FIN344: Credit Risk Management

Topic 9: Advanced Credit Risk Management Methods

Presented by Darren O’Connell

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Disclaimer

• The views expressed are those of the presenter,and may not reflect those of Kaplan OHE.

•Use of information contained in this presentationis at your own risk. The presenter recommendsyou seek independent professional advice priorto making any investment decisions.

• The information in this presentation is notintended as investment advice. The presenter isnot offering or making recommendations inrelation to securities or other financial orinvestment products.

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Subject & Topic learning outcomes

• On completion of this topic, students should be able to:

- Define the main types of advanced quantitative models.

- Describe, in general terms, the underlying methodology of each model

type.

- Recognise the complexity and ongoing evolution of the credit risk

management process.

• This topic specifically addresses the following subject learning

outcomes:

1. Compare credit risk and market risk.

2. Assess credit risk in a range of different scenarios.

3. Evaluate credit derivatives and how they are used in a range of different

scenarios.

4. Analyse a range of credit products.

5. Analyse the regulatory framework for credit risk in Australia and

internationally.

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1.0 Structural Credit Models

• The basis of the structural approach is that corporate

liabilities are contingent claims on the assets of a firm.

• As recalled from basic corporate finance theory:- Assets = Liabilities + shareholder’s equity

• The limited liability of a firm means that shareholders have

the right, but not the obligation, to pay off the debt holders

and assume ownership of the firm’s residual assets.

• Until this happens, the debt holders own the firm until its

liabilities are paid off.

• As such, shareholders possess a call option on the firms

assets with a strike, or exercise price, equal to the book

value of the firm’s liabilities.

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1.1 The KMV Framework

• KMV, owned by Moodys, has produced a series of credit risk tools

that estimate Expected Default Frequency (EDF) and the Loss

Distribution of a credit portfolio.

• Under this approach there are three elements that determine the

default probability of any given (rateable) firm:

- Value of Assets is the market price of the firm's assets; it measures the present

value of the future free cash flows produced by the firm's assets discounted

back at the appropriate discount rate.

- Asset Risk which is the uncertainty, or risk, of the asset value; it measures the

firm's ongoing viability and is an uncertain variable.

- Leverage which is the extent of the firm's contractual liabilities. The book value

of liabilities relative to the market value of assets measures firm leverage, since

that is the amount the firm must repay.

• The default risk of the firm increases as the value of the

assets becomes insufficient to repay the book value of its

liabilities. However, not all firms default at this point.

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1.1 The KMV Framework

• The KMV model consists of 6

variables:1. The current asset value.

2. The distribution of asset value.

3. The volatility of the future asset value

4. The level of the default point, the

book value of liabilities.

5. The expected rate of growth in the

asset value over the horizon.

6. The length of the horizon, H.

• The first four variables are

critical inputs.

• The probability of asset values

being worth less than liabilities

is shown as the shaded EDF

area.

• The gap between the mean and

the EDF is the Default Distance

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1.1 The KMV Framework

• The Default Distance (DD) is the measure that allows

Moodys to assign credit ratings to firms based on historical

outcomes of firms with the same DD.

• However the DD is difficult to measure:- Default probability requires an accurate appreciation of the likelihood of adverse

changes to asset values.

- Defaulting firms experience large and sudden (rather than small and

incremental) changes in asset values that are difficult to predict.

- Asset values and changes in leverage are highly correlated variables.

- KMV first measures the DD as the number of standard deviations the asset

value is away from default.

- It then uses empirical data (i.e. actual default stats) to determine the

corresponding probability of default.

• As such, the KMV approach is still subjective in a number of

critical points but is a widely used and reliable product.

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1.2 CreditMetrics

• CreditMetrics is an alternative product much the same as KMV.

• The release of the model’s technical documentation allowed

FSE to make advances in internal risk management

techniques.

• In turn, this has influenced industry developments such as

CreditMetrics publishing a valuation model for collateralised

debt obligations (CDOs – see Topic 7).

• Both KMV and CreditMetrics models assume that:- There is a process to asset values.

- Default or survival depends on the state of assets over a given timeframe.

- If asset values fall below liabilities then default is assumed to have occurred (and

vice versa).

• Both models are complex hence why there are only two major

suppliers (to high-end users).

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2.0 Reduced Form Models

• Reduced form models assume that default occurs without

warning at an exogenous default rate, or intensity.

• The dynamics of the intensity are specified under some pricing

probability.

• Instead of asking why the firm defaults, the intensity model is

calibrated from market prices.

• The reduced-form approach directly models the conditional

default rate, which is often preferred for pricing.

• These models are complex and will not be discussed any

further.

• The two main quantitative approaches to analysing credit risk

discussed so far ignore issues such as fraud and opaque

accounting practices which are unavoidable in practice.

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3.0 Incomplete Information Models• The incomplete information (also known as I2) framework contains

previously unrecognised structural/reduced form hybrids, some of which

incorporate the best features of both traditional approaches.

• A ‘first-passage time’ model assumes that a firm defaults when its value

falls below a certain barrier, in a similar way to the KMV approach.

• First-passage time models are widely used since they take account of

the empirical fact that default can occur at any time.

• Such a model assumes investors do not know where the default barrier

lies.

• The importance of modelling uncertainty about the default barrier is

highlighted by high-profile scandals at firms such as Enron. In these

cases, public information led to poor estimates of the default barrier

under the previous approaches.

• The I2 model explicitly recognises that default cannot be anticipated

unlike other approaches.

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3.1 Leverage Models

• Leverage, which is the ratio of a firm’s debt to its value, is a key input to

most structural credit models.

• The I2 model incorporates the history of a firm’s leverage, not just its

current value.

• The height of the default barrier, which is its largest possible value, is

based on the maximum historical leverage ratio.

• When a firm first hits a historically high leverage ratio, short-term

forecasts jump up, since the model allows that the distance to default

might be arbitrarily small.

• When the leverage ratio falls from its historical high, the distance to

default acquires a positive lower bound, and short-term forecasts jump

down.

• This suggests that the I2 model has better reactivity relative to others

types of credit risk predictive models.

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3.1 Leverage Models

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3.2 Default Beyond Forecast

• The I2 model has been extended to price defaultable securities.

• This extension takes into account the abrupt drops in security values

that accompany default.

• It also includes a decomposition of the credit risk premium into two

economically meaningful components:

- The first is associated with the variation in firm value prior to default.

- The second comes from the jumps in prices that happen at default.

• The two components of the credit risk premium and implied recovery

rates can be disentangled and extracted from market prices of

defaultable securities.

• Thus, they can be integrated into portfolio risk forecasts and hedged

separately.

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3.3 The Future of Predictive Credit Risk

Models?• Incomplete information has recently been incorporated into the I2 model

and comes into play through an unobservable default barrier.

• This extended model combines a known default barrier with noisy

estimates of firm value in a first-passage time model.

• By recognising that the level of default is unknown, the I2 model

incorporates the strengths of the structural and reduced-form models

while avoiding their shortcomings.

• It is a simple, economically reasonable model that reacts quickly to new

information and accounts for the short-term uncertainty inherent in the

market.

• Other modifications and extensions have been explored theoretically but

awaits implementation.

• These may lead to a host of interesting new results.

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4.0 Summary

• In this topic, we discussed the three main quantitative approaches

to analysing credit risk:

Structural Approach – Explicit assumptions are made about a firm’s (the

customer) assets, its capital structure, its debt and its shareholders. Default

occurs if assets are insufficient according to some measure. As such a

corporate liability is seen as an option on the firm’s assets.

Reduced Form Approach – This approach is silent about why a firm defaults.

Instead, the dynamics of default are exogenously given through a default rate,

or intensity. In this approach, prices of credit sensitive securities can be

calculated as if they were default free using an interest rate that is the risk-free

rate adjusted by the intensity.

Incomplete Information Approach – This approach combines the structural

and reduced form models. While avoiding their difficulties, it picks the best

features of both approaches: the economic and intuitive appeal of the structural

approach and the tractability and empirical fit of the reduced form approach.

Thank you and good luck!