SlideShare une entreprise Scribd logo
1  sur  32
Credit Risk Models
Question: What is an appropriate
modeling approach to value
defaultable debt (bonds and loans)?
Gieseke
“Credit Risk Modeling and Valuation: An Introduction,” October 2004
Three main approaches to modeling credit risk
in the finance literature
•

•

•

Structural approach: Assumptions are made about the dynamics
of a firm’s assets, its capital structure, and its debt and share
holders. A firm defaults if the assets are insufficient according to
some measure. A liability is characterized as an option on the firm’s
assets.
Reduced form approach: No assumptions are made concerning
why a default occurs. Rather, the dynamics of default are
exogenously given by the default rate (or intensity). Prices of credit
sensitive securities can be calculated as if they were default free
using the risk free rate adjusted by the level of intensity.
Incomplete information approach: Combines the structural and
reduced form approaches.
Structural approach: default in the classical Merton
model (1974).
1. We want to use the structural approach to
incorporate bond default risk in bond valuation
The value of the firm’s assets are assumed to follow the process,

where μ is the instantaneous expected rate of return on assets, and
σ is the standard deviation of the return on assets.
Let D(t,T) be the date t market value of debt with promised payment B
at date t.

The second line in (18.2) says that the payoff to the creditors equals
the promised payment (B) minus the payoff on a European put
option written on the firm’s assets with exercise price B.
Market value of firm debt, D(t)
Let P(t,T) represent the current date t price of a default-free, zerocoupon bond that pays $1 at date T, where the bond conforms with
the Vasicek model in Ch. 9.
Pennacchi asserts that using results for pricing options (Ch. 9.3)
when interest rates are random (as in 9.58), we can write
Market value of firm equity, E(t)

Shareholder equity is similar to a call option on the firm’s assets, since
at maturity the payoff to equity holders is max [A(t) – B, 0].
However, shareholder equity is different from a European option if the
firm pay dividends to shareholders prior to maturity as reflected in
the first term of the last line in (18.4) where δ denotes the dividend
rate.
Critique of the Merton model
The Merton model assumption is that the firm has a single issue of zero-coupon
debt. That is unrealistic. Modeling multiple issues with different maturities
and seniorities complicates default.
In response some models have suggested that default occurs when the firm’s
assets hit a lower boundary. That boundary has a monotonic relation to the
firm’s total outstanding debt. The first passage time is when the value of the
firm’s assets crosses through the lower boundary.
First passage model - - bond indenture provisions often include safety
covenants that give bond holders the right to reorganize the firm if the value
falls below a given barrier.
The first passage model defines the survival probability as p(t,T) that the
distance to default does not reach zero at any date τ between t and T. The
distance to default is often measured in terms of standard deviations.
Structural approach: default in the first passage
model (Black & Cox, 1976)
2. We want to use the reduced form approach to
incorporate bond default risk in bond valuation
The reduced form model was developed to overcome the
nontradeability and nonobservability of the firm’s asset value
process (Jarrow &Turnbull, 1992).
Default is not tied to the dynamics of asset prices and this breaks the
link between the firm’s balance sheet and the likelihood of default.
Rather, default is based on an exogenous Poisson process, so it may
be better able to capture the effects of default due to additional
unobserved factors.
Reduced form models can also be used to value defaultable bonds
using the techniques used for default-free bonds.
In the reduced form framework, we assume that the default event
depends on a “reduced form process,” that may depend on the
firm’s assets and capital structure, but also on other macroeconomic
factors that influence default.
The default event for a firm’s bond is modeled as a Poisson process
with a time-varying “default intensity.”
Conditional on no default occurring up to date t, the instantaneous
probability of default at (t, t+dt) is denoted as λ(t) dt, where λ(t) is the
physical default intensity, or “hazard rate,” where it is assumed that
λ(t) ≥ 0. Since λ(t) is time-varying, it may be linked to changes in
underlying state variables.
We can compute the physical probability that a bond will not default
from date t to date τ where t ≤ τ ≤ T. This physical survival
probability is written
Zero recovery bond
(bond holders receive nothing in the event of a default)
With zero recovery the bondholder payoff at date T is either D(T,T) = B
if no default occurs, or D(T,T) = 0 if default occurred during (t,T).
If we apply risk-neutral pricing, the date t value of a zero-recovery bond
can be written

where r is the instantaneous “default-free” interest rate, which gives
us the risk-neutral default intensity rather than the physical default
intensity in (18.5).
The risk-neutral default intensity accounts for the market price of risk
due to the Poisson arrival of the default event.
Value of the zero-recovery defaultable bond
Using the calculated survival probability in (18.5) we get

So, (18.9) indicates that valuing a zero-recovery defaultable bond is
similar to valuing a default-free bond, except that we use the
discount rate, r(u) + λ(t), rather than just r(u).
Pennacchi - Problem #2
Pennacchi - Problem #3
Default depends on both Brownian motion vector (dz) for
the state variables and the Poisson process (dq) for arrival
of default) - - the default process is “doubly stochastic”
3. We want to extend the structural and reduced
form models for bonds to the case of bank loans
The link between loans and optionality can be illustrated by a payoff
function to a bank lender. Here repayment of the loan requires
amount 0B. But the market value of project assets can be AL or AH.
At AL the borrow would have an incentive to default on the loan
contract by forfeiting the assets to the bank. Above 0B the bank
earns a fixed return on the loan.
This is analogous to the payoff to a put option writer on a stock with
exercise price B.
Structural model (KMV)
The value of a put option on a stock can be written as,
F(S, X, r, σ, T)
The value of a default option on a loan can be written as,
G(A, B, r, σA, T)
where A is the value of the firm’s assets and B is the repayment at
maturity. We note that the values for A and σA are not directly
observable.
The KMV Credit Monitor Model turns the bank’s lending problem
around and considers it from the perspective of the borrower.
To solve for the two unknowns, A and σA , the model uses
•
•

the structural relationship between market value of equity and
market value of assets, and
the relationship between volatility of assets and volatility of equity.
Loan repayment from the perspective of the
borrower (equity holder)
The payoff function of the equity holder is a call option on the assets of
the firm, H(A, σA, r, B, T).
KMV solves the unobservables problem by assuming that σE = g(σA)
where σE is the observable volatility of firm equity and with two
equations in two unknowns, we can solve for A and σA . Once these
values are derived, KMV calculates the expected default frequency
(EDF).
Calculating the theoretical EDF
If A = 100, σA = 10, and B = 80, the distance to default = (A-B)/σA =
2 standard deviations. The value of assets would have to decline by
2 standard deviations in order to enter default.
Based on a sufficiently large sample of firms, we
can map the distance to default into EDF
Critique of the KMV model
It is difficult to construct the theoretical EDF curves without the
assumption of normality of asset returns
Private firm EDFs can only be constructed by using accounting data
and other observable characteristics of the borrower
The KMV approach does not distinguish between different types of
debt (bonds that vary by seniority, collateral, covenants,
convertibility, etc.)
The KMV model is static - - once the debt is in place the firm does not
change it. The default behavior of firms that manage their leverage
positions is not captured.
Reduced-form model (CreditRisk+)
The Credit Risk+ model is based on an insurance approach where
default is an event that resembles other insurable events (casualty
losses, death, injury, etc.). These are generally referred to as
mortality models which involve actuarial estimate of the events
occurring.
• Default is modeled as a continuous variable with an underlying
probability distribution.
• Default uncertainty is one type of uncertainty, there is also
uncertainty surrounding the size or severity of the loss.
• Loss severities are distributed into “bands,” and the number of
bands is adjusted to get greater accuracy in the estimation.
• The frequency of losses and the severity of losses produce a
distribution of losses for each band. Summing across these bands
we construct the loss distribution for a portfolio of loans.
Constructing the loss distribution in the
CreditRisk+ model
Using the formula for the Poisson distribution
The calculated probability of default in band 1
The distribution of defaults in band 1
The loss distribution for a single loan portfolio
(severity rate = $20,000 per $100,000 of loan)
Critique of the CreditRisk+ model
The observed distribution of losses may have a larger variance than
the model shows. This would tend to underestimate the true
economic capital requirement.
• This may be due to an assumption that the mean default rate is
constant within each band. So, increase the number of bands for
more accuracy.
• Default rates across bands may be correlated due to underlying
state variables that have broader impact on borrowers.
• The predictive usefulness of the approach depends on the size of
the sample of loans.
• The model is not a “full VaR model” because it concentrates on loss
rates, not on loan value changes. It is a default model, not a markto-market model.

Contenu connexe

Tendances

Chapter 3 - Risk Management - 2nd Semester - M.Com - Bangalore University
Chapter 3 - Risk Management - 2nd Semester - M.Com - Bangalore UniversityChapter 3 - Risk Management - 2nd Semester - M.Com - Bangalore University
Chapter 3 - Risk Management - 2nd Semester - M.Com - Bangalore UniversitySwaminath Sam
 
"Credit Risk-Probabilities Of Default"
"Credit Risk-Probabilities Of Default""Credit Risk-Probabilities Of Default"
"Credit Risk-Probabilities Of Default"Arun Singh
 
Capital adequacy norms
Capital adequacy normsCapital adequacy norms
Capital adequacy normsRahul Sir
 
Toward Credit Portfolio Management
Toward Credit Portfolio Management Toward Credit Portfolio Management
Toward Credit Portfolio Management Eric Kuo
 
Chapter 08 risk management in banks
Chapter 08    risk management in banksChapter 08    risk management in banks
Chapter 08 risk management in banksiipmff2
 
Liquidity Risk Measurement
Liquidity Risk MeasurementLiquidity Risk Measurement
Liquidity Risk MeasurementRaja Abdarrahman
 
Risk management basel ii
Risk management basel iiRisk management basel ii
Risk management basel iiUjjwal 'Shanu'
 
Credit risk management (2)
Credit risk management (2)Credit risk management (2)
Credit risk management (2)Ujjwal 'Shanu'
 
Credit Risk Management Presentation
Credit Risk Management PresentationCredit Risk Management Presentation
Credit Risk Management PresentationSumant Palwankar
 
Credit risk management presentation
Credit risk management presentationCredit risk management presentation
Credit risk management presentationharsh raj
 
Interest Rate Risk And Management
Interest Rate Risk And ManagementInterest Rate Risk And Management
Interest Rate Risk And Managementcatelong
 
Capital adequacy (final)
Capital adequacy (final)Capital adequacy (final)
Capital adequacy (final)Harsh Chadha
 
Counterparty credit risk. general review
Counterparty credit risk. general reviewCounterparty credit risk. general review
Counterparty credit risk. general reviewRoman Kornyliuk
 
Asset liability management
Asset liability managementAsset liability management
Asset liability managementTeena George
 
01.2 credit risk factors and measures
01.2   credit risk factors and measures01.2   credit risk factors and measures
01.2 credit risk factors and measurescrmbasel
 
Capital Adequacy
Capital AdequacyCapital Adequacy
Capital AdequacyT A Sairam
 

Tendances (20)

KMV model
KMV modelKMV model
KMV model
 
Fundamentals of Market Risk Management by Dr. Emmanuel Moore ABOLO
Fundamentals of Market Risk Management by Dr. Emmanuel Moore ABOLOFundamentals of Market Risk Management by Dr. Emmanuel Moore ABOLO
Fundamentals of Market Risk Management by Dr. Emmanuel Moore ABOLO
 
Chapter 3 - Risk Management - 2nd Semester - M.Com - Bangalore University
Chapter 3 - Risk Management - 2nd Semester - M.Com - Bangalore UniversityChapter 3 - Risk Management - 2nd Semester - M.Com - Bangalore University
Chapter 3 - Risk Management - 2nd Semester - M.Com - Bangalore University
 
"Credit Risk-Probabilities Of Default"
"Credit Risk-Probabilities Of Default""Credit Risk-Probabilities Of Default"
"Credit Risk-Probabilities Of Default"
 
Capital adequacy norms
Capital adequacy normsCapital adequacy norms
Capital adequacy norms
 
Toward Credit Portfolio Management
Toward Credit Portfolio Management Toward Credit Portfolio Management
Toward Credit Portfolio Management
 
Chapter 08 risk management in banks
Chapter 08    risk management in banksChapter 08    risk management in banks
Chapter 08 risk management in banks
 
Liquidity Risk Measurement
Liquidity Risk MeasurementLiquidity Risk Measurement
Liquidity Risk Measurement
 
Risk management basel ii
Risk management basel iiRisk management basel ii
Risk management basel ii
 
Credit risk management (2)
Credit risk management (2)Credit risk management (2)
Credit risk management (2)
 
Credit Risk Management Presentation
Credit Risk Management PresentationCredit Risk Management Presentation
Credit Risk Management Presentation
 
Asset Liability Management
Asset Liability ManagementAsset Liability Management
Asset Liability Management
 
Credit risk management presentation
Credit risk management presentationCredit risk management presentation
Credit risk management presentation
 
Interest Rate Risk And Management
Interest Rate Risk And ManagementInterest Rate Risk And Management
Interest Rate Risk And Management
 
Capital adequacy (final)
Capital adequacy (final)Capital adequacy (final)
Capital adequacy (final)
 
Counterparty credit risk. general review
Counterparty credit risk. general reviewCounterparty credit risk. general review
Counterparty credit risk. general review
 
Asset liability management
Asset liability managementAsset liability management
Asset liability management
 
01.2 credit risk factors and measures
01.2   credit risk factors and measures01.2   credit risk factors and measures
01.2 credit risk factors and measures
 
Capital Adequacy
Capital AdequacyCapital Adequacy
Capital Adequacy
 
FRTB CVA Capital Charge
FRTB CVA Capital ChargeFRTB CVA Capital Charge
FRTB CVA Capital Charge
 

En vedette

Altman zscore (Finance)
Altman zscore (Finance)Altman zscore (Finance)
Altman zscore (Finance)Jobin Mathew
 
Credit risk management
Credit risk managementCredit risk management
Credit risk managementUjjwal 'Shanu'
 
Risk management & basel ii
Risk management & basel ii Risk management & basel ii
Risk management & basel ii Amir Razvi
 
Lecture 1 - 23 september 2012
Lecture  1 - 23 september 2012Lecture  1 - 23 september 2012
Lecture 1 - 23 september 2012Nimisha Gupta
 
Introduction to Default
Introduction to DefaultIntroduction to Default
Introduction to DefaultLoanXpress
 
Credit risk management for industrial corporates
Credit risk management for industrial corporatesCredit risk management for industrial corporates
Credit risk management for industrial corporatesMarco Berizzi
 
Onno de vrij (sas) better decision making 12-10
Onno de vrij (sas) better decision making 12-10Onno de vrij (sas) better decision making 12-10
Onno de vrij (sas) better decision making 12-10Wim Assink
 
Flevy.com - Financial Derivatives - Forwards/Futures/Options
Flevy.com - Financial Derivatives - Forwards/Futures/OptionsFlevy.com - Financial Derivatives - Forwards/Futures/Options
Flevy.com - Financial Derivatives - Forwards/Futures/OptionsDavid Tracy
 
Risk management models - Core Consulting
Risk management models - Core ConsultingRisk management models - Core Consulting
Risk management models - Core ConsultingCORE Consulting
 
Introduction of VAR/GVAR Model as a Methodology to Develop Stress Test Scenar...
Introduction of VAR/GVAR Model as a Methodology to Develop Stress Test Scenar...Introduction of VAR/GVAR Model as a Methodology to Develop Stress Test Scenar...
Introduction of VAR/GVAR Model as a Methodology to Develop Stress Test Scenar...基晴 出井
 
Use of R in Actuarial Works
Use of R in Actuarial WorksUse of R in Actuarial Works
Use of R in Actuarial Works基晴 出井
 
Credit+risk+estimation(2)
Credit+risk+estimation(2)Credit+risk+estimation(2)
Credit+risk+estimation(2)Wrik Barman
 
researsh on Altman Zscore
researsh on Altman Zscoreresearsh on Altman Zscore
researsh on Altman ZscoreANRAG TIWARI
 

En vedette (18)

Credit Risk Model Building Steps
Credit Risk Model Building StepsCredit Risk Model Building Steps
Credit Risk Model Building Steps
 
Presentation on credit risk
Presentation on credit risk Presentation on credit risk
Presentation on credit risk
 
Altman zscore (Finance)
Altman zscore (Finance)Altman zscore (Finance)
Altman zscore (Finance)
 
Credit risk management
Credit risk managementCredit risk management
Credit risk management
 
Risk management & basel ii
Risk management & basel ii Risk management & basel ii
Risk management & basel ii
 
Lecture 1 - 23 september 2012
Lecture  1 - 23 september 2012Lecture  1 - 23 september 2012
Lecture 1 - 23 september 2012
 
Introduction to Default
Introduction to DefaultIntroduction to Default
Introduction to Default
 
Credit risk management for industrial corporates
Credit risk management for industrial corporatesCredit risk management for industrial corporates
Credit risk management for industrial corporates
 
7_Credit Derivatives
7_Credit Derivatives7_Credit Derivatives
7_Credit Derivatives
 
L Pch22
L Pch22L Pch22
L Pch22
 
Onno de vrij (sas) better decision making 12-10
Onno de vrij (sas) better decision making 12-10Onno de vrij (sas) better decision making 12-10
Onno de vrij (sas) better decision making 12-10
 
Stress Testing
Stress TestingStress Testing
Stress Testing
 
Flevy.com - Financial Derivatives - Forwards/Futures/Options
Flevy.com - Financial Derivatives - Forwards/Futures/OptionsFlevy.com - Financial Derivatives - Forwards/Futures/Options
Flevy.com - Financial Derivatives - Forwards/Futures/Options
 
Risk management models - Core Consulting
Risk management models - Core ConsultingRisk management models - Core Consulting
Risk management models - Core Consulting
 
Introduction of VAR/GVAR Model as a Methodology to Develop Stress Test Scenar...
Introduction of VAR/GVAR Model as a Methodology to Develop Stress Test Scenar...Introduction of VAR/GVAR Model as a Methodology to Develop Stress Test Scenar...
Introduction of VAR/GVAR Model as a Methodology to Develop Stress Test Scenar...
 
Use of R in Actuarial Works
Use of R in Actuarial WorksUse of R in Actuarial Works
Use of R in Actuarial Works
 
Credit+risk+estimation(2)
Credit+risk+estimation(2)Credit+risk+estimation(2)
Credit+risk+estimation(2)
 
researsh on Altman Zscore
researsh on Altman Zscoreresearsh on Altman Zscore
researsh on Altman Zscore
 

Similaire à Credit risk models

International journal of engineering and mathematical modelling vol1 no1_2015_2
International journal of engineering and mathematical modelling vol1 no1_2015_2International journal of engineering and mathematical modelling vol1 no1_2015_2
International journal of engineering and mathematical modelling vol1 no1_2015_2IJEMM
 
Collateral Re-Hypothecation
Collateral Re-HypothecationCollateral Re-Hypothecation
Collateral Re-HypothecationEric Tham
 
Jacobs Mdl Rsk Par Crdt Der Risk Nov2011 V17 11 7 11
Jacobs Mdl Rsk Par Crdt Der Risk Nov2011 V17 11 7 11Jacobs Mdl Rsk Par Crdt Der Risk Nov2011 V17 11 7 11
Jacobs Mdl Rsk Par Crdt Der Risk Nov2011 V17 11 7 11Michael Jacobs, Jr.
 
Modelling Credit Risk of Portfolios of Consumer Loans
Modelling Credit Risk of Portfolios of Consumer LoansModelling Credit Risk of Portfolios of Consumer Loans
Modelling Credit Risk of Portfolios of Consumer LoansMadhur Malik
 
Scenario generation and stochastic programming models for asset liabiltiy man...
Scenario generation and stochastic programming models for asset liabiltiy man...Scenario generation and stochastic programming models for asset liabiltiy man...
Scenario generation and stochastic programming models for asset liabiltiy man...Nicha Tatsaneeyapan
 
Ifrs9 ntu mfe2000-ews-credit-deterioration
Ifrs9 ntu mfe2000-ews-credit-deteriorationIfrs9 ntu mfe2000-ews-credit-deterioration
Ifrs9 ntu mfe2000-ews-credit-deteriorationGuan Khoo
 
Using Cross Asset Information To Improve Portfolio Risk Estimation
Using Cross Asset Information To Improve Portfolio Risk EstimationUsing Cross Asset Information To Improve Portfolio Risk Estimation
Using Cross Asset Information To Improve Portfolio Risk Estimationyamanote
 
Using a Survival Model for Credit Risk Scoring and Loan Pricing Instead of XG...
Using a Survival Model for Credit Risk Scoring and Loan Pricing Instead of XG...Using a Survival Model for Credit Risk Scoring and Loan Pricing Instead of XG...
Using a Survival Model for Credit Risk Scoring and Loan Pricing Instead of XG...CFO Pro+Analytics
 
Federico Thibaud - Capital Structure Arbitrage
Federico Thibaud - Capital Structure ArbitrageFederico Thibaud - Capital Structure Arbitrage
Federico Thibaud - Capital Structure ArbitrageFederico Thibaud
 
Transition matrix models of consumer credit ratings
Transition matrix models of consumer credit ratingsTransition matrix models of consumer credit ratings
Transition matrix models of consumer credit ratingsMadhur Malik
 
2013 CFS Modeling Competition
2013 CFS Modeling Competition2013 CFS Modeling Competition
2013 CFS Modeling CompetitionDongyang Wang
 
CFS modeling competition
CFS modeling competition CFS modeling competition
CFS modeling competition Jingli Li
 
Fincad hedge-accounting
Fincad hedge-accountingFincad hedge-accounting
Fincad hedge-accountingssuser689d57
 
Default Forecasting in KMV.pdf
Default Forecasting in KMV.pdfDefault Forecasting in KMV.pdf
Default Forecasting in KMV.pdfHuanPhamQuoc2
 
Tracking Variation in Systemic Risk-2 8-3
Tracking Variation in Systemic Risk-2 8-3Tracking Variation in Systemic Risk-2 8-3
Tracking Variation in Systemic Risk-2 8-3edward kane
 

Similaire à Credit risk models (20)

International journal of engineering and mathematical modelling vol1 no1_2015_2
International journal of engineering and mathematical modelling vol1 no1_2015_2International journal of engineering and mathematical modelling vol1 no1_2015_2
International journal of engineering and mathematical modelling vol1 no1_2015_2
 
Collateral Re-Hypothecation
Collateral Re-HypothecationCollateral Re-Hypothecation
Collateral Re-Hypothecation
 
Research on Credit Default Swaps Pricing Under Uncertainty in the Distributio...
Research on Credit Default Swaps Pricing Under Uncertainty in the Distributio...Research on Credit Default Swaps Pricing Under Uncertainty in the Distributio...
Research on Credit Default Swaps Pricing Under Uncertainty in the Distributio...
 
Jacobs Mdl Rsk Par Crdt Der Risk Nov2011 V17 11 7 11
Jacobs Mdl Rsk Par Crdt Der Risk Nov2011 V17 11 7 11Jacobs Mdl Rsk Par Crdt Der Risk Nov2011 V17 11 7 11
Jacobs Mdl Rsk Par Crdt Der Risk Nov2011 V17 11 7 11
 
Lgd Model Jacobs 10 10 V2[1]
Lgd Model Jacobs 10 10 V2[1]Lgd Model Jacobs 10 10 V2[1]
Lgd Model Jacobs 10 10 V2[1]
 
Reinsurance Counterparty Credit Risk and Optimal Regulatory Capital under Dis...
Reinsurance Counterparty Credit Risk and Optimal Regulatory Capital under Dis...Reinsurance Counterparty Credit Risk and Optimal Regulatory Capital under Dis...
Reinsurance Counterparty Credit Risk and Optimal Regulatory Capital under Dis...
 
Modelling Credit Risk of Portfolios of Consumer Loans
Modelling Credit Risk of Portfolios of Consumer LoansModelling Credit Risk of Portfolios of Consumer Loans
Modelling Credit Risk of Portfolios of Consumer Loans
 
Scenario generation and stochastic programming models for asset liabiltiy man...
Scenario generation and stochastic programming models for asset liabiltiy man...Scenario generation and stochastic programming models for asset liabiltiy man...
Scenario generation and stochastic programming models for asset liabiltiy man...
 
Ifrs9 ntu mfe2000-ews-credit-deterioration
Ifrs9 ntu mfe2000-ews-credit-deteriorationIfrs9 ntu mfe2000-ews-credit-deterioration
Ifrs9 ntu mfe2000-ews-credit-deterioration
 
Using Cross Asset Information To Improve Portfolio Risk Estimation
Using Cross Asset Information To Improve Portfolio Risk EstimationUsing Cross Asset Information To Improve Portfolio Risk Estimation
Using Cross Asset Information To Improve Portfolio Risk Estimation
 
Using a Survival Model for Credit Risk Scoring and Loan Pricing Instead of XG...
Using a Survival Model for Credit Risk Scoring and Loan Pricing Instead of XG...Using a Survival Model for Credit Risk Scoring and Loan Pricing Instead of XG...
Using a Survival Model for Credit Risk Scoring and Loan Pricing Instead of XG...
 
Federico Thibaud - Capital Structure Arbitrage
Federico Thibaud - Capital Structure ArbitrageFederico Thibaud - Capital Structure Arbitrage
Federico Thibaud - Capital Structure Arbitrage
 
Transition matrix models of consumer credit ratings
Transition matrix models of consumer credit ratingsTransition matrix models of consumer credit ratings
Transition matrix models of consumer credit ratings
 
2013 CFS Modeling Competition
2013 CFS Modeling Competition2013 CFS Modeling Competition
2013 CFS Modeling Competition
 
CFS modeling competition
CFS modeling competition CFS modeling competition
CFS modeling competition
 
credit_risk.pdf
credit_risk.pdfcredit_risk.pdf
credit_risk.pdf
 
Fincad hedge-accounting
Fincad hedge-accountingFincad hedge-accounting
Fincad hedge-accounting
 
3. .xiaofei li 1
3. .xiaofei li 13. .xiaofei li 1
3. .xiaofei li 1
 
Default Forecasting in KMV.pdf
Default Forecasting in KMV.pdfDefault Forecasting in KMV.pdf
Default Forecasting in KMV.pdf
 
Tracking Variation in Systemic Risk-2 8-3
Tracking Variation in Systemic Risk-2 8-3Tracking Variation in Systemic Risk-2 8-3
Tracking Variation in Systemic Risk-2 8-3
 

Plus de Ujjwal 'Shanu' (20)

Risk return trade off
Risk return trade offRisk return trade off
Risk return trade off
 
Risk perceprtion of ads of infosys
Risk perceprtion of ads of infosysRisk perceprtion of ads of infosys
Risk perceprtion of ads of infosys
 
Mutual funds
Mutual fundsMutual funds
Mutual funds
 
Corporate governance
Corporate governanceCorporate governance
Corporate governance
 
Whistle blower final
Whistle blower finalWhistle blower final
Whistle blower final
 
M&a
M&aM&a
M&a
 
T test
T testT test
T test
 
Research design
Research designResearch design
Research design
 
Business research
Business researchBusiness research
Business research
 
Measurement and scaling techniques
Measurement  and  scaling  techniquesMeasurement  and  scaling  techniques
Measurement and scaling techniques
 
Taxation
TaxationTaxation
Taxation
 
Shri palaniappan chidambram
Shri palaniappan chidambramShri palaniappan chidambram
Shri palaniappan chidambram
 
Planning
PlanningPlanning
Planning
 
Merger & acquisition
Merger & acquisitionMerger & acquisition
Merger & acquisition
 
Gaar ppt
Gaar pptGaar ppt
Gaar ppt
 
Rbi
RbiRbi
Rbi
 
Production theory
Production theoryProduction theory
Production theory
 
Perfect competition
Perfect competitionPerfect competition
Perfect competition
 
Oligopoly
OligopolyOligopoly
Oligopoly
 
National income
National incomeNational income
National income
 

Dernier

VIP Call Girl Service Andheri West ⚡ 9920725232 What It Takes To Be The Best ...
VIP Call Girl Service Andheri West ⚡ 9920725232 What It Takes To Be The Best ...VIP Call Girl Service Andheri West ⚡ 9920725232 What It Takes To Be The Best ...
VIP Call Girl Service Andheri West ⚡ 9920725232 What It Takes To Be The Best ...dipikadinghjn ( Why You Choose Us? ) Escorts
 
Booking open Available Pune Call Girls Wadgaon Sheri 6297143586 Call Hot Ind...
Booking open Available Pune Call Girls Wadgaon Sheri  6297143586 Call Hot Ind...Booking open Available Pune Call Girls Wadgaon Sheri  6297143586 Call Hot Ind...
Booking open Available Pune Call Girls Wadgaon Sheri 6297143586 Call Hot Ind...Call Girls in Nagpur High Profile
 
(Sexy Sheela) Call Girl Mumbai Call Now 👉9920725232👈 Mumbai Escorts 24x7
(Sexy Sheela) Call Girl Mumbai Call Now 👉9920725232👈 Mumbai Escorts 24x7(Sexy Sheela) Call Girl Mumbai Call Now 👉9920725232👈 Mumbai Escorts 24x7
(Sexy Sheela) Call Girl Mumbai Call Now 👉9920725232👈 Mumbai Escorts 24x7jayawati511
 
VIP Independent Call Girls in Taloja 🌹 9920725232 ( Call Me ) Mumbai Escorts ...
VIP Independent Call Girls in Taloja 🌹 9920725232 ( Call Me ) Mumbai Escorts ...VIP Independent Call Girls in Taloja 🌹 9920725232 ( Call Me ) Mumbai Escorts ...
VIP Independent Call Girls in Taloja 🌹 9920725232 ( Call Me ) Mumbai Escorts ...dipikadinghjn ( Why You Choose Us? ) Escorts
 
Kopar Khairane Russian Call Girls Number-9833754194-Navi Mumbai Fantastic Unl...
Kopar Khairane Russian Call Girls Number-9833754194-Navi Mumbai Fantastic Unl...Kopar Khairane Russian Call Girls Number-9833754194-Navi Mumbai Fantastic Unl...
Kopar Khairane Russian Call Girls Number-9833754194-Navi Mumbai Fantastic Unl...priyasharma62062
 
Business Principles, Tools, and Techniques in Participating in Various Types...
Business Principles, Tools, and Techniques  in Participating in Various Types...Business Principles, Tools, and Techniques  in Participating in Various Types...
Business Principles, Tools, and Techniques in Participating in Various Types...jeffreytingson
 
VIP Independent Call Girls in Andheri 🌹 9920725232 ( Call Me ) Mumbai Escorts...
VIP Independent Call Girls in Andheri 🌹 9920725232 ( Call Me ) Mumbai Escorts...VIP Independent Call Girls in Andheri 🌹 9920725232 ( Call Me ) Mumbai Escorts...
VIP Independent Call Girls in Andheri 🌹 9920725232 ( Call Me ) Mumbai Escorts...dipikadinghjn ( Why You Choose Us? ) Escorts
 
Top Rated Pune Call Girls Shikrapur ⟟ 6297143586 ⟟ Call Me For Genuine Sex S...
Top Rated  Pune Call Girls Shikrapur ⟟ 6297143586 ⟟ Call Me For Genuine Sex S...Top Rated  Pune Call Girls Shikrapur ⟟ 6297143586 ⟟ Call Me For Genuine Sex S...
Top Rated Pune Call Girls Shikrapur ⟟ 6297143586 ⟟ Call Me For Genuine Sex S...Call Girls in Nagpur High Profile
 
Vasai-Virar Fantastic Call Girls-9833754194-Call Girls MUmbai
Vasai-Virar Fantastic Call Girls-9833754194-Call Girls MUmbaiVasai-Virar Fantastic Call Girls-9833754194-Call Girls MUmbai
Vasai-Virar Fantastic Call Girls-9833754194-Call Girls MUmbaipriyasharma62062
 
Webinar on E-Invoicing for Fintech Belgium
Webinar on E-Invoicing for Fintech BelgiumWebinar on E-Invoicing for Fintech Belgium
Webinar on E-Invoicing for Fintech BelgiumFinTech Belgium
 
VIP Kalyan Call Girls 🌐 9920725232 🌐 Make Your Dreams Come True With Mumbai E...
VIP Kalyan Call Girls 🌐 9920725232 🌐 Make Your Dreams Come True With Mumbai E...VIP Kalyan Call Girls 🌐 9920725232 🌐 Make Your Dreams Come True With Mumbai E...
VIP Kalyan Call Girls 🌐 9920725232 🌐 Make Your Dreams Come True With Mumbai E...roshnidevijkn ( Why You Choose Us? ) Escorts
 
Call Girls Service Pune ₹7.5k Pick Up & Drop With Cash Payment 9352852248 Cal...
Call Girls Service Pune ₹7.5k Pick Up & Drop With Cash Payment 9352852248 Cal...Call Girls Service Pune ₹7.5k Pick Up & Drop With Cash Payment 9352852248 Cal...
Call Girls Service Pune ₹7.5k Pick Up & Drop With Cash Payment 9352852248 Cal...roshnidevijkn ( Why You Choose Us? ) Escorts
 
Stock Market Brief Deck (Under Pressure).pdf
Stock Market Brief Deck (Under Pressure).pdfStock Market Brief Deck (Under Pressure).pdf
Stock Market Brief Deck (Under Pressure).pdfMichael Silva
 
falcon-invoice-discounting-unlocking-prime-investment-opportunities
falcon-invoice-discounting-unlocking-prime-investment-opportunitiesfalcon-invoice-discounting-unlocking-prime-investment-opportunities
falcon-invoice-discounting-unlocking-prime-investment-opportunitiesFalcon Invoice Discounting
 
Vip Call US 📞 7738631006 ✅Call Girls In Sakinaka ( Mumbai )
Vip Call US 📞 7738631006 ✅Call Girls In Sakinaka ( Mumbai )Vip Call US 📞 7738631006 ✅Call Girls In Sakinaka ( Mumbai )
Vip Call US 📞 7738631006 ✅Call Girls In Sakinaka ( Mumbai )Pooja Nehwal
 
Top Rated Pune Call Girls Sinhagad Road ⟟ 6297143586 ⟟ Call Me For Genuine S...
Top Rated  Pune Call Girls Sinhagad Road ⟟ 6297143586 ⟟ Call Me For Genuine S...Top Rated  Pune Call Girls Sinhagad Road ⟟ 6297143586 ⟟ Call Me For Genuine S...
Top Rated Pune Call Girls Sinhagad Road ⟟ 6297143586 ⟟ Call Me For Genuine S...Call Girls in Nagpur High Profile
 
VIP Independent Call Girls in Mumbai 🌹 9920725232 ( Call Me ) Mumbai Escorts ...
VIP Independent Call Girls in Mumbai 🌹 9920725232 ( Call Me ) Mumbai Escorts ...VIP Independent Call Girls in Mumbai 🌹 9920725232 ( Call Me ) Mumbai Escorts ...
VIP Independent Call Girls in Mumbai 🌹 9920725232 ( Call Me ) Mumbai Escorts ...dipikadinghjn ( Why You Choose Us? ) Escorts
 
Navi Mumbai Cooperetive Housewife Call Girls-9833754194-Natural Panvel Enjoye...
Navi Mumbai Cooperetive Housewife Call Girls-9833754194-Natural Panvel Enjoye...Navi Mumbai Cooperetive Housewife Call Girls-9833754194-Natural Panvel Enjoye...
Navi Mumbai Cooperetive Housewife Call Girls-9833754194-Natural Panvel Enjoye...priyasharma62062
 

Dernier (20)

VIP Call Girl Service Andheri West ⚡ 9920725232 What It Takes To Be The Best ...
VIP Call Girl Service Andheri West ⚡ 9920725232 What It Takes To Be The Best ...VIP Call Girl Service Andheri West ⚡ 9920725232 What It Takes To Be The Best ...
VIP Call Girl Service Andheri West ⚡ 9920725232 What It Takes To Be The Best ...
 
Booking open Available Pune Call Girls Wadgaon Sheri 6297143586 Call Hot Ind...
Booking open Available Pune Call Girls Wadgaon Sheri  6297143586 Call Hot Ind...Booking open Available Pune Call Girls Wadgaon Sheri  6297143586 Call Hot Ind...
Booking open Available Pune Call Girls Wadgaon Sheri 6297143586 Call Hot Ind...
 
(Sexy Sheela) Call Girl Mumbai Call Now 👉9920725232👈 Mumbai Escorts 24x7
(Sexy Sheela) Call Girl Mumbai Call Now 👉9920725232👈 Mumbai Escorts 24x7(Sexy Sheela) Call Girl Mumbai Call Now 👉9920725232👈 Mumbai Escorts 24x7
(Sexy Sheela) Call Girl Mumbai Call Now 👉9920725232👈 Mumbai Escorts 24x7
 
VIP Independent Call Girls in Taloja 🌹 9920725232 ( Call Me ) Mumbai Escorts ...
VIP Independent Call Girls in Taloja 🌹 9920725232 ( Call Me ) Mumbai Escorts ...VIP Independent Call Girls in Taloja 🌹 9920725232 ( Call Me ) Mumbai Escorts ...
VIP Independent Call Girls in Taloja 🌹 9920725232 ( Call Me ) Mumbai Escorts ...
 
Kopar Khairane Russian Call Girls Number-9833754194-Navi Mumbai Fantastic Unl...
Kopar Khairane Russian Call Girls Number-9833754194-Navi Mumbai Fantastic Unl...Kopar Khairane Russian Call Girls Number-9833754194-Navi Mumbai Fantastic Unl...
Kopar Khairane Russian Call Girls Number-9833754194-Navi Mumbai Fantastic Unl...
 
Business Principles, Tools, and Techniques in Participating in Various Types...
Business Principles, Tools, and Techniques  in Participating in Various Types...Business Principles, Tools, and Techniques  in Participating in Various Types...
Business Principles, Tools, and Techniques in Participating in Various Types...
 
VIP Independent Call Girls in Andheri 🌹 9920725232 ( Call Me ) Mumbai Escorts...
VIP Independent Call Girls in Andheri 🌹 9920725232 ( Call Me ) Mumbai Escorts...VIP Independent Call Girls in Andheri 🌹 9920725232 ( Call Me ) Mumbai Escorts...
VIP Independent Call Girls in Andheri 🌹 9920725232 ( Call Me ) Mumbai Escorts...
 
Top Rated Pune Call Girls Shikrapur ⟟ 6297143586 ⟟ Call Me For Genuine Sex S...
Top Rated  Pune Call Girls Shikrapur ⟟ 6297143586 ⟟ Call Me For Genuine Sex S...Top Rated  Pune Call Girls Shikrapur ⟟ 6297143586 ⟟ Call Me For Genuine Sex S...
Top Rated Pune Call Girls Shikrapur ⟟ 6297143586 ⟟ Call Me For Genuine Sex S...
 
Vasai-Virar Fantastic Call Girls-9833754194-Call Girls MUmbai
Vasai-Virar Fantastic Call Girls-9833754194-Call Girls MUmbaiVasai-Virar Fantastic Call Girls-9833754194-Call Girls MUmbai
Vasai-Virar Fantastic Call Girls-9833754194-Call Girls MUmbai
 
(INDIRA) Call Girl Mumbai Call Now 8250077686 Mumbai Escorts 24x7
(INDIRA) Call Girl Mumbai Call Now 8250077686 Mumbai Escorts 24x7(INDIRA) Call Girl Mumbai Call Now 8250077686 Mumbai Escorts 24x7
(INDIRA) Call Girl Mumbai Call Now 8250077686 Mumbai Escorts 24x7
 
Webinar on E-Invoicing for Fintech Belgium
Webinar on E-Invoicing for Fintech BelgiumWebinar on E-Invoicing for Fintech Belgium
Webinar on E-Invoicing for Fintech Belgium
 
VIP Kalyan Call Girls 🌐 9920725232 🌐 Make Your Dreams Come True With Mumbai E...
VIP Kalyan Call Girls 🌐 9920725232 🌐 Make Your Dreams Come True With Mumbai E...VIP Kalyan Call Girls 🌐 9920725232 🌐 Make Your Dreams Come True With Mumbai E...
VIP Kalyan Call Girls 🌐 9920725232 🌐 Make Your Dreams Come True With Mumbai E...
 
Call Girls Service Pune ₹7.5k Pick Up & Drop With Cash Payment 9352852248 Cal...
Call Girls Service Pune ₹7.5k Pick Up & Drop With Cash Payment 9352852248 Cal...Call Girls Service Pune ₹7.5k Pick Up & Drop With Cash Payment 9352852248 Cal...
Call Girls Service Pune ₹7.5k Pick Up & Drop With Cash Payment 9352852248 Cal...
 
Stock Market Brief Deck (Under Pressure).pdf
Stock Market Brief Deck (Under Pressure).pdfStock Market Brief Deck (Under Pressure).pdf
Stock Market Brief Deck (Under Pressure).pdf
 
falcon-invoice-discounting-unlocking-prime-investment-opportunities
falcon-invoice-discounting-unlocking-prime-investment-opportunitiesfalcon-invoice-discounting-unlocking-prime-investment-opportunities
falcon-invoice-discounting-unlocking-prime-investment-opportunities
 
Vip Call US 📞 7738631006 ✅Call Girls In Sakinaka ( Mumbai )
Vip Call US 📞 7738631006 ✅Call Girls In Sakinaka ( Mumbai )Vip Call US 📞 7738631006 ✅Call Girls In Sakinaka ( Mumbai )
Vip Call US 📞 7738631006 ✅Call Girls In Sakinaka ( Mumbai )
 
Top Rated Pune Call Girls Sinhagad Road ⟟ 6297143586 ⟟ Call Me For Genuine S...
Top Rated  Pune Call Girls Sinhagad Road ⟟ 6297143586 ⟟ Call Me For Genuine S...Top Rated  Pune Call Girls Sinhagad Road ⟟ 6297143586 ⟟ Call Me For Genuine S...
Top Rated Pune Call Girls Sinhagad Road ⟟ 6297143586 ⟟ Call Me For Genuine S...
 
VIP Independent Call Girls in Mumbai 🌹 9920725232 ( Call Me ) Mumbai Escorts ...
VIP Independent Call Girls in Mumbai 🌹 9920725232 ( Call Me ) Mumbai Escorts ...VIP Independent Call Girls in Mumbai 🌹 9920725232 ( Call Me ) Mumbai Escorts ...
VIP Independent Call Girls in Mumbai 🌹 9920725232 ( Call Me ) Mumbai Escorts ...
 
Navi Mumbai Cooperetive Housewife Call Girls-9833754194-Natural Panvel Enjoye...
Navi Mumbai Cooperetive Housewife Call Girls-9833754194-Natural Panvel Enjoye...Navi Mumbai Cooperetive Housewife Call Girls-9833754194-Natural Panvel Enjoye...
Navi Mumbai Cooperetive Housewife Call Girls-9833754194-Natural Panvel Enjoye...
 
Call Girls in New Ashok Nagar, (delhi) call me [9953056974] escort service 24X7
Call Girls in New Ashok Nagar, (delhi) call me [9953056974] escort service 24X7Call Girls in New Ashok Nagar, (delhi) call me [9953056974] escort service 24X7
Call Girls in New Ashok Nagar, (delhi) call me [9953056974] escort service 24X7
 

Credit risk models

  • 1. Credit Risk Models Question: What is an appropriate modeling approach to value defaultable debt (bonds and loans)?
  • 2. Gieseke “Credit Risk Modeling and Valuation: An Introduction,” October 2004
  • 3. Three main approaches to modeling credit risk in the finance literature • • • Structural approach: Assumptions are made about the dynamics of a firm’s assets, its capital structure, and its debt and share holders. A firm defaults if the assets are insufficient according to some measure. A liability is characterized as an option on the firm’s assets. Reduced form approach: No assumptions are made concerning why a default occurs. Rather, the dynamics of default are exogenously given by the default rate (or intensity). Prices of credit sensitive securities can be calculated as if they were default free using the risk free rate adjusted by the level of intensity. Incomplete information approach: Combines the structural and reduced form approaches.
  • 4. Structural approach: default in the classical Merton model (1974).
  • 5. 1. We want to use the structural approach to incorporate bond default risk in bond valuation The value of the firm’s assets are assumed to follow the process, where μ is the instantaneous expected rate of return on assets, and σ is the standard deviation of the return on assets. Let D(t,T) be the date t market value of debt with promised payment B at date t. The second line in (18.2) says that the payoff to the creditors equals the promised payment (B) minus the payoff on a European put option written on the firm’s assets with exercise price B.
  • 6. Market value of firm debt, D(t) Let P(t,T) represent the current date t price of a default-free, zerocoupon bond that pays $1 at date T, where the bond conforms with the Vasicek model in Ch. 9. Pennacchi asserts that using results for pricing options (Ch. 9.3) when interest rates are random (as in 9.58), we can write
  • 7. Market value of firm equity, E(t) Shareholder equity is similar to a call option on the firm’s assets, since at maturity the payoff to equity holders is max [A(t) – B, 0]. However, shareholder equity is different from a European option if the firm pay dividends to shareholders prior to maturity as reflected in the first term of the last line in (18.4) where δ denotes the dividend rate.
  • 8. Critique of the Merton model The Merton model assumption is that the firm has a single issue of zero-coupon debt. That is unrealistic. Modeling multiple issues with different maturities and seniorities complicates default. In response some models have suggested that default occurs when the firm’s assets hit a lower boundary. That boundary has a monotonic relation to the firm’s total outstanding debt. The first passage time is when the value of the firm’s assets crosses through the lower boundary. First passage model - - bond indenture provisions often include safety covenants that give bond holders the right to reorganize the firm if the value falls below a given barrier. The first passage model defines the survival probability as p(t,T) that the distance to default does not reach zero at any date τ between t and T. The distance to default is often measured in terms of standard deviations.
  • 9. Structural approach: default in the first passage model (Black & Cox, 1976)
  • 10. 2. We want to use the reduced form approach to incorporate bond default risk in bond valuation The reduced form model was developed to overcome the nontradeability and nonobservability of the firm’s asset value process (Jarrow &Turnbull, 1992). Default is not tied to the dynamics of asset prices and this breaks the link between the firm’s balance sheet and the likelihood of default. Rather, default is based on an exogenous Poisson process, so it may be better able to capture the effects of default due to additional unobserved factors. Reduced form models can also be used to value defaultable bonds using the techniques used for default-free bonds. In the reduced form framework, we assume that the default event depends on a “reduced form process,” that may depend on the firm’s assets and capital structure, but also on other macroeconomic factors that influence default.
  • 11. The default event for a firm’s bond is modeled as a Poisson process with a time-varying “default intensity.” Conditional on no default occurring up to date t, the instantaneous probability of default at (t, t+dt) is denoted as λ(t) dt, where λ(t) is the physical default intensity, or “hazard rate,” where it is assumed that λ(t) ≥ 0. Since λ(t) is time-varying, it may be linked to changes in underlying state variables. We can compute the physical probability that a bond will not default from date t to date τ where t ≤ τ ≤ T. This physical survival probability is written
  • 12. Zero recovery bond (bond holders receive nothing in the event of a default) With zero recovery the bondholder payoff at date T is either D(T,T) = B if no default occurs, or D(T,T) = 0 if default occurred during (t,T). If we apply risk-neutral pricing, the date t value of a zero-recovery bond can be written where r is the instantaneous “default-free” interest rate, which gives us the risk-neutral default intensity rather than the physical default intensity in (18.5). The risk-neutral default intensity accounts for the market price of risk due to the Poisson arrival of the default event.
  • 13. Value of the zero-recovery defaultable bond Using the calculated survival probability in (18.5) we get So, (18.9) indicates that valuing a zero-recovery defaultable bond is similar to valuing a default-free bond, except that we use the discount rate, r(u) + λ(t), rather than just r(u).
  • 15.
  • 16.
  • 17.
  • 18.
  • 20. Default depends on both Brownian motion vector (dz) for the state variables and the Poisson process (dq) for arrival of default) - - the default process is “doubly stochastic”
  • 21. 3. We want to extend the structural and reduced form models for bonds to the case of bank loans The link between loans and optionality can be illustrated by a payoff function to a bank lender. Here repayment of the loan requires amount 0B. But the market value of project assets can be AL or AH. At AL the borrow would have an incentive to default on the loan contract by forfeiting the assets to the bank. Above 0B the bank earns a fixed return on the loan. This is analogous to the payoff to a put option writer on a stock with exercise price B.
  • 22. Structural model (KMV) The value of a put option on a stock can be written as, F(S, X, r, σ, T) The value of a default option on a loan can be written as, G(A, B, r, σA, T) where A is the value of the firm’s assets and B is the repayment at maturity. We note that the values for A and σA are not directly observable. The KMV Credit Monitor Model turns the bank’s lending problem around and considers it from the perspective of the borrower. To solve for the two unknowns, A and σA , the model uses • • the structural relationship between market value of equity and market value of assets, and the relationship between volatility of assets and volatility of equity.
  • 23. Loan repayment from the perspective of the borrower (equity holder) The payoff function of the equity holder is a call option on the assets of the firm, H(A, σA, r, B, T). KMV solves the unobservables problem by assuming that σE = g(σA) where σE is the observable volatility of firm equity and with two equations in two unknowns, we can solve for A and σA . Once these values are derived, KMV calculates the expected default frequency (EDF).
  • 24. Calculating the theoretical EDF If A = 100, σA = 10, and B = 80, the distance to default = (A-B)/σA = 2 standard deviations. The value of assets would have to decline by 2 standard deviations in order to enter default.
  • 25. Based on a sufficiently large sample of firms, we can map the distance to default into EDF
  • 26. Critique of the KMV model It is difficult to construct the theoretical EDF curves without the assumption of normality of asset returns Private firm EDFs can only be constructed by using accounting data and other observable characteristics of the borrower The KMV approach does not distinguish between different types of debt (bonds that vary by seniority, collateral, covenants, convertibility, etc.) The KMV model is static - - once the debt is in place the firm does not change it. The default behavior of firms that manage their leverage positions is not captured.
  • 27. Reduced-form model (CreditRisk+) The Credit Risk+ model is based on an insurance approach where default is an event that resembles other insurable events (casualty losses, death, injury, etc.). These are generally referred to as mortality models which involve actuarial estimate of the events occurring. • Default is modeled as a continuous variable with an underlying probability distribution. • Default uncertainty is one type of uncertainty, there is also uncertainty surrounding the size or severity of the loss. • Loss severities are distributed into “bands,” and the number of bands is adjusted to get greater accuracy in the estimation. • The frequency of losses and the severity of losses produce a distribution of losses for each band. Summing across these bands we construct the loss distribution for a portfolio of loans.
  • 28. Constructing the loss distribution in the CreditRisk+ model Using the formula for the Poisson distribution
  • 29. The calculated probability of default in band 1
  • 30. The distribution of defaults in band 1
  • 31. The loss distribution for a single loan portfolio (severity rate = $20,000 per $100,000 of loan)
  • 32. Critique of the CreditRisk+ model The observed distribution of losses may have a larger variance than the model shows. This would tend to underestimate the true economic capital requirement. • This may be due to an assumption that the mean default rate is constant within each band. So, increase the number of bands for more accuracy. • Default rates across bands may be correlated due to underlying state variables that have broader impact on borrowers. • The predictive usefulness of the approach depends on the size of the sample of loans. • The model is not a “full VaR model” because it concentrates on loss rates, not on loan value changes. It is a default model, not a markto-market model.