Managing Portfolio Credit Risk in Banks Arindam Bandyopadhyay 4843/24, 2nd Floor, Ansari Road, Daryaganj, Delhi 110002, India Cambridge Univerisity Press is part of the University of Cambridge It furthers the University’s mission by disseminating knowledge in the pursuit of education, learning and research at the highest international levels of excellence www.cambridge.org Information on this title: www.cambridge.org/9781107146471 © Arindam Bandyopadhyay 2016 This publication is in copyright Subject to statutory exception and to the provisions of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University Press First published 2016 Printed in India A catalogue record for this publication is available from the British Library ISBN 978-1-107-14647-1 Hardback Cambridge University Press has no responsibility for the persistence or accuracy of URLs for external or third-party internet websites referred to in this publication, and does not guarantee that any content on such websites is, or will remain, accurate or appropriate To my wife Mousumi, whose encouragement and support made it possible Contents Tables, Figures, Charts viii Prefacexv Acknowledgementsxx Abbreviationsxxii 1.  Introduction to Credit Risk 2.  Credit Rating Models 24 3.  Approaches for Measuring Probability of Default (PD) 111 4. Exposure at Default (EAD) and Loss Given Default (LGD) 137 5.  Validation and Stress Testing of Credit Risk Models 186 6. Portfolio Assessment of Credit Risk: Default Correlation, Asset Correlation and Loss Estimation 235 7.  Economic Capital and RAROC 276 8. Basel II IRB Approach of Measuring Credit Risk Regulatory Capital318 Index355 Tables, Figures, Charts Tables Table 1.1: Trends in Quarterly Gross Non-performing Assets of Indian Banks by Banking Groups (%)  10 Table 2.1: Expert Judgement System vs Model-driven System Rating Model  27 Table 2.2: Example of an Internal Rating Template  33 Table 2.3: Use of Two-Dimensional Rating in Credit Management 37 Table 2.4: Example of Project Finance Expert-based Rating Model 40 Table 2.5: Risk Criteria of Credit Rating Agencies (CR As)  47 Table 2.6: Comparison of Predictive Power of New Z-score vis-à-vis other Z-score Models  59 Table 2.7: Parameters of Indian Logit EPD Models  65 Table 2.8: Example of Statistically Derived Application Scorecard— Residential Housing Loan  75 Table 2.9: Statistically Derived Risk Weights in Agri-loans 78 Table 2.10: Mapping Scores to PD  80 Table 2.11: Steps in Estimating EDF in MKMV Model  93 Table 2.12: Calibration of Real EDFs with CRISIL Corporate Rating Grades  95 Table 2.13: Example of Hybrid Corporate Logit Model for Indian Public Firms  103 Table 3.1: Historical Rating-wise Default Statistics  116 Table 3.2: Average One-year Rating Transition of 572 Indian Corporate Bonds Rated Externally by CRISIL, 1992–2009118 Table 3.2a: Indian Corporate Loan Rating Movements in Recent Years, 2008–15  119 Tables, Figures, Charts    ix Table 3.3: CRISIL’s Published One-year Indian Corporate Transition Matrix  119 Table 3.4: S&P Global Corporate Transition Matrix in % (1981–2012) 120 Table 3.5: Annual Industry PDs (%) for Different Loan Grades  121 Table 3.6: One-year Corporate Transition Matrix of a Bank in India, 2003–09 (%) 123 Table 3.7: Historical Rating-wise Default Statistics  126 Table 3.8: Relationship between Yearly PD and Cumulative PD (CPD) 127 Table 3.9: Default Rates for Different Horizons  128 Table 3.10: CRISIL’s Indian Corporate Cumulative PDs (Withdrawal Adjusted) (%)  129 Table 3.11: Estimation of Frequency-based Pooled PD for Homogeneous Retail Buckets (Personal Loans) – Illustration 1  131 Table 3.12: Estimation of Long-run Average Pooled Probability of Default for Homogeneous Retail Pool (Personal Loan) – Illustration (Exposure-based Method)  133 Table 4.1: UGD per Rating Class on Bank’s Loan Commitments  143 Table 4.2: Facility-wise CCF/UGD (%) Estimates of a Large Indian PSB  145 Table 4.3: Facility Pool-wise UGD/CCFs of another Large PSB  145 Table 4.4: Rating-wise UGD Estimates of a Large PSB  146 Table 4.5: Illustrative Example for Computing Historical and Economic LGD  156 Table 4.6: List of Popular Public Studies on Loan LGD  160 Table 4.7: First Round LGD Survey Estimates for Indian Banks: Commercial and Retail Bank Loans, 1998–2007  161 Table 4.8: LGD (%) Statistics for Defaulted Commercial Loans in India: Second Round Survey Results  164 Table 4.9: LGD (%) Statistics for Commercial Loans: Secured vs Unsecured Loans  165 Table 4.10: Margin-wise LGD (%) Statistics–Secured Commercial Loans  166 Table 4.11: Collateral-wise Secured Commercial Loan LGD (%)  167 Table 4.12: Historical LGD (%) for Retail Loans: Secured vs Unsecured Loans  168 x  |  Tables, Figures, Charts Table 4.13: LGD Predictor Models – Multivariate Tobit Regression Results 174 Table 4.14: Estimation of Long-run Average LGD for a Retail Pool 176 Table 4.15: LGDs: Simple vs Weighted Average by Default Year (Corporate Loans) 178 Table 5.1: Validation of CR As Ratings through Descriptive Statistics 198 Table 5.2: Group Statistics – Solvent vs Defaulted Firms 199 Table 5.3: Classification Power of the Model (Within Sample Test)  200 Table 5.4: Validation Report of a Bank’s Internal Rating System for Commercial Loans, 2003–09  202 Table 5.5: Comparison of Discriminatory Power of Two-rating Models 206 Table 5.6: Comparing Model Gini Coefficients  207 Table 5.7: Example of KS Test  212 Table 5.8: A Retail-rating Model Calibration  214 Table 5.9: Chi-square Test for Model Comparison  217 Table 5.10: Calibration Test for LGD Rating Model  218 Table 5.10a: Comparing the Discriminatory Power of Models  219 Table 6.1: Portfolio Loss Calculations for Two-asset Example  241 Table 6.2: Assessment Industry Rating Position and Sectoral Credit Growth  244 Table 6.3: Estimation of Single Default Correlation  249 Table 6.4: Default and Asset Correlation of Indian Banks  250 Table 6.5: Estimation of Rating-wise Default Correlation  253 Table 6.6: Overall IG–NIG Default Correlations (%), 1992–93 to 2012–13 254 Table 6.7: Default Correlation across Rating Grades, 1992–93 to 2008–09255 Table 6.7a: Global Rating-wise Default Correlations (%) – All Countries, All Industries, 1981–2002, S&P Credit Pro 255 Table 6.8: Industry Risk Weights  259 Table 6.9: The System-Level Industry Default Correlation Estimates in India  261 Table 6.10: Descriptive Statistics for Exposure Concentration of Large Borrowers (of A/Cs > `5 crore exposure) 267 Table 6.11: Estimation of Rating-wise Single Default Correlation  268 348  |  Managing Portfolio Credit Risk in Banks Basel III regulation expects that banks for its survival in future must understand the importance of people’s perception about a bank’s liquidity condition (short term as well as long term) besides internal management of liquidity It emphasized that banks’ liquid assets should be sufficient enough to cover net cash outflow It focuses on stable funding sources and high quality liquid assets to reduce the short-term vulnerabilityof the maturity structure of bank’s assets and liabilities Basel III also wants to ensure that banks’ leverage ratio (Tier I capital divided by all on and off balance sheet items) should be at least greater than per cent (in India, 4.5 per cent) The countercyclical and capital conservation buffers have been introduced to dampen the cyclical effect of the minimum capital requirements and provisioning standards The capital conservation buffer should be built in the form of common equity in phased manner (2.5 per cent or RWA) over and above the minimum capital requirement so that it can be drawn during the periods of economic stress The counter cyclical buffer (0–2.5 per cent of RWA) has been kept to adjust the bank credit growth to macroeconomic growth These ensure the availability of credit during economic downturn to reduce pro-cyclical effects It is expected that more conservative capital estimate under Basel III will provide more stability in the bank’s capital structure by restricting the banks to take on excessive leverage (or more risky) positions Recent Regulatory Developments in Assessing Credit Risk Capital Conceptually, the IRB framework is intended to ensure accuracy and risk sensitivity of capital requirements and thereby motivate the banks to implement better risk management practices As part of regulatory efforts in ensuring level playing field among internationally active banks, the Basel Committee on Banking Supervision has established the Regulatory Consistency Assessment Programme (RCAP) in 2012 to assess the consistency of Basel IRB standards Lately, BCBS July 2013 RCAP study has highlighted that different PD methodologies used by banks lead to significant variations in PD estimates and risk-weighted assets (RWAs) The European Banking Authority (EBA) has also reviewed the validity of IRB framework since its implementation over the years The forum has accepted that the more risk sensitive IRB system has actually encouraged the institutions to adopt more sound and sophisticated internal risk management practices However, both EBA and RCAP forum have also noted that there is a significant Basel II IR B Approach of Measuring Credit R isk Regulatory Capital    349 divergence in the risk estimates due to IRB Both the regulatory bodies have opined that this difference can be remedied through proper benchmarking exercises, delegation of a number of technical standards and planned regulatory developments through seeking industry opinion They have also recommended harmonization of methodologies and publication of supervisory benchmarks in due course Recently, Basel Committee has suggested certain revisions to the standardized approach to credit risk to properly balance risk sensitivity and complexity in risk weighted assets across banks and jurisdictions (BCBS, 22 December 2015) However, these changes will be consistent with the existing set of norms The second consultative document has come in response to the extensive industry feedback on its first consultative document that was published in December 14 (BSBCS, 22 December, 2014) The main aim is to further strengthen the link between the standardized approach and the internal ratings based (IRB) approach by increasing risk sensitivity and improve consistency and congruity of capital requirements across banks The US agencies have also proposed some additional regulatory initiatives to improve the level of compliance with the Basel IRB minimum standards Still, it is under consultation process as the regulator is now awaiting comments from the banking industry The amendments to the final rule are expected to be released in the middle of 2016 (see BIS website) Lately, the Reserve Bank of India (RBI, 31 March 2015) has made certain modifications to the guidelines on implementation of advanced model-based approaches for credit risk The regulator expects that once IRB is adopted by Indian banks, it will be extended across all material asset classes within the bank and for the entire banking group Only the exposures to Central Counter Parties (CCPs) arising from OTC derivatives will be treated separately under Basel III However, in some bank cases, RBI may permit a phased transition to IRB approach During the transition period, the IRB regulatory capital will be subject to prudential floor which shall be 100 per cent of the existing standardized approach for one year and 90 per cent for second year and onwards As part of quantitative disclosures, now banks will have to report NPA movements by major industry or counterparty type In addition, banks will have to reveal the amount of NPAs and past due loans by significant geographic areas including provisions related to each area It has also been indicated that the IRB banks will have to make additional disclosures in comparison to banks under the standardized approach It is expected that the improved disclosures will bring more transparency to the entire banking system and enhance stakeholders’ value 350  |  Managing Portfolio Credit Risk in Banks Summary Broadly speaking, the objectives of internal rating-based approach under Basel II and Basel III are to encourage better and more systematic risk management practices, especially in the area of credit risk, and to provide improved measures of capital adequacy for the benefit of supervisors and the marketplace more generally The details about regulatory capital requirements for various exposure categories have been explained in this chapter The regulatory and supervisory aspects pertaining to the adoption of internal rating-based approach are also discussed in detail Basel IRB approach wants to ensure that regulatory capital requirements are more in line with economic capital requirements of banks and by this, make capital allocation of banks more risk sensitive and accurate The advanced risk management system will make the banks aware of the risks inherent in the business activities and take advantage of this knowledge to gain competitive advantage and enhance shareholder value Supervisors too need to develop skills to validate the internal models developed by the banks, in conjunction with enhanced disclosure requirements that reveal more detailed risk information to the market at an aggregate level Integrated implementation of advanced credit risk measurement approaches in banks with wide geographical coverage is a phenomenal challenge Major IRB implementation challenges for banks are building a robust and accurate credit risk data system, model development, combine the model outputs, obtain human resource and skills, train the business people to familiarize with the system, and finally, involve the top management to implement the process successfully The IRB approaches require banks to formulate their own internal-rating models to calculate the credit risk capital It should be seen as not only an exercise in determining regulatory capital but also a sophisticated risk management tool Senior bank management may have legitimate concern about IRB approaches due to the potentially high-implementation cost (develop models, IT system, maintain database, develop skillsets, etc.) For example, at the top level, retail pooling logic involves business judgement and, hence, is subjective It is difficult to determine which product variant or customer segment should be treated separately (for example, different models for auto loan and used auto loan) Moreover, not all factors may be captured in a scoring model and subjectivity in applying application or behavioural scorecards may further lead to risk weight variation across banks However, the benefits of IRB compliance are manifold The IRB approach seeks to differentiate risk on an asset-by-asset level in direct contrast to the “grouping” Basel II IR B Approach of Measuring Credit R isk Regulatory Capital    351 method in the standardized approach This risk differentiation is intended to add transparency to the decision-making process at many levels in the bank: by asset, customer, business unit, portfolio and organization wide Then, the IRB approach urges banks to apply the sophisticated risk methodologies consistently across the organization The IRB advanced approach provides banks the opportunity to significantly reduce their credit risk-weights and reduce their required regulatory capital if they suitably adjust their portfolio by lending to rated but strong corporates, increase their retail lending, provide mortgage loans with higher margins and adopt diversification strategies It may lead to the realignment of business models as banks will seek to optimize risk reward and return on capital As the banks progress towards increased sophisticated risk measurement framework, in the long run they will be able to reduce their capital requirement in a controlled way and maximize its return through better capital management Finally, the more recent Basel III capital standard expects that bank should create a buffer in good time so that it can be used in bad time It urges banks to maintain high credit ratings to ensure greater solvency and to avoid costs in raising additional capital under unavoidable market conditions The regulator may take credit to GDP ratio as a measure of balancing factor and calibrating measure The RBI has recently identified some systemically important banks and suggests that these banks should have orderly resolution criteria so that the effect on the system in case of crisis is lessened Basel III also emphasizes on better, more transparent disclosure requirements for banks to conform to the Pillar III market discipline In this context, ICAAP policy of a bank plays a very important role as a risk management progress report of banks to the regulator The purpose of the ICAAP is to inform the board about the ongoing assessment of the bank’s risks, how management intends to mitigate those risks and how much current and future capital is necessary for long-term survival It is expected that by attaining more sophisticated IRB status, banks will be able to align their capital more favorably to the Basel III measures and can accomplish higher levels of core capital Key learning outcomes After studying the chapter and the relevant reading, you should be able to understand: • Risk weights and CRAR calculations under the Basel II standardized approach • Basel II IRB risk weight functions for various categories of assets 352  |  Managing Portfolio Credit Risk in Banks • Major differences between the Basel II standardized approach and internal rating based approach (IRB) • Haircut approach and CCF applications for various credit exposures • Challenges under FIRB and AIRB approaches • Implications of ICAAP and SREP on business benefits of banks • Basel III regulatory expectations Review questions Do you think that Basel II IRB approach is a more risk-sensitive approach than the Basel II standardized approach? What is the basis of risk-weight functions under the Basel II standardized approach? What is capital arbitrage? Why is this problematic from the supervisory angle? Explain the exposure classification under the IRB approach What is portfolio invariance? Is Basel II IRB risk-weight function more sensitive to LGD than the PD? Do you think IRB maturity effects are stronger for obligors with low probability of default and why? Does the association between the correlation factor and the PD values vary across asset classes? What are the major challenges for banks under Basel II IRB approach? 10 Mention the key IRB challenges for supervisors 11 Do you understand the exposure classification process under the Basel II IRB approach? Why has it been suggested? 12 What is ICAAP? Why is it important for banks? 13 Who owns the risk appetite statement in a bank? State three indicators to explain the risk appetite statement of a bank 14 What is a loan loss provision? 15 What is pro-cyclicality problem? 16 What is Basel III capital regulation? 17 What is the purpose of capital conservation buffer (CCB)? 18 What is countercyclical capital buffer (CCCB)? References Aas, K 2005 “The Basel II IRB Approach for Credit Portfolios: A Survey”, Norsk Regnesentral Basel II IR B Approach of Measuring Credit R isk Regulatory Capital    353 BCBS 2005 “An Explanatory Note on the Basel II IRB Risk Weight Functions”, BIS, July ——— 2006 “International Convergence of Capital Measurement and Capital Standards: A Revised Framework”, Publication No 128, Basel Committee on Banking Supervision, Bank for International Settlements, Basel, June ——— 2010 “Basel III: A Global Regulatory Framework for More Resilient Banks and Banking Systems”, 10 December ——— 2011 “Basel III: A Global Regulatory Framework for More Resilient Banks and Banking Systems”, June (revised) ——— 2012 “Basel III Counterparty Credit Risk and Exposures to Central Counterparties: Frequently Asked Questions”, December ——— 2013 “Analysis of Risk-weighted Assets for Credit Risk in the Banking Book”, BIS, July ——— 2013 “Regulatory Consistency Assessment Programme (RCAAP): Analysis of Risk-weighted Asset for Credit Risk in Banking Book”, July ——— 2014 “Revisions to the Standardized Approach for Credit Risk”, Consultative Document, 22 December ——— 2015 “Revisions to the Standardized Approach for Credit Risk”, Second Consultative Document, 22 December Bluhm, C., L Overbeck and C Wagner 2003 An Introduction to Credit Risk Modeling (Boca Raton, Florida: Chapman Hall/CRC) Drehmann, M and K Tsatsaronis 2014 “The Credit to GDP Gap and Countercyclical Buffers: Questions and Answers”, BIS Quarterly Review, March EBA 2015 “Future of the IRB Approach”, Discussion Paper, March, European Banking Authority Gordy, M 2001 “A Risk Factor Model Foundation for Ratings-based Bank Capital Rules”, Working Paper, February Ong M 2003 The Basel Handbook: A Guide for Financial Practitioners (London: RISK Books) Jokivuolle, E., J Pesola and M Viren 2015 “Why is Credit-to-GDP a Good Measure for Setting Countercyclical Buffers?” Journal of Financial Stability 18: 117–26 RBI 1999 “Risk Management Systems in Banks”, DBOD, October ——— 2007 “Guidelines for Implementation of the New Capital Adequacy Framework”, April ——— 2008 “Master Circular – Prudential Guidelines on Capital Adequacy and Market Discipline – Implementation of the New Capital Adequacy Framework (NCAF)”, 26 March ——— 2010 “Master Circular–Prudential Guidelines on Capital Adequacy and Market Discipline – New Capital Adequacy Framework (NCAF)”, February 354  |  Managing Portfolio Credit Risk in Banks ——— 2011 “Implementation of the Internal Rating Based (IRB) Approaches for Calculation of Capital Charge for Credit Risk”, DBOD, 22 December ——— 2012 “Guidelines on Implementation of Basel III Capital Regulations in India”, May ——— 2013 “Master Circular – Prudential Guidelines on Capital Adequacy and Market Discipline – New Capital Adequacy Framework (NCAF)”, July ——— 2013 “Master Circular – Basel III Capital Regulations”, July ——— 2014 “Implementation of Basel III Capital Regulations in India – Capital Planning”, 24 March ——— 2014 “Master Circular – Basel III Capital Regulations”, July ——— 2014 “Mater Circular – Prudential Guidelines on Capital Adequacy and Market Discipline – New Capital Adequacy Framework (NCAF), July ——— 2015 “Prudential Guidelines on Capital Adequacy and Liquidity Standards – Amendments”, 31 March ——— 2015 “Guidelines for Implementation of Countercyclical Capital Buffer (CCCB)”, February 2015 ——— 2015 “Master Circular-Basel III Capital Regulations”, July Index Accounting LGD, 154, 159 Accuracy ratio (AR), 125, 203 Advance bills (AB), 146, 148 Advanced internal rating based approach, 324 Adverse selection, 59, 23, 303 Agriculture Rating Model, 77–81 AIRB (see Advanced internal rating based approach) Altman, 52–57, 59, 67–68, 152, 161, 209 Application scorecard, 68, 75, 81, 105 Area under curve (AUC), 210 Area under ROC, 79 Asset correlation, 5, 249, 251, 256– 260, 268–269, 272, 324, 330–331 Asset drift, 90–93 Asset liability committee (ALCO), 15 Asset volatility, 85, 87, 89–90, 92, 103 Asymptotic single risk factor (ASRF), 330 Average risk contribution, 240 Back-testing, 22, 275 Bank failure, 101 Bank guarantee (BG), 146, 148–149 Bank solvency, 98, 265 Bankruptcy, 7, 9, 55–57, 68, 94, 112, 178 Basel committee for banking supervision (BCBS), 18 Basel II IRB approach, 19, 38, 73, 111–112, 187, 222, 289, 319–351 Basel II key principles, 189–190, 291 Basel II regulation, 32, 46, 187 Basel II standardized approach, 139, 163, 318–319, 322, 336–337, 351–352 Basel III regulation, 11, 343, 348 Bayes’ theorem, 58 Bayesian probability, 125 Bayesian, 125 Behavioural scorecard, 68, 81–82, 105, 350 Benchmarking, 70, 104, 112, 196, 201, 215, 220, 255, 349 Beta distribution, LGD, 172–173 Beta equity, 302 Bills discounted (BD), 146–147 Bills purchased (BP), 146–147 Binomial distribution, 237 Bivariate normal distribution, 257 Black and Scholes and Merton model (BSM), 84–85, 88, 90, 94, 209 Bluhm and Overbeck, 250 Borrower rating, 5, 36–37, 75, 123, 140, 149, 158, 163, 176, 262, 289, 337, 341 Borrower risk, 18, 24, 36, 63, 75, 81, 189, 258, 277 Brier Score, 73, 197, 217 Business cycle, 2, 51, 124, 158, 175, 221, 223–224, 226, 228–229, 289–291, 293, 299, 323 356 | Index Business risk, 20, 30, 34, 45, 47–48, 276 Calibration, 21, 37, 80, 112, 125, 196–197, 201, 213–218, 220– 221, 223, 230–231 CAP (see Cumulative accuracy profile) Capital adequacy ratio, 9, 17, 254, 271, 291, 293, 294, 344, 347 Capital allocation, 20–21, 112, 180, 243, 276, 300, 302, 311–312, 330, 350 Capital asset pricing models (CAPM), 91, 302, 305 Capital buffer, 324, 345–346 Capital charge, 112, 128, 231, 321, 344 Capital conservation buffer (CCB), 345–348 Capital floor, 336 Capital management, 343, 351 Capital multiplier, 279, 288, 303, 305 Capital planning, 121, 343 Capital risk weighted assets ratio, 11 CAPM (see capital asset pricing) CAR (see Capital adequacy ratio) Cash credit (CC), 146–147 Cash flow, 7, 30, 38–39, 46–47, 58, 85, 147–148, 153–154, 157– 159 CCF (see Credit Conversion Factor) CET1 (see Common equity tier 1) Chi-square test, 73, 175, 196–197, 216–217, 221 Chief Risk Officer (CRO), 15 Classification power, 53, 59, 200, 208 Cohort analysis, 112, 116, 119, 141 Collateral charge, 161 Collateral, 29, 70 Commercial real estate (CRE), 162, 322, 340 Common equity tier (CET1), 345– 347 Concentration risk capital, 269, 271 Concentration risk, 3, 5, 124, 235, 245, 247, 255, 260, 262–263, 265–266, 269, 271 Core equity capital, 351 Counter cyclical buffer (CB), 299, 348 Counter cyclical capital buffer (CCCB), 346, 352 Counterparty risk, 43, 344 Country risk, 43, 171 Covenants, 14 36, 50, 100, 139, 143, 145, 180 CRAR (see Capital risk weighted assets ratio) CRAs (see credit rating agencies) Credit bureau, 68, 70, 72, 83, 105, 215 Credit conversion factor, 3, 139–140, 337 Credit cycle, 221, 231 Credit loss distribution, 236, 260, 279 Credit monitor, 51, 85–87, 94, 193 Credit rating agencies (CRAs) rating, 45, 47 Credit Rating Information Services of India Limited (CRISIL), 48 Credit risk drivers, 38, 223 Credit risk management committee (CRMC), 14–15 Credit risk management department (CRMD), 14–15, 17, 21, 134 Credit risk mitigation, 320, 323, 342 Credit risk plus, 49, 143 Credit spread, 12–13, 93, 102, 153, 305 Credit value adjustment (CVA), 344 Credit VaR, 277–279, 284, 288–289, 299, 307 Index   357 CRISIL, 31, 35, 48, 58, 60–62, 94, 96, 104, 113, 117–120, 129, 153, 253, 258, 260, 294, 296–297 Cumulative accuracy profile, 73, 125, 200–201, 206 Cumulative probability of default (CPD), 125–129 Data cleaning and sorting, 164 Data collection, 137 Data quality, 20, 230, 341 Decision tree, 31–33, 328 Default correlation, 239–240, 246– 251 Default definition, 47 Default point, 84–85, 89, 90, 92, 96, 101 Default weighted LGD, 178, 229 Disclosure, 324, 349 Discount rate, 155, 157–159, 172, 175, 180 Discounted LGD, 175–176 Discriminatory power, 73, 79, 125, 187–188, 197, 200–201, 203– 211, 215, 218–219 Distance to default (DD), 84, 89–90, 92, 97–98, 101 Distribution fitting, 151 Downturn LGD, 178, 228 Drawing power, 137–138 Early warning signal, 25, 41, 52, 57, 59–60, 64, 101, 134, 143, 149 ECAIs, roles, 46, 321 Economic capital, 7, 9, 11, 13, 18, 20, 68–69, 81, 276–311 Economic LGD, 154–157, 159, 162, 164–165, 167, 172, 176 Economic profit, 301, 303, 310 Economic value added (EVA), 301 Education loan risks, 304 Effective maturity (M), 325, 333 EL (see Expected Loss) EL based HHI, 263 Enron case, 94–96 Equity correlation, 256–258, 260 European Banking Association (EBA), 348 Ex-ante LGD, 169, 179 Ex-ante prediction, 181 Expected default frequency (EDF), 43, 84, 87, 90, 92, 94 Expected LGD, 36, 152 Expected loss, 26, 3, 45, 43, 83, 90, 138, 235, 237 Expected probability of default (EPD), 76, 80 Expert judgment systems, 32 Facility rating, 35–37, 170 FICO score, 186 Financial instruments, 167, 346 Financial stability report (FSR), Fund based facilities, 147, 149–150 Gini coefficient, 73, 188, 196–197, 201, 203–204, 210, 262, 264 Gordy, 250, 257, 330 Granularity, 100, 125, 219, 232, 263, 328–329 Gross non performing assets (GNPA), 3, 9, 132 Guarantee, 30, 36, 40, 49, 70, 138, 150, 153 Gupton and Stein, 159, 171–172 Hair cut, 337–338 Hazard function, 102 Hirschman Herfindahl Index (HHI), 262 Historical data, 20, 27, 40, 130, 138, 158, 163, 180 Historical LGD, 154–155, 162, 167, 172 Homogeneous pool, 328 Housing loan rating model, 32, 203 358 | Index Hybrid credit scoring models, 102– 104 Hypothecation, 147, 158 ICAAP (see Internal capital adequacy assessment process) Idiosyncratic risk, 256 260, 330 Industry default rates, 121, 158, 229 Infinitely granular portfolio, 227 Infrastructure rating, 107 Internal audit, 193–194 Internal capital adequacy assessment process, 271, 289, 311, 342 Internal rating, 26, 31–33, 52, 59, 67, 104 Internal ratings based approach, 319– 320, 325 Investment grade (IG), 49, 123 IRB (see Internal ratings-based approach) IRB challenges, 352 IRB road map, xxiii Issue rating, 49–50 Joint default probability, 252, 256– 258 Judgmental rating model, xx K (see capital multiplier) Kendall’s tau, 200 Kingfisher, 96 KMV model, 55, 57, 84–86, 90, 94, 104–105, 256 Kolmogorov-Smirnov (KS) test, 217 LDP (see Low default portfolio) Letter of credit (LC), 149 Leverage ratio, 57, 348 Leverage, 28–29, 46, 54–55, 57, 68, 104, 158 LGD (see Loss given default) LGD prediction, 176 LGD predictor model, 36, 159, 162– 163, 169–171 LIED (see Loss in the event of default) Liquidity risk, 42, 44 Liquidity, 67 Loan to value ratio, 162 Log normal distribution, 92 Logit model, 63–64, 103 Long run PD, 116, 130, 133, 227, 248, 253 Long run LGD, 176 Lorenz curve, 202, 262, 264–265 Loss given default, 45, 90, 137–179 Loss in the event of default, 157, 303 Low default portfolio, 124–125 LTV (see loan to value ration) Mapping process, 33, 51, 225, 230 Marginal risk contribution (MRC), 266, 311 Market value of assets (MVA), 96 Markov, 112, 114 Master scale, 213 Maturity adjustment, 128, 332–334 Maximum likelihood estimation, 172 MDA (see Multiple discriminant analysis) Mean square error (MSE), 196 Micro Finance Institute (MFI)s rating model, 42 Migration, 114–115 MKMV (see KMV) MLE (see Maximum likelihood estimation) Model design, 188, 191, 230 Model development, 191–192, 194, 197, 200, 203 Model governance, 186–188 Model stability, 221 Model validation, xxii, 125, 186 Moody’s, 45, 49, 55, 84, 886, 102 Moral hazard, 97 Mortality analysis, 112, 125 Mortgage loans, 186, 351 Index   359 Multinomial logistic regression, 48 Multiple discriminant analysis, 53, 72 Multivariate analysis, 72 NCAF (see New capital adequacy framework) New capital adequacy framework (NCAF), 318 Newton Raphson algorithm, 89 Non bank financial cos (NBFCs), 266 Non fund based facilities, 148–149 Non investment grade (NIG), 49, 121–123, 129 Non Performing Assets, 3, 6, 9, 187 Normal distribution, 8, 88, 90–94, 172, 330 NPA (see Non Performing Assets) Obligor rating, 36, 180 Off balance sheet exposures, 36, 180 One dimensional rating, 36 Other retail, 168, 328, 331 Over the counter derivatives (OTC), 177, 344, 349 Overdraft (OD), 147 Packing credit (PC), 145–147 Payoff function, 86 Percentile, 80, 266, 277–280 Pivot table, 116–117 Pluto and Tasche, 125 Point in time (PIT) rating, 51 Pooled PD, 129–132 Portfolio diversification, 5, 11, 117, 179 Portfolio optimization, 306–307 Portfolio risk, 284–285, 293 Power curve, 201–209 Present value, 85, 90, 154–155, 157– 158 Probability of default (PD), 25–26, 29, 36, 111–133 Pro-cyclicality, 150, 221–222, 289– 291, 324 Project finance (PF), 38–40 Prudential limits, 12, 14, 18, 271, 299 Qualifying criteria, 213, 328 Qualifying revolving retail exposures (QRRE), 334 Qualitative factors, 27–28, 42, 46 Qualitative validation, 190–196 Quantile function, 329–330 Quantitative factors, 46, 74, 71, 77 Quantitative validation, 189–190, 196–197 Rank correlation, 200 RAROC (see Risk adjusted return on capital) RARORAC (see Risk adjusted return on risk adjusted capital) Rating history, 117, 203, 246 Rating models, 209–210, 216, 221 Rating sheet, 32 RBI (see Reserve Bank of India) RCAP (see Regulatory consistency assessment programme) Receiver operating characteristic (ROC), 208–209 Recovery rating, 49–50, 180 Recovery, 175–176, 178 Reduced form models, 32, 101–102 Regression model, 31, 48, 57, 63, 151, 172, 175 Regulatory arbitrage, 323 Regulatory capital, 289–300 Regulatory compliance, 6–7, 26, 262, 312 Regulatory consistency assessment programme, 348 Regulatory retail, 328 Required capital, 11, 291, 324–325 Reserve Bank of India, 5, 19, 166, 289, 318–319, 344, 349 Residential mortgage, 331, 333, 335 360 | Index Residential real estate (RRE), 169, 349, 340 Retail pooling, 328, 350 Retail scoring models, 74–81 Return on equity (ROE), 301 Return on risk weighted assets (RORWA), 347 Revised standardized approach, 349 Revolving credit exposure, 331 Risk adjusted performance measurement (RAPM), 311 Risk adjusted return on capital (RAROC), 276, 299 Risk adjusted return on risk adjusted capital (RARORAC), 301 Risk appetite, 15, 284, 342–343 Risk assessment model (RAM), 31 Risk based pricing, 7, 12 Risk capital, 11, 265–266 Risk contribution, 239–242 Risk differentiation, 49–52 Risk factors, 75 Risk management department (RMD), 15 Risk modelling, 16, 20 Risk neutral EDF, 90–91, 96, 100 Risk reporting, 21 Risk vision, Risk weight, 40–43, 149, 151, 321 Risk weighted assets (RWA), 9, 330, 339 RMD (see Risk management department) Robustness, 196–197, 221 RORWA (see Risk adjusted return on risk weighted assets) Scenario analysis, 90, 221–223 Shareholder value, 112, 276, 301–303 Significance, 79 Simulation methods, 280, 284 Size adjustment, 324, 332–334 Skewness, 51, 266 Slippage, 268 Slope adjustment, 332 Small and medium enterprises (SME), 14, 41 Solvency, 53, 55 Solver, 89, 205 Sovereign, 332–333 Spearman’s rank correlation, 200 SREP (see Supervisory Review Process) Standard & Poor (S&P), 45, 49 Standard deviation, 46, 89, 90, 98 Standardized Approach, 19, 139 Statistical scoring model, 31, 53, 67– 68 Stress testing, 186–229 Stressed PD, 226 Structural model, 83, 85, 100–101 Subprime crisis, 6, 186 Supervisory Review Process, 323 Survival probability, 127 Systematic risk, 171, 241, 250, 256, 260 Template, 307 Term loan, 3, 138–139 Theil entropy, 264 Through the cycle (TTC) rating, 124 Tier capital, Tier capital, 11, 318 Tobit model, 172–173 Top 20 borrower limits, 269–270 Trading book, 344 Transition matrix analysis, xxi T-statistic, 199, 264 Two dimensional rating, 36 Two tier rating system, 35–37 Type I error rate, 200 Type II error rate, 200 Unexpected loss (UL), 26, 235–236, 272, 303, 325 Index   361 Unexpected loss contribution (ULC), 240 Unsecured loans, 159, 166, 339 Usage given default (UGD), 139–140 Use test, 192–193 Utilization of limits, 143 Validation principles, 231 Value at risk (VaR), 20, 277, 279, 329 Variable selection, 71 Vicious cycle, Wilk’s lambda, 53, 198 Wilcoxon rank-sum test, Whitney rank-sum tests, 169, 217 Working capital demand loan (WCDL), 146, 148 Working capital, 24, 26, 45, 54 Z score, 52–53 ... fulfilled Credit risk exists as long as banks lend money Credit risk is not confined to the risk that borrowers are unable to 2  |  Managing Portfolio Credit Risk in Banks pay; it also includes the risk. .. the balance sheet Banks are also increasingly facing credit risk in various financial instruments other than loans, including acceptances, interbank transactions, trade financing, foreign exchange... challenges for banks in Indian measuring and managing credit risk in the backdrop of global financial crisis and recent macroeconomic scenario This chapter also reviews banks existing internal risk management