reinvest the proceeds h th b d i ll d t i t t t l th th b d’

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when the bond is called at interest rates lower than the bond’s coupon rate or yield to maturity.

• Because of these disadvantages faced by the investor, a callable

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bond is said to expose the investor to call risk. The same disadvantages apply to mortgage‐backed and asset‐backed securities where the borrower can prepay principal. p p y p p

• The embedded option value is sensitive to the volatility of the

security, so that finally it is related to the specific interest rate risk

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Liquidity risk Liquidity risk

Liquidity risk can materialize in two basic forms:

• Market liquidity risk which is the risk that a firm

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Market liquidity risk, which is the risk that a firm will not be able to sell an asset quickly without materially affecting its price;

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materially affecting its price;

• Funding liquidity risk, which is the risk that a firm

will not be able to meet expected cash flow

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will not be able to meet expected cash flow requirements (future and current) by raising funds on short notice

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funds on short notice. The two types of liquidity risks can interact with each other and through markets affect multiple each other and, through markets, affect multiple institutions.

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Systemic liquidity shortfalls Systemic liquidity shortfalls

• In periods of rising uncertainty, the interaction can give rise to

systemic liquidity shortfalls. A negative spiral between market and funding liquidity can develop whereby a sudden lack of funding

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funding liquidity can develop whereby a sudden lack of funding leads to multiple institutions attempting to sell their assets simultaneously to generate cash. Th l d fi l f l d li f li idi

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• These correlated fire sales of assets may lead suppliers of liquidity

to insist on higher margin and larger haircuts (the deduction in the asset’s value used as collateral) as the value of collateral(assets

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pledged) declines. Creditors may become even less likely to provide funding, fearing insolvency of their counterparties, resulting in significant funding disruptions.

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g g p

• This self‐reinforcing process can lead to downward cascades in

asset prices and to further declines in a firm’s net worth, morphing into a systemic crisis as many institutions become affected into a systemic crisis as many institutions become affected.

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Liq idit risk management frame ork Liquidity risk management framework

• A bank should establish a robust liquidity risk

management framework that ensures it maintains

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g sufficient liquidity, including a cushion of unencumbered, high quality liquid assets, to

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g q y q withstand a range of stress events, including those involving the loss or impairment of both unsecured and

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secured funding sources.

• A bank should define a liquidity risk tolerance that

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q y should ensure that the firm manages its liquidity strongly in normal times in such a way that it is able to g y y withstand a prolonged period of stress.

21 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Credit risk (counterparty risk) ( p y ) definitions

Narrow sense definition

• Credit risk is a possibility of investor's loss arising from a borrower who

does not make payments as promised Such an event is called a default

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does not make payments as promised. Such an event is called a default. This risk should be preferably called default risk . Wide sense definition C dit i k i th i k th t h i dit lit f t t ill

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• Credit risk is the risk that changing credit quality of a counterparty will

affect the value of a bank’s positions .The credit quality of a counterparty refers generally to the counterparty’s ability to perform on that obligation. This encompasses both the counterparty’s default probability and

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This encompasses both the counterparty’s default probability and anticipated recovery rate. Default occurs when a counterparty is unwilling

• r unable to fulfill its contractual obligations. This is the extreme case.

C dit i k i l if th iti i t i h it hibit

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• Credit risk arises only if the position is an asset, i.e. when it exhibits a

positive replacement value. If a counterparty defaults, the loss can be the position’s total market value or, a percentage of that value (called loss given default) The percentage of total market value that be recovered is given default). The percentage of total market value that be recovered is called recovery rate.

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Market vs. credit risk direct measurement

• Market risk exposure arises from unexpected security

price fluctuations. Using long histories of daily price fl d h b “ l” d

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fluctuations we can distinguish between “typical” and “atypical” trading days in order to assess either expected losses (on a typical day) or unexpected losses

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expected losses (on a typical day) or unexpected losses (on an atypical day that occurs with a given likelihood).

• Credit events such as default or rating downgrades are

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• Credit events such as default or rating downgrades are

rare, often nonrecurring events. Thus, we do not have enough statistical power to estimate directly daily

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g p y y measures of credit risk exposure. It is however possible to measure it in indirect way using market data, and i hi f k f di i k d l within framework of credit risk models.

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External ratings External ratings

“A bank should be aware that external ratings are a useful starting point for credit analysis but are no

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useful starting point for credit analysis, but are no substitute for full and proper understanding of the d l i i k i ll h ti f t i

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underlying risk, especially where ratings for certain asset classes have a short history or have been

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shown to be volatile”

• Enhancements to the Basel II framework 2009

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Enhancements to the Basel II framework 2009

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Credit risk concentrations Credit risk concentrations

The typical situations in which risk concentrations can arise include:

• exposures to a single counterparty, borrower or group of connected

counterparties or borrowers;

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counterparties or borrowers;

• industry or economic sectors, including exposures to both regulated and

nonregulated financial institutions such as hedge funds and private equity firms;

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firms;

• geographical regions;
• exposures arising from credit risk mitigation techniques, including

l ll l l l l l

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exposure to similar collateral types or to a single or closely related credit protection provider;

• trading exposures/market risk;

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g p

• exposures to counterparties (eg hedge funds and hedge counterparties)

through the execution or processing of transactions (either product or service); );

• ff‐balance sheet exposures, including guarantees, liquidity lines and
• ther commitments.

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3

RECENT CHANGES IN REGULATION ENVIRONMENT ENVIRONMENT

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Wh it i i t t t t d B l 2 2 5 d 3 Why it is important to study Basel 2, 2.5 and 3

• Recommendations Set by the Basel Committee on

Banking Supervision, are based on the best practice of

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g p , p risk management in the form of generalised rules, taking into account consultations with leading banks.

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g g

• It creates an international standard which for

regulators to formulate capital adequacy and liquidity

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regulators to formulate capital adequacy and liquidity requirements for financial institutions in order to avoid falling in a vortex of financial risk.

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• A Basel 2, 2.5 and 3 in its advanced form ‐ relying on

internal models ‐ is therefore a benchmark for internal models is therefore a benchmark for implementing the best practices in risk management.

27 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Revisions to the Basel II Market Risk 2009 (Basle 2.5)

• The Proposed Rule is a modification of the existing risk‐based

capital treatment of market risk, of the Market Risk Amendment of 1996 (MRA)

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1996 (MRA).

• Changes to the trading book rules are warranted because of

changes in the markets and the large trading book losses banks ff d i 2007 d 2008 F l h i i l d

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suffered in 2007 and 2008. For example, the existing rule does not adequately capture the credit risk of positions held in banks' trading books. Among other changes, the Proposed Rule ensures

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that capital is held against these positions by applying credit risk capital charges to trading positions.

• the Proposed Rule introduces several new capital requirements

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the Proposed Rule introduces several new capital requirements, including stressed‐value‐at‐risk (SVaR), the incremental risk charge, and charges for correlation trading positions.

28 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Incremental Risk Charge Incremental Risk Charge

• Banks have included certain types of positions in the market risk

capital framework that contain significant levels of credit risk. This was not envisioned when the MRA was first implemented To

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was not envisioned when the MRA was first implemented. To address this situation, the a new capital requirement is established, the incremental risk charge. Incremental risk is the default and credit migration risk that is not reflected in a bank's VaR based

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credit migration risk that is not reflected in a bank s VaR‐based measures.

• “After the crisis manifestation of 2007 and 2008 Basel Committee

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decided in 2009 to expand the scope of the capital charge. The decision was taken in light of the recent credit market turmoil where a number of major banking organisations have experienced

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j g g p large losses, most of which were sustained in banks’ trading books. Most of those losses were not captured in the 99%/10‐day VaR.” BIS Consultative Paper “Guidelines for Computing Capital for BIS Consultative Paper Guidelines for Computing Capital for Incremental Risk in the Trading Book” July 2008

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Incremental risk in the trading book Incremental risk in the trading book

• Since the losses have not arisen from actual defaults but rather

from credit migrations combined with widening of credit spreads

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from credit migrations combined with widening of credit spreads and the loss of liquidity, applying an incremental risk charge covering default risk only would not appear adequate. For l b f l b l fi i l i i i d h

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example, a number of global financial institutions commented that singling out just default risk was inconsistent with their internal practices and could be potentially burdensome

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• The incremental risk capital requirement must be consistent with a
• ne‐year horizon and a 99.9 percent confidence level, the

measurement standard under the credit risk capital framework

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measurement standard under the credit risk capital framework. This capital requirement would include losses arising from defaults and credit migrations in covered positions subject to specific interest rate risk interest rate risk.

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Trading and covered positions Trading and covered positions

• A trading position is defined as a position that is held by

the bank for the purpose of short‐term resale or with the i t t f b fiti f t l t d h t t

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intent of benefiting from actual or expected short‐term price movements, or to lock in arbitrage profits.

• Covered position definition includes trading assets and

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• Covered position definition includes trading assets and

trading liabilities that hedge covered positions. In addition, the trading asset or trading liability must be free of any

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restrictive covenants on its tradability or the bank must be able to hedge its material risk elements in a two‐way market A trading asset or trading liability that hedges a

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• market. A trading asset or trading liability that hedges a

trading position is a covered position only if the hedge is within the scope of the bank's hedging strategy.

31 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Liquidity risk management q y g importance

• Liquidity reserve vs. capital reserve: when Bear Stearns defaulted in 2008,

its capital reserve where above the minimal regulatory capital required by Basel II, but was not available (liquid) for meeting margin calls.

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, ( q ) g g

• Liquidity problems are typically low frequency but potentially high impact

events, and the board of directors and senior management of a bank may pay more attention to other, higher frequency risks or may limit a bank’s

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pay more attention to other, higher frequency risks or may limit a bank s liquidity risk mitigation due to competitive considerations.

• In addition, an expectation that central banks will provide liquidity

support alongside the guarantees to depositors provided by deposit

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support, alongside the guarantees to depositors provided by deposit insurance, could diminish the incentives of the bank to manage its liquidity as conservatively as it should.

• This is typical moral hazard situation (in which a party insulated

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• This is typical moral hazard situation (in which a party insulated

from risk behaves differently from how it would behave if it were fully exposed to the risk) increases the responsibility of supervisors to ensure that a bank does not lower its standard of liquidity risk management and that a bank does not lower its standard of liquidity risk management and adopt a less robust liquidity risk management framework as a result.

32 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Liquidity issues for the interaction q y between market and credit risk

an excerpt from “Findings on the interaction

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an excerpt from Findings on the interaction

• f market and credit risk”, Basel Committee on

Banking Supervision Working Paper No 16 2009

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Banking Supervision, Working Paper No. 16, 2009 “Li idit diti i t t ith k t i k d dit

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• “Liquidity conditions interact with market risk and credit

risk through the horizon over which assets can be liquidated

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liquidated.

• In particular, deteriorating market liquidity often forces

banks to lengthen the horizon over which they can banks to lengthen the horizon over which they can execute their risk management strategies.

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Liquidity issues continuation Liquidity issues: continuation…

• As this time horizon lengthens, overall risk exposures

generally increase, as does the contribution of credit k l k k h l d f d d

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risk relative to market risk. The liquidity of traded products can vary substantially over time and in unpredictable ways Such liquidity fluctuations all else

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unpredictable ways. Such liquidity fluctuations, all else equal, should have a larger impact on prices of products with greater credit risk.

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products with greater credit risk.

• Liquidity risks are not accounted for in pricing models

used in trading on the financial markets. Since all

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g models are not geared towards this scenario, all participants in an illiquid market using such models will f i i k ” face systemic risks”.

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Taking into account European g p specificities

• In July 2011 European Commission issued a proposal containing

two parts: a directive governing the access to deposit‐taking activities and a regulation governing how activities of credit

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activities and a regulation governing how activities of credit institutions and investment firms are carried out

• The proposal EU regulation is faithful to Basel's spirit, letter and

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p p g p level of ambition. But it is not an easy exercise to apply rules to 8.200 banks amounting for 53% of global assets.

• It is necessary to take into account the specificities of the

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• It is necessary to take into account the specificities of the

European banking sector, with its mutual or cooperative banks (13% of the European banking sector) and its bank and i

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insurance groups.

• Gradual development is needed: not to destabilise the financing
• f the European economy The new prudential rules will
• f the European economy. The new prudential rules will

represent a considerable effort since our banks will have to find some 460 billion euros of additional capital.

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Positi e side of implementation in EU Positive side of implementation in EU

• the effort will take place over period of time from 2013

to 2019

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• it will be accompanied and compensated by an

increased level of trust (lacking today)

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• there is an estimate of the implementation impact by

modelling: the probability of severe systemic crises h ld d b 70%

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should decrease by 70%.

• moreover, if a crisis nevertheless will occur, it will be

less serio s

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less serious.

• the Basel committee predicts that the EU's GDP will

increase by 0 3 to 2 percentage points per year once increase by 0.3 to 2 percentage points per year, once all measures are fully implemented.

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Role of credit rating agencies: g g European point of view

• A hot topic, which directly concerns European banks and which is tackled

in the proposal: the role of credit rating agencies. Excerpt from Michel Barnier member of the European Commission

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Excerpt from Michel Barnier, member of the European Commission responsible for Internal Market and Services, speech: “W t d d t dit ti i A lt I i h t

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“We are too dependent on credit rating agencies. As a result, I wish to suppress as much as possible the reference to credit ratings in the prudential rules. This is essential for financial stability. h h f h b k l

EFFAS‐EBC an Confe eneva, 2nd N

Today, we propose to strengthen the requirement for the banks to lead their own risk analyses, without relying mechanically on credit rating agencies”.

E G

• These proposals will be followed by a new legislative initiative aiming at

improving the framework for credit rating agencies, in particular regarding p g g g , p g g their activity on sovereign debt.

37 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Proposal content Proposal content

The Regulation contains the detailed prudential requirements for credit institutions and investment firms and it covers:

d 11

1. capital: The Commission’s proposal increases the amount of own funds banks need to hold as well as the quality of those funds. It

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q y also harmonises the deductions from own funds in order to determine the amount of regulatory capital that is prudent to recognise for regulatory purposes.

EFFAS‐EBC an Confe eneva, 2nd N

recognise for regulatory purposes. 2. liquidity: To improve short‐term resilience of the liquidity risk profile of financial institutions, the Commission proposes the introduction of a Liquidity Coverage Ratio (LCR) the exact

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introduction of a Liquidity Coverage Ratio (LCR) ‐ the exact composition and calibration of which will be determined after an

• bservation and review period in 2015.

38 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Proposal content continuation Proposal content, continuation

3. leverage ratio: In order to limit an excessive build‐up of leverage

• n credit institutions' and investment firms' balance sheets, the

Commission also proposes that a leverage ratio be subject to

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Commission also proposes that a leverage ratio be subject to supervisory review. Implications of a leverage ratio will be closely monitored prior to its possible move to a binding requirement on 1 January 2018

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1 January 2018. 4. counter party credit risk: consistent with the Commission's policy vis‐à‐vis OTC (over the counter) derivatives (IP/10/1125), changes

EFFAS‐EBC an Confe eneva, 2nd N

are made to encourage banks to clear OTC derivatives on CCPs (central counterparties). 5 single rule book: the financial crisis highlighted the danger of

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5. single rule book: the financial crisis highlighted the danger of divergent national rules. A single market needs a single rule book. The Regulation is directly applicable without the need for national transposition and accordingly eliminates one source of such transposition and accordingly eliminates one source of such

• divergence. The Regulation also sets a single set of capital rules.,

39 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

4

GLOBAL FINANCIAL STABILITY AND SYSTEMIC RISKS AND SYSTEMIC RISKS

40 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Problem: “Risk‐sensitive” regulation g failed to stop the crisis?

“If th b d l th t fi i l i ld b

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• “If the bad apple theory were correct, financial crises would be

random, arising whenever there was a sufficient concentration of evil‐doers But crashes are not random; they always follow booms

nd Chinabon erence

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evil doers. But crashes are not random; they always follow booms. And booms are not caused by people doing things they know are risky, but by people doing things they perceive as safe; so safe as

EFFAS‐EBC an Confe eneva, 2nd N

y y p p g g y p to justify doubling up and betting the house.

• This is the essential challenge of banking regulation, a challenge ill

E G

served by the “Basel II” approach of requiring the banks to put aside capital depending on their perception of risks. So‐called “risk‐ i i ” l i dd fi h b d i h b ” sensitive” regulation adds fire to the boom and ice to the bust”. Prof Avinash D. Persaud

41 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Global Financial Stability Report 2009: y p when risk become systemic

The GFSR 2009 feature methodologies that can shed light on when direct d i di t fi i l li k b t i S ifi ll th

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and indirect financial linkages can become systemic. Specifically, the authors present complementary approaches to assess financial sector systemic linkages, including: h k h h h k h b f d

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• The network approach, which tracks the reverberation of a credit event

and a liquidity squeeze throughout the system.


• The co‐risk model, which exploits market data to assess systemic linkages

EFFAS‐EBC an Confe eneva, 2nd N

at an institutional level and is an important method of assessing the markets’ perception of how much more tightly the fortunes of financial institutions are linked together during stress times.


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• The default intensity model, which measures the probability of failures of

a large fraction of financial institutions (default clustering) as a result of both direct and indirect systemic linkages.

42 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Russian interbank market liquidity q y (Buzdalin, Smirnov et al, 2005)

• It was a part of the work done in 2005 for Deposit Insurance
• Agency. We were using the data on the number and volume of the

inter‐bank lending of the Russian banks in the first quarter 2005

d 11

inter bank lending of the Russian banks in the first quarter 2005.

• Since only about 250 Russian banks are active on the inter‐bank

lending market, the suggested algorithm is suitable only for these b k h h il bl i i d i i ffi i f

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banks, whereas the available statistic data is insufficient for identification of clusters for other banks. However, the 250 banks active on the inter‐bank lending market account for 90% of the total

EFFAS‐EBC an Confe eneva, 2nd N

assets of the banking system.

• Based on the computations performed, 18 main clusters were

identified (see next slide) The hierarchical structures within the

E G

identified (see next slide). The hierarchical structures within the banking system are represented by arrows that indicate the banks

• f the senior clusters that redistribute liquidity to the subordinate

43 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Russian interbank market: identified clusters, 2005

Urals ib-Nikoil et al Proms vyazbank, BIN Bank, Interregional Inves tment Bank

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CORE: Sberbank, Vnes htorgbank, Vnes heconombank Foreign Banks иностранным Avangard et al. Banksof Samara Region, Soyuz

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Foreign Banks иностранным Indus trial and banking groups Gazprombank, Interprombank et al

EFFAS‐EBC an Confe eneva, 2nd N

Ga p o ba , Indus trial and Cons truction Bank, Alfa-Bank, MDM Bank, Bank of Khanty-Mans iys k Satellite Banks Satellite Banks Satellite Banks Satellite Banks

E G

Autonomous Groups Nomos

• Bank

Metallinves t Lefco-Bank et Nomos

• Bank

et al Metallinves t Bank et al Lefco-Bank et al Orgbank et al Vozrozhdeniye Bank, Bank Zenit et al

44 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Brasilian interbank network (Cont & Bastos 2009)

d 11 nd Chinabon erence

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EFFAS‐EBC an Confe eneva, 2nd N E G 45 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

The Empirically Constructed CDS Network p y for US Banks and Outside Entity (Markose et al, 2010)

d 11 nd Chinabon erence

• vember 20

EFFAS‐EBC an Confe eneva, 2nd N E G 46 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Global Financial Stability Report, y p , April 2010

The GFSR report examines systemic risk and the

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The GFSR report examines systemic risk and the redesign of financial regulation. In particular:

nd Chinabon erence

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• implementing Systemic‐Risk‐Based Capital

Surcharges

EFFAS‐EBC an Confe eneva, 2nd N

Surcharges ;

• the role of central counterparties in making over‐

h d i i f

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the‐counter derivatives safer;

• the effects of the expansion of global liquidity on

receiving economies.

47 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Global Financial Stability Report, y p , October 2010

• In this GFSR, it was noted that improvements in market

infrastructure could help mitigate systemic liquidity k

d 11

risks.

• For instance, some risks associated with collateral

i d f di k ld b

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management in secured funding markets could be addressed through greater use of central counterparties for repurchase agreements and

EFFAS‐EBC an Confe eneva, 2nd N

counterparties for repurchase agreements and through‐the‐cycle haircuts, or minimum haircut requirements, for collateral.

E G

q ,

• Also, nonbank financial institutions that contribute to

systemic liquidity risk should receive more oversight y q y g and regulation.

48 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Global Financial Stability Report, y p , April 2011

• Broadly speaking, at their core the Basel III rules are

microprudential, aimed at encouraging banks to hold higher liquidity buffers and to lower maturity mismatches to lower the

d 11

liquidity buffers and to lower maturity mismatches to lower the likelihood that any individual institution will run into liquidity

• problems. They are not intended or designed to mitigate systemic

liquidity risks where the interactions of financial institutions can

nd Chinabon erence

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liquidity risks, where the interactions of financial institutions can result in the simultaneous inability of institutions to access sufficient market liquidity and funding liquidity under stress.

EFFAS‐EBC an Confe eneva, 2nd N

• The document suggests three separate methods of measuring

systemic liquidity risk, each of which could be used to construct a macroprudential tool. Each technique measures an institution’s

E G

p q

• ngoing contribution to system‐wide liquidity risk, thereby

establishing an objective basis on which to charge an institution for the externality it imposes on the financial system. the externality it imposes on the financial system.

49 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

September 2011 GFSR : Sovereign p g Vulnerabilities and Contagion Risks

• Sovereign balance sheets remain fragile in a number of

advanced economies despite steps toward fiscal lid ti Th l k f ffi i t liti l t f

d 11

• consolidation. The lack of sufficient political support for

medium‐term fiscal adjustment and growth‐enhancing reforms worsens funding pressures for sovereigns amidst a

nd Chinabon erence

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reforms worsens funding pressures for sovereigns amidst a softer growth outlook. These pressures increase the risk that the debt dynamics of vulnerable sovereigns will slide i t i l f d t i ti i th b f h t

EFFAS‐EBC an Confe eneva, 2nd N

into a spiral of deterioration in the absence of a coherent policy framework and adequate backstops to prevent the spread of contagion.

E G

spread of contagion.

• The spillover of sovereign risks to the banking sector has

put funding strains on many banks operating in the euro area and depressed their market capitalization.

50 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Nouriel Roubini point of view Nouriel Roubini point of view about the next (wave) of crisis about the next (wave) of crisis

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• February 2, 2011, www.spiegel.de

I d 't t th t i i t b i l t d d i

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• vember 20

...I don't expect that my views are going to be implemented during this crisis. We might have to wait until the next one, until more radical proposals will be considered. My worry is that if we don't

EFFAS‐EBC an Confe eneva, 2nd N

create a system where these crises occur less frequently, then the backlash we have seen in recent times against market oriented economies, against reforms, against globalization, against free

E G

, g , g g , g trade, could become more severe the next time around. The lesson is actually if another crisis were to occur down the line, it's going to be even more virulent then the last one even more it s going to be even more virulent then the last one, even more damaging and costly for any measure you want to look at, income, jobs, wealth, fiscal costs. We just can not afford that....

51 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Joseph Stiglitz on ‘Ersatz Capitalism’ p g p and Moral Bankruptcy

• Joseph Stiglitz is a recipient of the Nobel Memorial Prize in Economic Sciences

(2001). In 2010 he calls “ersatz capitalism” the version of capitalism in the US ended up with is a flawed, unfair system that socializes economic losses and i i h i

d 11

privatizes the gains.

• “‘An awful lot of people are not managing their own money. In old‐style 19th

Century capitalism, I owned my company, I made a mistake, I bore the consequences Today (at) most of the big companies you have managers who

nd Chinabon erence

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consequences… Today, (at) most of the big companies you have managers who, when things go well, walk off with a lot of money. When things go bad the shareholders bear the costs.”

• As Stiglitz sees it the current economic paradigm is a symptom of a deeper

EFFAS‐EBC an Confe eneva, 2nd N

As Stiglitz sees it, the current economic paradigm is a symptom of a deeper, society‐wide problem.

• “We have created a society in which materialism overwhelms moral commitment,

in which the rapid growth that we have achieved is not sustainable

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p g environmentally or socially, in which we do not act together to address our common needs. Market fundamentalism has eroded any sense of community and has led to rampant exploitation of unwary and unprotected individuals. There has b i f t t d t j t i fi i l i tit ti It i t t been an erosion of trust — and not just in our financial institutions. It is not too late to close these fissures.”

52 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

5

TRADITIONAL CREDIT ANALYSIS: EXPERT JUDGEMENT EXPERT JUDGEMENT

53 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Flexibility of y expert judgement systems

• The key feature of expert‐judgement systems is flexibility. The prevalence
• f judgmental rating systems reflects the view that the determinants of

default are too complicated to be captured by a single quantitative

d 11

p p y g q

• model. The quality of management is often cited as an example of a risk

determinant that is difficult to assess through a quantitative model.

• In order to foster internal consistency, banks employing expert judgment

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In order to foster internal consistency, banks employing expert judgment rating systems typically provide narrative guidelines that set out ratings

• criteria. However, the expert must decide how narrative guidelines apply

to a given set of circumstances.

EFFAS‐EBC an Confe eneva, 2nd N

g

• The flexibility possible in the assignment of judgmental ratings has

implications for the types of ratings review that are feasible. As part of the ratings validation process, banks will attempt to confirm that raters follow

E G

ratings validation process, banks will attempt to confirm that raters follow bank policy. However, two individuals exercising judgment can use the same information to support different ratings. Thus, the review of an expert judgment rating system will require an expert who can identify the p j g g y q p y impact of policy and the impact of judgment on a rating.

54 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Expert Systems p y

• f assessing credit quality

Historically, bankers have relied on the “5 C’s of credit” principles of professional judgment to assess

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credit principles of professional judgment to assess credit quality:

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• 1. Capacity
• 2. Character

EFFAS‐EBC an Confe eneva, 2nd N

• 2. Character
• 3. Collateral

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• 4. Covenants
• 5. Conditions
• 5. Conditions

55 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Capacity Capacity

Capacity: The most critical

• How exactly do the borrower(s) intend to repay

d 11

• How exactly do the borrower(s) intend to repay

the loan? The lender considers cash flow (based th 3 fit d l j ti )

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• n the 3 year profit and loss projections) as

compared to similar or the same businesses. The b ( ) h l

EFFAS‐EBC an Confe eneva, 2nd N

borrower(s) previous payment history is a critical factor in the lenders decision to make the loan.

E G

• Do the borrower(s) have any secondary sources

to repay the loan if the business is not able to to repay the loan if the business is not able to repay the loan?

56 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Capacity analysis Capacity analysis

• Traditional financial ratios to evaluate the ability of an

issuer to meet its obligations including profitability ratios, d bt d ti

d 11

debt and coverage ratios

• Cash flow analysis is of the most important role. By analyzing

these individual statement of cash flows creditors can

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these individual statement of cash flows, creditors can examine such aspects of the business as:

– The source of financing for business operations, whether

EFFAS‐EBC an Confe eneva, 2nd N

g p through internally generated funds or external sources of funds. – The ability of the company to meet debt obligations (interest and principal payments)

E G

and principal payments). – The ability of the company to finance expansion through cash flow from its operating activities. – The ability of the company to pay dividends to shareholders. – The flexibility the business has in financing its operations.

57 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Character Character

Character: the foundation of sound credit

• The lenders impression of the borrower(s), subjective

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p ( ), j determination of trustworthiness to repay the loan as agreed.

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• Quality of management, business qualifications and

skills

EFFAS‐EBC an Confe eneva, 2nd N

• Corporate Governance, including risk management
• Integrity

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– Consistency of actions, values, methods, measures, principles, expectations, and outcomes. hi l i h h hf l f – ethical reputation, the honesty, truthfulness, accuracy of actions

58 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Collateral Collateral

Collateral: reducing credit risk exposure

• Considered not only in the traditional sense of assets pledged to

secure the debt their value in a liquidation sale if the business

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secure the debt, their value in a liquidation sale if the business cannot repay the loan, but also to the quality and value of those unpledged assets controlled by the issuer.

nd Chinabon erence

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• Collateral provides a lender with an alternative source to repay the

loan if the business is not successful. Other than cash, the lender must make a guess at the value of most collateral.

EFFAS‐EBC an Confe eneva, 2nd N

must make a guess at the value of most collateral.

• Common types of collateral include: accounts receivable, inventory,

machinery and equipment, real estate, marketable securities (traded stocks/bonds) cash and guarantees that may be

E G

(traded stocks/bonds), cash, and guarantees that may be collateralized (if the guarantor pledge assets to back part or all of the guaranty).

59 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Covenants Covenants

Covenants (vs. Indentures) have value impact, influence the risk to creditors. They help

d 11

p , y p prevent the transfer of wealth from debt holders to equity holders.

nd Chinabon erence

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• An indenture is a formal debt agreement that

establishes the terms of a bond issue, while

EFFAS‐EBC an Confe eneva, 2nd N

, covenants are the clauses of such an agreement.

• Covenants specify the rights of bondholders and

E G

Covenants specify the rights of bondholders and the duties of issuers, such as actions that the issuer is obligated to perform or is prohibited issuer is obligated to perform or is prohibited from performing.

60 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Conditions Conditions

• Conditions: general economic environment for the

particular type of business both nationally and locally

d 11

p yp y y

• What conditions outside of the control of the owners

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• What conditions outside of the control of the owners
• r managers could negatively impact this business?

L l titi i d t t d l i

EFFAS‐EBC an Confe eneva, 2nd N

• Local competition, industry trends, general economic

conditions, legal and regulatory restrictions, and liability concerns

E G

liability concerns.

• Some conditions are outside of the borrower’s control

i d d d l i di i e.g. industry trends and general economic conditions – but they may affect the business’ ability to succeed.

61 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

6

CREDIT RISK MODELS

62 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Ordinal vs Cardinal scale Ordinal vs Cardinal scale

• Risk assessment methods based on scoring methods that rate the

level of risk factors on an ordinal scale are widely used, especially in expert systems

d 11

expert systems

• The use of verbal scales for eliciting estimates of risk’s severity and

probabilities, is subject to some persistent human errors when i i k Th f h i l i h d

nd Chinabon erence

• vember 20

assessing risks. The fact that simple scoring methods are easy to use, combined with the difficulty and time delay in tracking results with respect to reality, means that the proliferation of such

EFFAS‐EBC an Confe eneva, 2nd N

methods may well be due entirely to their perceived benefits

• The need for portfolio analysis is one of the strong reasons why risk

assessment approaches should describe risk in terms of cardinal

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assessment approaches should describe risk in terms of cardinal scale (PD, LGD, correlations, etc), to achieve the necessary flexibility

• f risk description. Mapping from ordinal to cardinal scale is not

always an easy exercise always an easy exercise.

63 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Market Based Credit Risk Models Market Based Credit Risk Models

• For publicly traded company, market models can be

used to derive probabilities of default from the market

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p prices of corporate debt and equity. The general disadvantage of these models is that they rely on the

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g y y availability of market data and also on market efficiency (in particular, liquidity of the traded

EFFAS‐EBC an Confe eneva, 2nd N

instruments). In the lending industry, however, most entities are either unlisted or thinly traded on the stock

E G

exchange, which restricts the use of such models.

• Market based models can be further categorized into

g structural models and reduced form models

64 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Structural models Structural models

• Structural models link the default of an entity to the value
• f the firm through its equity price. These models treat

it ti t b th ’ t d

d 11

equity as an option to buy the company’s assets, and use

• ption pricing formulae to link the equity price, which is

used as a proxy of the (generally unobservable) firm’s asset

nd Chinabon erence

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used as a proxy of the (generally unobservable) firm s asset value, to likelihood of default.

• The obvious benefit of such models is that they can use the

EFFAS‐EBC an Confe eneva, 2nd N

latest market prices to provide a “marked to market” likelihood of default for individual companies. Th j h tf ll f t t l d l i th t th

E G

• The major shortfall of structural models is that they

deliberately simplify the capital structure of a firm, meaning that these models are hardly suitable for analyzing meaning that these models are hardly suitable for analyzing assets that have unusual capital structures or unusual pay‐

• ffs.

65 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Reduced form models Reduced form models

• Reduced form models are generally used in areas such as

bond and credit derivative pricing, and rather than d i lik lih d f d f lt d d f

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producing likelihood of default measures, reduced‐form models are generally used to calculate prices of such assets,

• r spreads between the assets’ yields and the reference

nd Chinabon erence

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• r spreads between the assets yields and the reference

risk‐free yield. Probability of Default (PD) can then be derived from these spreads, if some explicit assumptions b t L Gi D f lt (LGD) d itt d (f l

EFFAS‐EBC an Confe eneva, 2nd N

about Loss Given Default (LGD) are admitted (for example, constant LGD) .

• The reduced‐form models assume that the prices of such

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• The reduced‐form models assume that the prices of such

assets follow stochastic processes. Thus, there are two sets

• f difficulties: firstly the correct process description has to

be developed, and secondly the process needs calibrating to market data.

66 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Latest research Latest research

• The latest academic thinking in the credit risk

modeling area is that the debt and equity market

d 11

g q y data can be combined to produce hybrid models that allow for a more precise estimation of

nd Chinabon erence

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probabilities of default (PD) and possibly of loss given default (LGD).

EFFAS‐EBC an Confe eneva, 2nd N

• Detailed discussion of the credit risk models is

present in our complementary material (Credit

E G

p p y ( Risk Modeling: Literature Review), as well as the problem of measuring PD, LGD and correlations

• f defaults.

67 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

CreditMetrics model CreditMetrics model

• CreditMetrics is one of the first proprietary models of credit
• risk. It utilizes external credit rating as a main credit risk

d i ( k id f th d l ti )

d 11

driver (weak side of the model assumptions) .

• That is, the CreditMetrics model is built around a credit

migration or transition matrix that measures the probability

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migration or transition matrix that measures the probability that the credit rating of any given debt security will change

• ver the course of the credit horizon. The process of rating

EFFAS‐EBC an Confe eneva, 2nd N

migration is therefore regarded as a Markov chain.

• Empirical transition matrices are published by S&P (please

“D f lt T iti d R 2010 A l Gl b l

E G

see “Default, Transition, and Recovery: 2010 Annual Global Corporate Default Study And Rating Transitions”)

68 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Credit ratings and historic default g frequencies

• two ways of deriving PDs from independent credit ratings are used

in practice: cohort analysis and duration analysis.

• Cohort analysis is the simplest method to estimate default

d 11

• Cohort analysis is the simplest method to estimate default

probabilities when credit ratings are available for a relatively large cross‐section of firms or loans. For a given observation period, the b bili f i i f di i h i i l

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• vember 20

probability of migrating from one credit rating to another is simply the observed proportion of firms that experience such migration. In particular, cohort analysis can be used to estimate the default

EFFAS‐EBC an Confe eneva, 2nd N

probability given the credit rating of the firm or bond at the beginning of the period.

• Duration analysis accounts for the time spent in different credit

E G

Duration analysis accounts for the time spent in different credit ratings during the observation period. In duration analysis, the migration intensity is determined as the proportion of firm‐years that migrated from one rating category to the other divided by the that migrated from one rating category to the other divided by the total number of firm‐years.

69 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Econometric models Econometric models

• Regression analysis of macro‐ and/or microeconomic time

series a kind of statistical credit scoring method. M i b d d l ti t d b th

d 11

Macroeconomic‐based models are motivated by the

• bservation that default rates in the financial, corporate,

and household sectors increase during recessions.

nd Chinabon erence

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and household sectors increase during recessions.

• Regressions based on microeconomic data are more

suitable for credit risk analysis. iI particular, widely used in

EFFAS‐EBC an Confe eneva, 2nd N

practice are models based on accounting reporting and default data, aggregated as financial indicators and ratios that fall into broad categories like liquidity; capital

E G

that fall into broad categories like liquidity; capital adequacy, etc.

• Hybrid econometric models combine macroeconomic

Hybrid econometric models combine macroeconomic values, credit ratings, market variables and financial ratios to produce more accurate estimates of default probabilities

70 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Lorenz curves for the different econometric models

0.9 1 ROC Curves Tree Linear logit d 11 0.7 0.8 Linear logit Linear probit Bayesian nd Chinabon erence

• vember 20

0 5 0.6 0.7 Cases EFFAS‐EBC an Confe eneva, 2nd N 0 3 0.4 0.5 Default E G 0 1 0.2 0.3 0.2 0.4 0.6 0.8 1 0.1 Non-Default Cases Non-Default Cases 71 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Econometric model in action: PD of Promeximbank ‐ Early Warning System

25 Dynamics of Probability of Default

d 11

20

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15 , %

EFFAS‐EBC an Confe eneva, 2nd N

10 PD

E G

5 1998 1999 2000 2001 2002 2003 2004 2005 Month

72 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

7

CREDIT RATINGS AGENCIES: EXTERNAL CREDIT QUALITY ASSESSMENT CREDIT QUALITY ASSESSMENT

73 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Credit ratings agencies Credit ratings agencies

• Credit rating agencies are privately organized companies set up to

apply ratings to individual debt securities and entire entities that wish to issue bonds within capital markets The issuers of these

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wish to issue bonds within capital markets. The issuers of these debt securities include publicly traded corporations and distinct branches of government. Bond ratings effectively influence capital flows by supplying and confirming information to investors A credit

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flows by supplying and confirming information to investors. A credit rating measures credit worthiness, or the ability to pay back a loan.

• The top three credit ratings agencies are (all in the United States):

EFFAS‐EBC an Confe eneva, 2nd N

Moody's, Standard & Poor's, Fitch Ratings

• Each rating agency has developed its own system of rating

sovereign and corporate borrowers Fitch Ratings developed a

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sovereign and corporate borrowers. Fitch Ratings developed a rating system in 1924 that was adopted by Standard & Poor's. Moody's grading is slightly different. Moody's sometimes argues that their ratings embed a conceptually superior approach that that their ratings embed a conceptually superior approach that directly considers not only the likelihood of default but also the severity of loss in the event of default

74 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Basel 2 and CRAs Basel 2 and CRAs

• Under Pillar 1 of Basel II, regulatory capital requirements for credit risk are

calculated according to two alternative approaches:

– the Standardized Approach; and

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the Standardized Approach; and – the Internal Ratings‐Based Approach.

• Under the Standardised Approach (SA) the measurement of credit risk is

based on external credit assessments provided by External Credit

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based on external credit assessments provided by External Credit Assessment Institutions (ECAIs) such as credit rating agencies or export credit agencies.

• Under the Internal Ratings Based Approach (IRBA) subject to supervisory

EFFAS‐EBC an Confe eneva, 2nd N

• Under the Internal Ratings‐Based Approach (IRBA), subject to supervisory

approval as to the satisfaction of certain conditions, banks use their own rating systems to measure some or all of the determinants of credit risk. Under the Foundation Version (FV) banks calculate the Probability of

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Under the Foundation Version (FV), banks calculate the Probability of Default (PD) on the basis of their own ratings but rely on their supervisors for measures of the other determinants of credit risk. Under the Advanced Version (AV) banks also estimate their own measures of all the Version (AV), banks also estimate their own measures of all the determinants of credit risk, including Loss Given Default (LGD) and Exposure at Default (EAD).

75 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Rating an issue S&P Rating an issue, S&P

• In rating an individual debt issue, such as a

corporate or municipal bond, Standard & Poor’s

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p p , typically uses, among other things, information from the issuer and other sources to evaluate the

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credit quality of the issue and the likelihood of default.

EFFAS‐EBC an Confe eneva, 2nd N

• In the case of bonds issued by corporations or

municipalities, rating agencies typically begin

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p , g g yp y g with an evaluation of the creditworthiness of the issuer before assessing the credit quality of a specific debt issue

76 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Analyzing debt issues S&P Analyzing debt issues, S&P

In analyzing debt issues, for example, Standard & Poor’s analysts evaluate, among other things:

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• The terms and conditions of the debt security and, if

relevant, its legal structure.

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• The relative seniority of the issue with regard to the

issuer’s other debt issues and priority of repayment in th t f d f lt

EFFAS‐EBC an Confe eneva, 2nd N

the event of default.

• The existence of external support or credit

enhancements s ch as letters

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enhancements, such as letters

• f credit, guarantees, insurance, and collateral. These

protections can provide a cushion that limits the protections can provide a cushion that limits the potential credit risks associated with a particular issue.

77 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Standard & Poor’s ratings Standard & Poor’s ratings

Ratings from ‘AA’ to ‘CCC’ may be modified by the addition of a plus (+) or minus (‐) sign to show relative standing within the major rating categories.

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• ‘AAA’ Extremely strong capacity to meet financial
• commitments. Highest rating

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• ‘AA’ Very strong capacity to meet financial commitments
• ‘A’ Strong capacity to meet financial commitments, but

h ibl d i di i d

EFFAS‐EBC an Confe eneva, 2nd N

somewhat susceptible to adverse economic conditions and changes in circumstances

• ‘BBB’ Adequate capacity to meet financial commitments but

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• BBB Adequate capacity to meet financial commitments, but

more subject to adverse economic conditions

• ‘BBB‐’ Considered lowest investment grade by market

g y participants

78 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Standard & Poor’s ratings: g continuation

• ‘BB+’ Considered highest speculative grade by market participants
• ‘BB’ Less vulnerable in the near‐term but faces major ongoing

uncertainties to adverse business financial and economic

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uncertainties to adverse business, financial and economic conditions

• ‘B’ More vulnerable to adverse business, financial and economic

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, conditions but currently has the capacity to meet financial commitments

• ‘CCC’ Currently vulnerable and dependent on favorable business

EFFAS‐EBC an Confe eneva, 2nd N

• CCC Currently vulnerable and dependent on favorable business,

financial and economic conditions to meet financial commitments ‘ ’ l h hl l bl

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• ‘CC’ Currently highly vulnerable
• ‘C’ A bankruptcy petition has been filed or similar action taken, but

payments of financial commitments are continued payments of financial commitments are continued

• ‘D’ Payments default on financial commitments

79 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

S&P one year performance

(2010 Annual Global Corporate Default Study And Rating Transitions) g )

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EFFAS‐EBC an Confe eneva, 2nd N E G 80 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Table of correspondence of ratings: p g

Moody's, Standard & Poor's, Fitch Ratings

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EFFAS‐EBC an Confe eneva, 2nd N E G 81 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Ratings and borrowing costs Ratings and borrowing costs

• Every company or country that has a rating will be affected

in its borrowing costs, at least in public markets. A higher ki l i t t t f th b d

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ranking means lower interest rates for the borrower and vice versa. The price of credit is set not only by relative credit ratings but also by the general supply of money and

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credit ratings but also by the general supply of money and the specifics of an individual borrowing.

• A low‐rated borrower, for example, can sometimes borrow

EFFAS‐EBC an Confe eneva, 2nd N

more cheaply by securing the bond with a claim on specific assets, or by paying a third‐party to insure the bond. C l hi hl t d b h t t

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• Conversely, a highly‐rated borrower may choose a structure

that attracts a lower rating because of special characteristics of the issue, including its standing in the characteristics of the issue, including its standing in the borrower's capital structure or the jurisdiction in which it is issued.

82 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Controversy Controversy

• The high barriers of entry that require large amounts of

capital, connections and business reputation eliminate titi f th b d ti b i I t

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competition from the bond‐rating business. Investors believe this lack of competition leads to groupthink, where the major CRAs wrongly assign similar ratings to securities

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the major CRAs wrongly assign similar ratings to securities with little threat of recourse from more intelligent

• perators.

EFFAS‐EBC an Confe eneva, 2nd N

• CRAs are also paid by the debt issuer to rate bonds and

provide advice. This conflict of interest may pressure these firms to assign higher ratings than reality would suggest for

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firms to assign higher ratings than reality would suggest, for the sake of keeping business. CRAs are criticized whenever investment‐grade bonds default, as were the events associated with the 2007‐2009 recession.

83 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

CRA may have y considerable delay in reaction

• Empirical studies have documented that yield

spreads of corporate bonds start to expand as

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spreads of corporate bonds start to expand as credit quality deteriorates but before a rating downgrade implying that the market often leads

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downgrade, implying that the market often leads a downgrade and questioning the informational value of credit ratings

EFFAS‐EBC an Confe eneva, 2nd N

value of credit ratings.

• It is even more sluggish for upgrades (see next

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slide).

• Not only bonds but also CDS market can provide

Not only bonds but also CDS market can provide timely information, if available.

84 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Are credit ratings consistent with g credit risk? Case of Alfa Bank (Russia)

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EFFAS‐EBC an Confe eneva, 2nd N E G 85 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

An example: p case of China: Fitch Ratings, 2011

• September 8, 2011 (Reuters) ‐ Fitch Ratings warned on Thursday that it

might downgrade China's credit rating within two years as the country's banks struggle with debt loads following a lending surge to help lift the

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gg g g g p economy during the 2008 financial crisis.

• China's long‐term local currency rating is AA minus; it could be

downgraded over the next 12 to 24 months as Fitch expect a material

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downgraded over the next 12 to 24 months as Fitch expect a material deterioration in bank asset quality. Fitch downgraded the outlook on China's long‐term local currency debt to negative from stable in April 2011 because of concerns about the country's financial stability following

EFFAS‐EBC an Confe eneva, 2nd N

y y g a lending surge encouraged by Beijing to help maintain economic growth during the global downturn.

• Non‐performing loans at Chinese banks were about 2 percent of the total,

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Non performing loans at Chinese banks were about 2 percent of the total, but if lending to local government financing vehicles was appropriately classified, the figure would be more like 6‐7 percent.

• Fitch estimated bank loans would increase this year alone by 18 trillion

Fitch estimated bank loans would increase this year alone by 18 trillion

• yuan. This is the equivalent to 55 percent of China's GDP, which is an

extremely high number and a potential problem for banks' asset quality.

86 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

September 2011 GFSR: opinion about p p China credit performance

Still, while they believe it will be costly, most analysts consider that the likely fallout from China’s credit boom ill b bl O k f fid i

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will be manageable. One key source of confidence is China’s strong fiscal position, including a large stock of public‐sector assets and low central government debt

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public sector assets and low central government debt. Nevertheless, even those buffers do not preclude significant bouts of uncertainty as to how losses will

EFFAS‐EBC an Confe eneva, 2nd N

significant bouts of uncertainty as to how losses will ultimately be allocated among the banks’ private investors and local and central governments. To the h h d i h

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extent that the government needs to step in, the consequence could be a substantial worsening of China’s public debt metrics and a narrower scope for future public debt metrics and a narrower scope for future fiscal stimulus.

87 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Nationally Recognized Statistical y g Rating Organizations (U.S.)

• In of 2009 the U.S. Securities and Exchange Commission (SEC) has identified ten

Nationally Recognized Statistical Rating Organizations (NRSRO) that are registered by the Commission to rate financial institutions, corporations, asset‐backed i i d d b f l i h U i d S Fi h M d ' d

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securities and government debt for sale in the United States. Fitch, Moody's and Standard & Poor's are the most recognized credit rating agencies (CRA) in America and account for the majority of the market share within the bond rating industry. Firms currently registered as NRSROs are:

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Firms currently registered as NRSROs are:

– A.M. Best Company, Inc. – DBRS Ltd. – Egan‐Jones Rating Company

EFFAS‐EBC an Confe eneva, 2nd N

– Fitch, Inc. – Japan Credit Rating Agency, Ltd. – Kroll Bond Rating Agency, Inc. (f/k/a LACE Financial Corp.) – Moody’s Investors Service, Inc.

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Moody s Investors Service, Inc. – Rating and Investment Information, Inc. – Realpoint LLC – Standard & Poor’s Ratings Services

The US Congress and other regulatory agencies have since required that banks and institutional investors only buy bonds rated investment‐grade (those giving ratings of BBB or higher) by one of the NRSROs. The license has been a boon to the NRSROs.

88 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

National CRA in Russia recognized by government

• For the first time in 2010 in Russia seven rating agencies ‐ three

international and four Russian ‐ received official government accreditation by Ministry of Finance.

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y y

• In assessing the reliability of certain assets, restrictions or increase their

investments in pension funds, placing of state corporations will pay attention and recognize only the opinions of these market participants.

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attention and recognize only the opinions of these market participants. These are

– international agencies: Moody s Investor Service, Fitch Ratings, Standard & Poor s.

EFFAS‐EBC an Confe eneva, 2nd N

Poor s. – Russian agencies: National rating agency, AK & M, RusRating agency and "Expert RA".

• With regard to the seven rating agencies and the Ministry of Finance

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With regard to the seven rating agencies and the Ministry of Finance Central Bank opinions match.

• Our presentation concerning statistical analysis of credibility of ratings

assigned by Russian CRA is available as complementary material assigned by Russian CRA is available as complementary material

89 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

CRA in China CRA in China

• Since 2003, insurance companies and the National Council for Social Security Fund,

were given permission to invest in highly rated domestic corporate bonds by an authorized credit rating firm; the National Development and Reform Commission

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authorized credit rating firm; the National Development and Reform Commission indicated that corporate bonds issued from that point forward could only be rated by an agency which had already provided a domestic bond rating since the year 2000 A f 2008 h di d i 5 Chi Ch i C di R i C

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2000.As of 2008, the accredited agencies were 5: China Chengxin Credit Rating Co. (中信), Dagong Rating Co. (大公), China Lianhe Credit Rating Co. (合), Shanghai Brilliance Credit Rating & Investors Service (上海新世), Shanghai Far East (上海

EFFAS‐EBC an Confe eneva, 2nd N

)

• Since China has not permitted foreign credit rating agencies to rate locally issued

bonds independently they have had to access the market through partnerships

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bonds independently, they have had to access the market through partnerships with one of these local agencies. In particular, In 1999, Fitch and China Chengxin started a joint venture, China Chengxin International, but their arrangement collapsed in 2003 Mood ’s hich had a technical co operation agreement ith collapsed in 2003. Moody’s, which had a technical co‐operation agreement with Dagong in the late 1990s, eventually replaced Fitch in 2006 and reconstituted China Chengxin International with China Chengxin.

90 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

8

INTERNAL CREDIT RATINGS

91 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

What should be an ideal internal credit rating system

• A number of rating techniques and methodologies

have evolved over time. The methodologies range from f l / f l d

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a spectrum of purely expert/professional judgment taking into account only qualitative factors, to a sophisticated statistical model based methodology

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sophisticated statistical model based methodology solely taking into account the quantitative factors.

• Although the degree of subjectivity becomes lesser

EFFAS‐EBC an Confe eneva, 2nd N

• Although the degree of subjectivity becomes lesser

with the movement on the spectrum towards statistical methods, yet neither of the two extremes is advisable.

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, y An ideal internal risk rating system is based on both quantitative and qualitative factors concluding the d i i b d diff ib i l i decision based on many different attributes, involving the human judgment.

92 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Rating systems that combine g y models with judgment

In practice, many banks use rating systems that combine models with judgment. Two approaches are common.

• Judgmental systems with quantitative guidelines or model results as

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Judgmental systems with quantitative guidelines or model results as

• inputs. Historically, the most common approach to rating has involved

individuals exercising judgment about risks, subject to policy guidelines containing quantitative criteria such as minimum values for particular

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containing quantitative criteria such as minimum values for particular financial ratios. Banks develop quantitative criteria to guide individuals in assigning ratings, but often believe that those criteria do not adequately reflect the information needed to assign a rating.

EFFAS‐EBC an Confe eneva, 2nd N

g g

• Model‐based ratings with judgmental overrides. When banks use rating

models, individuals are generally permitted to override the results under certain conditions and within tolerance levels for frequency. Credit‐rating

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certain conditions and within tolerance levels for frequency. Credit rating systems in which individuals can override models raise many of the same issues presented separately by pure judgment and model‐based systems. If overrides are rare, the system can be evaluated largely as if it is a model‐ , y g y based system. If, however, overrides are prevalent, the system will be evaluated more like a judgmental system.

93 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Portfolio level Portfolio level

• Whatever the method used, the result of the evaluation should be

in such a shape that provides meaningful information which can be further used for effective credit risk measurement and

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further used for effective credit risk measurement and management of the credit exposure at an individual level as well as at a portfolio level Ri k i id f i f l diff i i f

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• Risk rating process must provide for a meaningful differentiation of

risk, grouping of satisfactory homogenous exposures, and must allow for accurate and consistent estimation of loss characteristics

EFFAS‐EBC an Confe eneva, 2nd N

at pool level.

• The internal risk rating system should be integrated with other

systems of the banks such as portfolio monitoring loan loss

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systems of the banks such as portfolio monitoring, loan loss reserves analysis for provisioning, pricing of the loan, internal capital planning and return on capital analysis. Banks should not use separate rating systems for lending purposes risk quantification separate rating systems for lending purposes, risk quantification (cardinal scale assessment) and capital allocation.

94 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Two tier rating system Two tier rating system.

The internal risk ratings should be based on a two tier rating system.

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g y

• 1. An obligor rating, based on the risk of borrower

default and representing the probability of default by a

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p g p y y borrower or group in repaying its obligation in the normal course of business and that can be easily

EFFAS‐EBC an Confe eneva, 2nd N

mapped to a default probability bucket.

• 2. A facility rating, taking into account transaction

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f y g, g specific factors, and determining the loss parameters in case of default and representing loss severity of principal and/or interest on any business credit facility.

95 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Obligor ratings Obligor ratings

• In order to assign obligor ratings the banks are required to consider,

but not limited to, the following aspects of the borrower:

• Financial Condition

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• Financial Condition

– Economic and financial situation – Leverage

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g – Profitability – Cash flows

M t d hi t t

EFFAS‐EBC an Confe eneva, 2nd N

• Management and ownership structure

– Ownership structure – Management and quality of internal controls

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Management and quality of internal controls – Promptness/ assessment of the willingness to pay

– Strength of Sponsors

I d t ti d t f b i

• Industry properties and sector of business
• Country risk (if appropriate)

96 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Facility ratings Facility ratings

• In order to assign the facility ratings, the bank should consider the

relevant and material information including :

• Facility

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Facility

– Nature and purpose of loan – Product type and structure P i it f i ht i f b k t

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– Priority of rights in case of bankruptcy – Degree of collateralization – Composition of collateral

EFFAS‐EBC an Confe eneva, 2nd N

• Collateral

– Nature and Quality – Liquidity

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q y – Market value – Exposure of the collateral to different risks – Legal status of rights – Legal status of rights – Legal enforceability – Time required to dispose of

97 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Qualifying rating system for IRB Qualifying rating system for IRB

• Rigorous credit risk measurement is a necessary element of

advanced risk management. Qualifying institutions will use their internal rating systems to associate a probability of default (PD)

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internal rating systems to associate a probability of default (PD) with each obligor grade, as well as a loss given default (LGD) with each credit facility. In addition, institutions will estimate exposure at default (EAD) and will calculate the effective remaining maturity (M)

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default (EAD) and will calculate the effective remaining maturity (M)

• f credit facilities.
• Qualifying institutions will be expected to have an IRB system

EFFAS‐EBC an Confe eneva, 2nd N

consisting of four interdependent components:

– A system that assigns ratings and validates their accuracy, – A quantification process that translates risk ratings into IRB

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– A quantification process that translates risk ratings into IRB parameters, – A data maintenance system that supports the IRB system, O i h d l h i h h i – Oversight and control mechanisms that ensure the system is functioning as intended and producing accurate ratings .

98 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Standards for IRB rating systems Standards for IRB rating systems

Institutions have to comply in designing and operating IRB rating systems subject to five broad standards:

• Two‐dimensional risk‐rating system – IRB institutions must be able to make

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g y meaningful and consistent differentiations among credit exposures along two dimensions—obligor default risk and loss severity in the event of a default.

• Rank order risks – IRB institutions must rank obligors by their likelihood of default,

d f ili i b h l i d i d f l

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and facilities by the loss severity expected in default.

• Calibration – IRB obligor ratings must be calibrated to values of the probability of

default (PD) parameter and loss severity ratings must be calibrated to values of the loss given default (LGD) parameter

EFFAS‐EBC an Confe eneva, 2nd N

loss given default (LGD) parameter.

• Accuracy – Actual long‐run actual default frequencies for obligor rating grades

must closely approximate the PDs assigned to those grades and realized loss rates

• n loss severity grades must closely approximate the LGDs assigned to those

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• n loss severity grades must closely approximate the LGDs assigned to those

grades.

• Validation process – IRB institutions must have ongoing validation processes for

rating systems that include the evaluation of developmental evidence, process rating systems that include the evaluation of developmental evidence, process verification, benchmarking, and the comparison of predicted parameter values to actual outcomes (back‐testing).

99 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

9

BOND SPREADS AND RISK FREE ZERO COUPON YIELD CURVE ZERO‐COUPON YIELD CURVE

100 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Bond spread Bond spread

• Bond spread is the difference between the yields of two bonds with

different credit quality and liquidity. For example, a corporate bond in US with a certain amount of risk is compared to

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bond in US with a certain amount of risk is compared to a standard risk‐free (???) treasuries with relevant maturity

• The bond spread show the risk premium, that

i ddi i l i ld h ld b d f b d hi h h

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is additional yield that could be earned from a bond which has a higher risk.

• The risk‐free spot yield curve in dollar were definitely easier, since

EFFAS‐EBC an Confe eneva, 2nd N

The risk free spot yield curve in dollar were definitely easier, since all US federal government notes and bonds had until recently the same credit rating (although the liquidity may vary).

• The main difficulty of our case is that the notes we need to analyze

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• The main difficulty of our case is that the notes we need to analyze

are of different credit quality.

• Currently there is no generally accepted standard for determination

y g y p

• f the risk‐free zero yield curve in the Eurozone

101 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

10‐Y Government bond yield spreads in the Euro area spreads in the Euro area.

Source: Datastream/Thomson Financial

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EFFAS‐EBC an Confe eneva, 2nd N E G 102 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

EFFAS‐EBC Standard on Risk‐Free Spot Yield Curve and bond spreads

• In June 2006 European Bond Commission adopted an important

standard “Methodology for Definition of Risk Free Zero‐Coupon Yield Curve and Spreads in the Eurozone” (see http://www effas‐

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Yield Curve and Spreads in the Eurozone (see http://www.effas ebc.org/projects/yield‐curves.html), setting standardized rules for constructing and calculation of the risk‐free zero‐coupon spot yield curve and credit spreads based on the bond market data (prices

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curve and credit spreads based on the bond market data (prices, quotes, bid‐ask spreads, outstanding volumes etc.) available for government notes (medium and long‐term) nominated in Euro

EFFAS‐EBC an Confe eneva, 2nd N

• Risk‐free zero‐coupon yield curve gives market practitioners a

common reference point for accurate estimation of present value

• f money, especially for financial engineering and risk management

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y, p y g g g applications

103 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Before‐crisis country spreads y p relative Average Yield Curve Level

15 20 AUSTRIA BELGIUM

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5 10 15 GERMANY SPAIN FINLAND FRANCE GREECE IRELAND

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−5 5 IRELAND ITALY NETHERLANDS PORTUGAL Risk−free

EFFAS‐EBC an Confe eneva, 2nd N

−15 −10 −5

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50 100 150 200 250 −20 −15 01−Feb−2005 12−Apr−2005 21−Jun−2005 30−Aug−2005 08−Nov−2005 17−Jan−2006

104 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Universality of the EFFAS‐EBC Methodology

• The approach developed for construction of a risk free zero‐

coupon yield curve in the Eurozone is also applicable to construct a risk free zero‐coupon yield curve in a particular

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construct a risk free zero coupon yield curve in a particular country, using benchmark government, municipal and corporate bonds.

• In case of low liquid market the procedure for yield curve

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• In case of low liquid market the procedure for yield curve

construction should be more elaborate. We suggest to perform data filtration and then projections for missing market data (using historical data) at the first stage and after

EFFAS‐EBC an Confe eneva, 2nd N

market data (using historical data) at the first stage and after that apply a fitting method at the second stage.

• The Methodology was successfully tested using Swiss and

R i b d k d A li bl Chi b d k

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Russian bond market data. Applicable to Chinese bond market after right interpretation

• f

data filtration and then projections for missing market data.

105 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Russian market: before‐crisis bond spreads p

300 400 MinFin Gazprom Karelia

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200 300 Karelia Komi Moscow Moscow region RZHD Risk−free

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100 Risk−free

EFFAS‐EBC an Confe eneva, 2nd N

−200 −100

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20 40 60 80 100 120 140 160 180 200 −300 −200 11−Jan−2005 24−Mar−2005 06−Jun−2005 16−Aug−2005 25−Oct−2005

106 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Before‐crisis liquidity of Russian q y exchange traded bonds (for MICEX)

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EFFAS‐EBC an Confe eneva, 2nd N E G 107 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Before‐crisis liquidity of Chinese exchange q y g traded bonds (for Shanghai SE)

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EFFAS‐EBC an Confe eneva, 2nd N E G 108 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Relation between credit risk and liquidity

• Financial crises underscore the vital relation between

credit risk and liquidity, and between liquidity in d ff k h d k

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different markets. As investors re‐assess the credit risk

• f many types of securities in their portfolio, trading of

these securities dries up and prices across a broad

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these securities dries up, and prices across a broad range of instruments and markets plummet.

• It was not catched up by the models used for pricing of

EFFAS‐EBC an Confe eneva, 2nd N

• It was not catched up by the models used for pricing of

subprime MBS, being one of the catalyst of financial crisis in 2007.

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• Quantifying credit risk directly from bond prices is

impossible, since the credit risk and liquidity impact p , q y p are not separately observable in the bond spread.

109 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011

Preparation of revisions to the p EFFAS‐EBC Methodology

• Currently the work on this project is concentrated on the

bl f d i t b d i ld d i t

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problem of decomposing corporate bond yield spreads into their credit risk and liquidity components.

• It is important in itself for multiple reasons:

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It is important in itself for multiple reasons:

– First, policymakers and banks must approach the external supervision and the internal management of these risks differently.

EFFAS‐EBC an Confe eneva, 2nd N

– Second, when analyzing the impact of new information regarding an issuer's credit risk on prices of fixed‐income instruments, we must know to which extent liquidity changes also affect these

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prices. – Third, if an issuer of bonds attempts to reduce its cost of debt, it may find it cheaper to improve the liquidity of its traded bonds, may find it cheaper to improve the liquidity of its traded bonds, compared to reducing its credit risk (example – Moscow debt agency)

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Credit default swaps (CDS) Credit default swaps (CDS).

• In the last decade, an active market for trading of credit risk

has developed through credit default swaps (CDS).

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• Credit default swaps initially conceived as a facility of

protection from credit risk (similar to a traditional insurance policy but it is not) allow investors

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traditional insurance policy, but it is not) allow investors (and now it is dominant behavior) to speculate on changes in CDS spreads of single names or of market indices such as

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the North American CDX index or the European iTraxx index. Thi d l t f li id CDS k t h f ilit t d th

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• This development of liquid CDS market has facilitated the

estimation of the credit risk component in bond spreads. The simplest way is to use the CDS mid premium as a The simplest way is to use the CDS mid premium as a measure of pure credit risk and attribute the residual bond spread to liquidity effects.

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Liquidity considerations Liquidity considerations

• Recent findings show that it is important to take into

consideration the liquidity of CDS contracts in particular CDS bid d k t d t li itl d l b d i

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CDS bid and ask quotes and to explicitly model bond prices and CDS bid and ask quotes as a function of interest rates, credit risk, and market liquidity.

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credit risk, and market liquidity.

• This more general view has two important consequences.

– First, the CDS mid premium does not necessarily coincide with

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, p y the pure credit risk premium, but may also contain a liquidity component. – Second by deriving directly comparable liquidity premia in bond

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– Second, by deriving directly comparable liquidity premia in bond spreads and CDS premia, one can study liquidity spillovers between the CDS market and the underlying bond market.

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Attribution of bond spread p component

• Using a kind of reduced credit model Bühler and Trapp

in 2009 found on the market dataset for bonds and

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CDS the following attribution:

– 60% of the bond spread is due to credit risk,

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p , – 35% to liquidity, and – 5% to the correlation between credit risk and liquidity.

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5% to the correlation between credit risk and liquidity.

• For CDS:

the credit risk component constitutes 95% of the observed

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– the credit risk component constitutes 95% of the observed mid premium, the pure liquidity component 4% – the pure liquidity component 4%, – the correlation component 1%.

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Thank o for o r attention! Thank you for your attention!

Sergey Smirnov EFFAS‐EBC Vice Chairman

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EFFAS EBC Vice Chairman SergeySmirnov@EFFAS‐EBC.org

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Director of Financial Engineering and Risk Management Laboratory Head of Risk Management and Insurance Department National Research University "Higher School of Economics"

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11 Pokrovskiy boulevard Moscow Russia tel /fax +7 495 6216762

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tel./fax +7 495 6216762 Public profile: http://ru.linkedin.com/in/snsmirnov

114 EFFAS‐EBC and Chinabond Seminar Geneva, 2nd November 2011