Interagency Supervisory Guidance on Counterparty Credit Risk - FDIC
Counterparty Credit risk measurement:Rules and Estimation methods
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Master’s Degree in risk management and quantitative finance Pisa, 16 th December 2011 VIII° Edition Counterparty Credit risk measurement: Rules and Estimation methods. Pierpaolo Cassese Prof. Franca Orsi University of Pisa (Academic Tutor)
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counterparty credit risk under new Regulatory Framework Merton\’s Model KMV-Moody\’s MOdel
Transcript of Counterparty Credit risk measurement:Rules and Estimation methods
Master’s Degree in risk management and quantitative financePisa, 16th December 2011 VIII° Edition
Counterparty Credit risk measurement:Rules and Estimation methods.
Pierpaolo Cassese
Prof. Franca Orsi University of Pisa(Academic Tutor)
Section 1 Counterparty Credit Risk: Regulatory aspects 3
Section 2 Counterparty credit risk evaluation : internal models
6
Section 3 Stochastical models for computing the Probability of Default
10
Section 4 Results 14
Section 5 Concluding remarks 19
La misurazione del rischio di controparte : Regole e metodi di stimaTable of contents
2
Section 1
Counterparty Credit Risk: regulatory aspects
Derivatives and credit Assets traded on the OTC Markets
Security Financing
Transactions
Long Term financial
operations
Counterparty risk, as defined by Basel Committee is "the risk that the counterparty who joined in a transaction involving certain financial instruments could default before the settlement of the transaction" (Role 263/2006 of Bank of Italy)
Counterparty Credit Risk: regulatory aspects
3
Basel 2 Regulatory Framework
Credit Risk Market Risk Operational Risk Counterparty Risk
Bilateral Risk
Counterparty Credit Risk: regulatory aspects
4
Section 2
Counterparty credit risk evaluation : internal models
EPE Model
Applicable with
permission of Bank of Italy
Not depending from model to compute
the RP
It adopts Monte Carlo Multi-Step Simulation
Model
Counterparty Credit Risk evaluation: Internal Models
5
BCBS proposes a rank for these risk measures as follows:
Positive Future Exposure: maximum exposure to positive
values extracted from the distribution of future scenarios at
the confidence level of 95%. Expected Positive Exposure: is the weighted average
exposures for the remainder of the financial contract.
Expected Exposure: mean of Positive Exposures Effective EPE: exposure weighted average estimated
effectively. Effective EE: Represents the maximum of the EE period (t-1)
and the EE at time t
Counterparty Credit Risk evaluation: internal modelsMain measures of counterparty credit risk
6
How to build the profile of the exposure at risk?
To identify risk and market factors.
To create n scenarios for each time-point .
Extracting the 95th percentile of the distribution of the maximum positive value of n scenarios.
Revaluation at Mark to Market of the values extracted by the ditribution.
Aggregation of maximum values extracted for each time-point in order to build the Exposure Profile.
Counterparty Credit Risk evaluation: internal models
7
Section 3
Stochastical models for computing PD
Merton Model (1974)
Stochastic Diffusion Model B&S option theory
KMV-Moody’s Model
Historical Balance Sheet Quali-quantitative evaluation
Stochastical models for the compute of the Probability of Default
8
It needs to estimate the asset value using a stochastic diffusion process.
It quantifies the liability values beyond which the Company will be in Default
It calculates the Distance to Default considering the number of standard deviation through the B&S-Merton’s Model
It associates the value of probability of Default on one year horizon at the N(-d2) value gained by Merton’s Model.
It compares the value of PD with the value declared by the rating agencies in order to assign a credit judgement.
How to compute the Default Probability?
Merton Model
9
Time Series of balancesheet
Composition of Liabilities
Determining asset and volatility value
Estimate of D-to-D
KMV-Moody’s Model
10
Section 4
Results
Application of Internal Model EPE.
Quantification of capital requirements for covering the counterparty credit risk.
Application of Merton model for judging the PD.
Assignment of credit rating to the counterparty.
Results
11
Results: Mortgage loans
12
Notional Amount 1 Mln €Expiry Date 60 MonthsInterest Rate 4.50%Amortization Constant paymentCollateral 40%
Mortgage Loans
Notional Amount 1 Mln €Expiry Date 60 MonthsInterest Rate 4.50%Amortization Constant payment
Mortgage Loans
MERTON MODEL PARAMETERS
TRANSICTION MATRIX 2009-2010 (SOURCE CERVED)
Merton Model
13
VE 615
VL 1930sE 20%T 1Rf 6.00%
VA 2493.5sA 16.10%m 7.00%d2 1.883
N(-d2) 2.98%Def.Prob. 2.98%
Book values
(Source: MEF)Rating classes
(Source: Moody’s- S&P)
KMV-Moody’s Model
14
VA 2439.50 Bil €sA 16.10%Default point 1570.00 Bil €D-to-D 2.30PD 107 BpsRating BB/B+
Snapshot of Distance to Default
Section 5
Concluding Remarks
Greater impact on conservative banks capital structure.
Internal models more efficient and less costly in terms of provision compared to standard models.
Compared to Basel 2, Basel 3 ratio is increased with the asset allocation that is poured on the final customer.
Complexity in the PD due to the counterparty.
Difficulties in the credit to the other party depending on the entrepreneurial company to which they belong.
Concluding Remarks
15
Master’s Degree in Risk Management in Financial MarketsPisa, 16th December 2011 VIII° Edition
Counterparty Credit risk measurement:Rules and Estimation methods.
Pierpaolo Cassese
