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Luis Ortizgracia - One of the best experts on this subject based on the ideXlab platform.
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quantifying Credit Portfolio losses under multi factor models
2019Co-Authors: Gemma Colldefornspapiol, Luis Ortizgracia, Cornelis W OosterleeAbstract:ABSTRACTIn this work, we investigate the challenging problem of estimating Credit risk measures of Portfolios with exposure concentration under the multi-factor Gaussian and multi-factor t-copula m...
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quantifying Credit Portfolio losses under multi factor models
2017Co-Authors: Gemma Colldefornspapiol, Luis Ortizgracia, Cornelis W OosterleeAbstract:In this work, we investigate the challenging problem of estimating Credit risk measures of Portfolios with exposure concentration under the multi-factor Gaussian and multi-factor t-copula models. It is well-known that Monte Carlo (MC) methods are highly demanding from the computational point of view in the aforementioned situations. We present efficient and robust numerical techniques based on the Haar wavelets theory for recovering the cumulative distribution function (CDF) of the loss variable from its characteristic function. To the best of our knowledge, this is the first time that multi-factor t-copula models are considered outside the MC framework. The analysis of the approximation error and the results obtained in the numerical experiments section show a reliable and useful machinery for Credit risk capital measurement purposes in line with Pillar II of the Basel Accords.
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haar wavelets based approach for quantifying Credit Portfolio losses
2014Co-Authors: Josep J Masdemont, Luis OrtizgraciaAbstract:This paper proposes a new methodology to compute Value at Risk (VaR) for quantifying losses in Credit Portfolios. We approximate the cumulative distribution of the loss function by a finite combination of Haar wavelet basis functions and calculate the coefficients of the approximation by inverting its Laplace transform. The Wavelet Approximation (WA) method is particularly suitable for non-smooth distributions, often arising in small or concentrated Portfolios, when the hypothesis of the Basel II formulas are violated. To test the methodology we consider the Vasicek one-factor Portfolio Credit loss model as our model framework. WA is an accurate, robust and fast method, allowing the estimation of the VaR much more quickly than with a Monte Carlo (MC) method at the same level of accuracy and reliability.
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haar wavelets based approach for quantifying Credit Portfolio losses
2011Co-Authors: Josep J Masdemont, Luis OrtizgraciaAbstract:This paper proposes a new methodology to compute Value at Risk (VaR) for quantifying losses in Credit Portfolios. We approximate the cumulative distribution of the loss function by a finite combination of Haar wavelet basis functions and calculate the coefficients of the approximation by inverting its Laplace transform. The Wavelet Approximation (WA) method is particularly suitable for non-smooth distributions, often arising in small or concentrated Portfolios, when the hypothesis of the Basel II formulas are violated. To test the methodology we consider the Vasicek one-factor Portfolio Credit loss model as our model framework. WA is an accurate, robust and fast method, allowing to estimate VaR much more quickly than with a Monte Carlo (MC) method at the same level of accuracy and reliability
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haar wavelets based approach for quantifying Credit Portfolio losses
2009Co-Authors: Josep J Masdemont, Luis OrtizgraciaAbstract:This paper proposes a new methodology to compute Value at Risk (VaR) for quantifying losses in Credit Portfolios. We approximate the cumulative distribution of the loss function by a finite combination of Haar wavelets basis functions and calculate the coefficients of the approximation by inverting its Laplace transform. In fact, we demonstrate that only a few coefficients of the approximation are needed, so VaR can be reached quickly. To test the methodology we consider the Vasicek one-factor Portfolio Credit loss model as our model framework. The Haar wavelets method is fast, accurate and robust to deal with small or concentrated Portfolios, when the hypothesis of the Basel II formulas are violated.
Klaus Duellmann - One of the best experts on this subject based on the ideXlab platform.
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systemic risk contributions a Credit Portfolio approach
2011Co-Authors: Natalia Tente, Klaus DuellmannAbstract:We put forward a framework for measuring systemic risk and attributing it to individual banks. Systemic risk is measured as the expected loss to depositors and investors when a low-probability systemic event occurs. The risk contributions are calculated based on derivatives of the systemic risk measure and, thus, ensure a full risk allocation among institutions. We apply our approach to a panel of 54 to 86 of the world's major commercial banks, using 13 years of monthly data. This empirical exercise shows that, whereas the median systemic risk in the sample is about $3tr, it peaks at $20tr at the beginning of 2009. Thereby some 4 to 10 of the biggest contributors account for more than 50% of the system-wide risk, contributing considerably more than their relative size suggests. Based on data for banks' liabilities, default probabilities and asset correlations, we can match very closely the list of G-SIBs revealed by FSB. The individual risk contributions may not only be used for identification of systemically important banks. Being an estimate of negative externalities, they can also be used to compute bank-specific capital surcharges, as we describe in the paper. In addition to this cross-sectional dimension, we also address the time dimension of systemic risk and suggest a method for smoothing the cyclicality of the underlying risk measure. The analysis of risk drivers confirms that the main focus of macroprudential banking supervision should be on a solid capital base throughout the cycle and de-correlation of banks' asset values.
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asset correlations and Credit Portfolio risk an empirical analysis
2007Co-Authors: Klaus Duellmann, Martin Scheicher, Christian SchmiederAbstract:In Credit risk modelling, the correlation of unobservable asset returns is a crucial component for the measurement of Portfolio risk. In this paper, we estimate asset correlations from monthly time series of Moody's KMV asset values for around 2,000 European firms from 1996 to 2004. We compare correlation and value-atrisk (VaR) estimates in a one-factor or market model and a multi-factor or sector model. Our main finding is a complex interaction of Credit risk correlations and default probabilities affecting total Credit Portfolio risk. Differentiation between industry sectors when using the sector model instead of the market model has only a secondary effect on Credit Portfolio risk, at least for the underlying Credit Portfolio. Averaging firm-dependent asset correlations on a sector level can, however, cause a substantial underestimation of the VaR in a Portfolio with heterogeneous borrower size. This result holds for the market as well as the sector model. Furthermore, the VaR of the IRB model is more stable over time than the VaR of the market model and the sector model, while its distance from the other two models fluctuates over time.
Jeffrey R Bohn - One of the best experts on this subject based on the ideXlab platform.
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approaches to improving bank share value using Credit Portfolio management and Credit transfer pricing
2013Co-Authors: Jeffrey R Bohn, Roger M SteinAbstract:Prudent Credit risk management within a bank requires that a number of agents within the firm communicate, agree, and act in a concerted fashion to manage Credit risk both at the individual exposure level and at the broader Portfolio level. This can be challenging, given the nature of Credit Portfolios. Even if highly diversified, Credit Portfolios display heavilyskewedlossdistributionsthatimplyrelativelylongquiescentperiods(duringwhich lossesarelowerthantheirmathematicalexpectationsandthebenefitsofriskmanagement less visible) and occasional periods of much higher losses. This phenomenon makes it difficult to maintain focus on the impact of individual trades or loans on the longerterm risk of Portfolio losses, particularly in large organizations. In this nontechnical paper, which draws on and extends portions of Bohn and Stein (2009), we reflect on some of these challenges and discuss mechanisms, such as Credit-transfer pricing, by which banks can better align the behaviors of underwriters, risk managers, and senior managers within large institutions while also increasing the communications between thesegroups.Thisapproachgrewoutofindustrypracticeandiscurrentlyinusetovarying degrees by a number of large banks worldwide. While many challenges still persist in its implementation, innovations in both extending Credit and modeling Credit continue to evolve to address them, making implementation more practically feasible.
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active Credit Portfolio management in practice
2009Co-Authors: Jeffrey R Bohn, Roger M SteinAbstract:Foreword. Preface. Acknowledgments. Chapter 1. The Framework: Definitions and Concepts. What Is Credit? Evolution of Credit Markets. Defining Risk. A Word About Regulation. What Are Credit Models Good For? Active Credit Portfolio Management (ACPM). Framework at 30,000 Feet. Building Blocks of Portfolio Risk. Using PDs in Practice. Value, Price, and Spread. Defining Default. Portfolio Performance Metrics. Data and Data Systems. Review Questions. Chapter 2. ACPM in Practice. Bank Valuation. Organizing Financial Institutions: Dividing into Two Business Lines. Emphasis on Credit Risk. Market Trends Supporting ACPM. Financial Instruments Used for Hedging and Managing Risk in a Credit Portfolio. Mark-To-Market and Transfer Pricing. Metrics for Managing a Credit Portfolio. Data and Models. Evaluating an ACPM Unit. Managing a Research Team. Conclusion. Review Questions. Exercises. Chapter 3. Structural Models. Structural Models in Context. A Basic Structural Model. Black-Scholes-Merton (BSM). Valuation. Modifying BSM. First-Passage Time: Black-Cox. Practical Implementation: Vasicek-Kealhofer. Stochastic Interest Rates: Longstaff-Schwartz. Jump-Diffusion Models: Zhou. Endogenous Default Barrier (Taxes and Bankruptcy Costs): Leland-Toft. Corporate Transaction Analysis. Liquidity. Other Structural Approaches. Conclusion. Appendix 1. Derivation of Black-Scholes-Merton Framework for Calculating Distance-to-Default (DD). Appendix 2. Derivation of Conversion of Physical Probability of Default (PD) to a Risk-Neutral Probability of Default (PD Q ). Review Questions. Exercises. Chapter 4. Econometric Models. Discrete-Choice Models. Early Discrete Choice Models: Beaver (1966) and Altman (1968). Hazard Rate (Duration) Models. Example of a Hazard Rate Framework for Predicting Default: Shumway (2001). Hazard Rates versus Discrete Choice. Practical Applications: Falkenstein, et al. (2000) and Dwyer and Stein (2004). Calibrating Econometric Models. Calibrating to PDs. Calibrating to Ratings. Interpreting the Relative Influence of Factors in Econometric Models. Data Issues. Taxonomy of Basic Data Woes. Biased Samples Cannot Easily Be Fixed. Conclusion. Appendix 1. Some Alternative Default Model Specifications. Review Questions. Exercises. Chapter 5. Loss Given Default. Road to Recovery: The Timeline of Default Resolution. Measures of LGD (Recovery). The Relationship between Market Prices and Ultimate Recovery. Approaches to Modeling LGD: The LossCalc (2002, 2004) Approaches and Extensions. Conclusion. Review Questions. Exercises. Chapter 6. Reduced-Form Models. Reduced-Form Models in Context. Basic Intensity Models. A Brief Interlude to Discuss Valuation. Duffie and Singleton Intensity Model. Credit Rating Transition Models. Default Probability Density Version of Intensity Models (Hull-White). Generic Credit Curves. Conclusion. Appendix: Kalman Filter. Review Questions. Exercises. Chapter 7. PD Model Validation. The Basics. Parameter Robustness. Measures of Model Power. Measures of PD Levels and Calibration. Sample Size and Confidence Bounds. Assessing the Economic Value of More Powerful PD Models. Avoiding Overfitting: A Walk-Forward Approach to Model Testing. Conclusion. Appendix 1. Type I and Type II Error: Converting Cap Plots into Contingency Tables. Appendix 2. The Likelihood for the General Case of a Default Model. Appendix 3. Tables of ROC e and n max. Appendix 4. Proof of the Relationship between NPV Terms and ROC Terms. Appendix 5. Derivation of Minimum Sample Size Required to Test for Default Rate Accuracy in Uncorrelated Case. Appendix 6. Tables for Lower Bounds of e and N on Probabilities of Default. Review Questions. Exercises. Chapter 8. Portfolio Models. A Structural Model of Default Risk. Measurement of Portfolio Diversification. Portfolio Risk Assuming No Credit Migration. Structural Models of Default Correlation. Credit Migration. A Model of Value Correlation. Probability of Large Losses. Valuation. Return Calculations. Risk Calculations. Portfolio Loss Distribution. Capital. Economic Capital and Portfolio Management. Improving Portfolio Performance. Performance Metrics. Reduced-Form Models and Portfolio Modeling. Correlation in Intensity Models. Copulas. Frailty. Integrating Market and Credit Risk. Counterparty Risk in Credit Default Swaps (CDS) and Credit Portfolios. Conclusion. Review Questions. Exercises. Chapter 9. Building a Better Bank. A Case Study. Description. Current Organization. Transforming the Capital Allocation Process. Portfolio Analysis. Active Credit Portfolio Management (ACPM). Data, Systems, and Metrics. ACPM and Transforming the Bank. Appendix: Figures. Exercises. References. About the Authors. Index.
Roger M Stein - One of the best experts on this subject based on the ideXlab platform.
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approaches to improving bank share value using Credit Portfolio management and Credit transfer pricing
2013Co-Authors: Jeffrey R Bohn, Roger M SteinAbstract:Prudent Credit risk management within a bank requires that a number of agents within the firm communicate, agree, and act in a concerted fashion to manage Credit risk both at the individual exposure level and at the broader Portfolio level. This can be challenging, given the nature of Credit Portfolios. Even if highly diversified, Credit Portfolios display heavilyskewedlossdistributionsthatimplyrelativelylongquiescentperiods(duringwhich lossesarelowerthantheirmathematicalexpectationsandthebenefitsofriskmanagement less visible) and occasional periods of much higher losses. This phenomenon makes it difficult to maintain focus on the impact of individual trades or loans on the longerterm risk of Portfolio losses, particularly in large organizations. In this nontechnical paper, which draws on and extends portions of Bohn and Stein (2009), we reflect on some of these challenges and discuss mechanisms, such as Credit-transfer pricing, by which banks can better align the behaviors of underwriters, risk managers, and senior managers within large institutions while also increasing the communications between thesegroups.Thisapproachgrewoutofindustrypracticeandiscurrentlyinusetovarying degrees by a number of large banks worldwide. While many challenges still persist in its implementation, innovations in both extending Credit and modeling Credit continue to evolve to address them, making implementation more practically feasible.
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active Credit Portfolio management in practice
2009Co-Authors: Jeffrey R Bohn, Roger M SteinAbstract:Foreword. Preface. Acknowledgments. Chapter 1. The Framework: Definitions and Concepts. What Is Credit? Evolution of Credit Markets. Defining Risk. A Word About Regulation. What Are Credit Models Good For? Active Credit Portfolio Management (ACPM). Framework at 30,000 Feet. Building Blocks of Portfolio Risk. Using PDs in Practice. Value, Price, and Spread. Defining Default. Portfolio Performance Metrics. Data and Data Systems. Review Questions. Chapter 2. ACPM in Practice. Bank Valuation. Organizing Financial Institutions: Dividing into Two Business Lines. Emphasis on Credit Risk. Market Trends Supporting ACPM. Financial Instruments Used for Hedging and Managing Risk in a Credit Portfolio. Mark-To-Market and Transfer Pricing. Metrics for Managing a Credit Portfolio. Data and Models. Evaluating an ACPM Unit. Managing a Research Team. Conclusion. Review Questions. Exercises. Chapter 3. Structural Models. Structural Models in Context. A Basic Structural Model. Black-Scholes-Merton (BSM). Valuation. Modifying BSM. First-Passage Time: Black-Cox. Practical Implementation: Vasicek-Kealhofer. Stochastic Interest Rates: Longstaff-Schwartz. Jump-Diffusion Models: Zhou. Endogenous Default Barrier (Taxes and Bankruptcy Costs): Leland-Toft. Corporate Transaction Analysis. Liquidity. Other Structural Approaches. Conclusion. Appendix 1. Derivation of Black-Scholes-Merton Framework for Calculating Distance-to-Default (DD). Appendix 2. Derivation of Conversion of Physical Probability of Default (PD) to a Risk-Neutral Probability of Default (PD Q ). Review Questions. Exercises. Chapter 4. Econometric Models. Discrete-Choice Models. Early Discrete Choice Models: Beaver (1966) and Altman (1968). Hazard Rate (Duration) Models. Example of a Hazard Rate Framework for Predicting Default: Shumway (2001). Hazard Rates versus Discrete Choice. Practical Applications: Falkenstein, et al. (2000) and Dwyer and Stein (2004). Calibrating Econometric Models. Calibrating to PDs. Calibrating to Ratings. Interpreting the Relative Influence of Factors in Econometric Models. Data Issues. Taxonomy of Basic Data Woes. Biased Samples Cannot Easily Be Fixed. Conclusion. Appendix 1. Some Alternative Default Model Specifications. Review Questions. Exercises. Chapter 5. Loss Given Default. Road to Recovery: The Timeline of Default Resolution. Measures of LGD (Recovery). The Relationship between Market Prices and Ultimate Recovery. Approaches to Modeling LGD: The LossCalc (2002, 2004) Approaches and Extensions. Conclusion. Review Questions. Exercises. Chapter 6. Reduced-Form Models. Reduced-Form Models in Context. Basic Intensity Models. A Brief Interlude to Discuss Valuation. Duffie and Singleton Intensity Model. Credit Rating Transition Models. Default Probability Density Version of Intensity Models (Hull-White). Generic Credit Curves. Conclusion. Appendix: Kalman Filter. Review Questions. Exercises. Chapter 7. PD Model Validation. The Basics. Parameter Robustness. Measures of Model Power. Measures of PD Levels and Calibration. Sample Size and Confidence Bounds. Assessing the Economic Value of More Powerful PD Models. Avoiding Overfitting: A Walk-Forward Approach to Model Testing. Conclusion. Appendix 1. Type I and Type II Error: Converting Cap Plots into Contingency Tables. Appendix 2. The Likelihood for the General Case of a Default Model. Appendix 3. Tables of ROC e and n max. Appendix 4. Proof of the Relationship between NPV Terms and ROC Terms. Appendix 5. Derivation of Minimum Sample Size Required to Test for Default Rate Accuracy in Uncorrelated Case. Appendix 6. Tables for Lower Bounds of e and N on Probabilities of Default. Review Questions. Exercises. Chapter 8. Portfolio Models. A Structural Model of Default Risk. Measurement of Portfolio Diversification. Portfolio Risk Assuming No Credit Migration. Structural Models of Default Correlation. Credit Migration. A Model of Value Correlation. Probability of Large Losses. Valuation. Return Calculations. Risk Calculations. Portfolio Loss Distribution. Capital. Economic Capital and Portfolio Management. Improving Portfolio Performance. Performance Metrics. Reduced-Form Models and Portfolio Modeling. Correlation in Intensity Models. Copulas. Frailty. Integrating Market and Credit Risk. Counterparty Risk in Credit Default Swaps (CDS) and Credit Portfolios. Conclusion. Review Questions. Exercises. Chapter 9. Building a Better Bank. A Case Study. Description. Current Organization. Transforming the Capital Allocation Process. Portfolio Analysis. Active Credit Portfolio Management (ACPM). Data, Systems, and Metrics. ACPM and Transforming the Bank. Appendix: Figures. Exercises. References. About the Authors. Index.
Wolf Wagner - One of the best experts on this subject based on the ideXlab platform.
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a market based measure of Credit Portfolio quality and banks performance during the subprime crisis
2012Co-Authors: Martin Knaup, Wolf WagnerAbstract:We propose a new method for measuring the quality of banks' Credit Portfolios. This method makes use of information embedded in bank share prices by exploiting differences in their sensitivity to Credit default swap spreads of borrowers of varying quality. The method allows us to derive a Credit risk indicator (CRI). This indicator represents the perceived share of high-risk exposures in a bank's Portfolio and can be used as a risk weight for computing regulatory capital requirements. We estimate CRIs for the 150 largest U.S. bank holding companies. We find that their CRIs are able to forecast bank failures and share price performances during the crisis of 2007--2009, even after controlling for a variety of traditional asset quality and general risk proxies. This paper was accepted by Wei Xiong, finance.
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a market based measure of Credit Portfolio quality and banks performance during the subprime crisis
2009Co-Authors: Martin Knaup, Wolf WagnerAbstract:We propose a new method for measuring the quality of banks' Credit Portfolios. This method makes use of information embedded in bank share prices by exploiting differences in their sensitivity to Credit default swap spreads of borrowers of varying quality. The method allows us to derive a Credit risk indicator (CRI). This indicator represents the perceived share of high risk exposures in a bank's Portfolio and can be used as a risk-weight for computing regulatory capital requirements. We estimate CRIs for the 150 largest U.S. bank holding companies (BHCs). We find that their CRIs are able to forecast bank failures and share price performances during the crisis of 2007-2009, even after controlling for a variety of traditional asset quality and general risk proxies.