Utilizamos tu perfil de LinkedIn y tus datos de actividad para personalizar los anuncios y mostrarte publicidad más relevante. Puedes cambiar tus preferencias de publicidad en cualquier momento.
Próxima SlideShare
Cargando en…5
×

# Counterparty Credit risk measurement:Rules and Estimation methods

2.795 visualizaciones

counterparty credit risk under new Regulatory Framework Merton\’s Model
KMV-Moody\’s MOdel

• Full Name
Comment goes here.

Are you sure you want to Yes No
• Sé el primero en comentar

• Sé el primero en recomendar esto

### Counterparty Credit risk measurement:Rules and Estimation methods

1. 1. Master’s Degree in risk management and quantitative finance Pisa, 16th December 2011 VIII° Edition Counterparty Credit risk measurement: Rules and Estimation methods. Pierpaolo Cassese Prof. Franca Orsi University of Pisa (Academic Tutor)
2. 2. 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
3. 3. Section 1 Counterparty Credit Risk: regulatory aspects
4. 4. 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
5. 5.  Basel 2 Regulatory Framework  Credit Risk  Market Risk  Operational Risk  Counterparty Risk  Bilateral Risk Counterparty Credit Risk: regulatory aspects
6. 6. Section 2 Counterparty credit risk evaluation : internal models
7. 7. Applicable with permission of Bank of Italy Not depending from model to compute the RP Counterparty Credit Risk evaluation: Internal Models
8. 8. 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 models Main measures of counterparty credit risk
9. 9. 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
10. 10. Section 3 Stochastical models for computing PD
11. 11.  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
12. 12.  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
13. 13.  Time Series of balancesheet  Composition of Liabilities  Determining asset and volatility value  Estimate of D-to-D KMV-Moody’s Model
14. 14. Section 4 Results
15. 15.  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
16. 16. Results: Mortgage loans Notional Amount 1 Mln € Expiry Date 60 Months Interest Rate 4.50% Amortization Constant payment Collateral 40% Mortgage Loans Notional Amount 1 Mln € Expiry Date 60 Months Interest Rate 4.50% Amortization Constant payment Mortgage Loans
17. 17. MERTON MODEL PARAMETERS TRANSICTION MATRIX 2009-2010 (SOURCE CERVED) Merton Model VE 615 VL 1930 E 20% T 1 Rf 6.00% VA 2493.5 A 16.10% 7.00% d2 1.883 N(-d2) 2.98% Def.Prob. 2.98%
18. 18. Book values (Source: MEF) Rating classes (Source: Moody’s- S&P) KMV-Moody’s Model VA 2439.50 Bil € A 16.10% Default point 1570.00 Bil € D-to-D 2.30 PD 107 Bps Rating BB/B+ Snapshot of Distance to Default
19. 19. Section 5 Concluding Remarks
20. 20.  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
21. 21. Master’s Degree in Risk Management in Financial Markets Pisa, 16th December 2011 VIII° Edition Counterparty Credit risk measurement: Rules and Estimation methods. Pierpaolo Cassese