Credit Risk讲稿

单击此处编辑母版标题样式,单击此处编辑母版文本样式,第二级,第三级,第四级,第五级,*,*,*,CREDIT RISK,Credit Risk Modeling,Measuring Credit Risk: Overview,Credit Migration Approach:,CreditMetrics,(from J.P. Morgan),CreditVaR,(CIBC),CreditPortfolioView,(McKinsey),The Option Pricing Approach:,KMV (from KMV Corp.),What are the current proposed industry sponsored Credit,VaR,methodologies?,3,What are the current proposed industry sponsored Credit,VaR,methodologies?,Measuring Credit Risk: Overview,The Actuarial Approach:,CreditRisk,+ (from Credit Suisse First Boston),The Reduced Form Approach:,Jarrow,/Turnbull,Duffie,/Singleton,4,Credit migration approach,Contingent claim,approach,Actuarial approach,Reduced form approach,Software,CreditMetrics,CreditPortfolioView,KMV,CreditRisk,+,Kamakura,Definition of,Risk,D,Market Value,Market Value,Default losses,Default losses,Default losses,Credit events,Downgrade/Default,Downgrade/Default,Continuous default,probabilities,Default,Default,Risk drivers,Asset Values,Macro-factors,Asset Values,Expected default,rates,Hazard rate,Transition,probabilities,Constant,Driven by Macro,factors,Driven by:,-,Individual term,structure of EDF,-,Asset value process,N/A,N/A,Correlation of,credit events,Standard,multivariate normal,distribution (equity-,factor model),Conditional default,probabilities as,functions of macro-,factors,Standard,multivariate normal,asset returns (asset,factor model),Conditional,default,probabilities as,functions of,common risk,factors,Conditional default,probabilities as,functions of macro-,factors,Recovery rates,Random (Beta,distribution,Random (empirical,distribution),Random (Beta,distribution),Loss given default,deterministic,Loss given default,deterministic,Numerical,approach,Simulation/Analytic,Simulation,Analytic/Simulation,Analytic,Tree based /simulation,D,Comparison of Models,5,Measuring Credit Risk: Overview,Credit risk models should capture:,Spread risk,Downgrade risk,Default risk,Recovery rate risk,Concentration risk (portfolio diversification and correlation risk),6,Measuring Credit Risk: Overview,Credit risk models generate:,Loss distribution (default risk),KMV,CreditRisk,+,Portfolio value distribution (migration and default risks),CreditMetrics,CreditVaR,CreditPortfolioView,7,Measuring Credit Risk: Overview,Typical market returns,Typical credit returns,Portfolio Value,Source: CIBC,Comparison of the distributions of credit returns and market returns,Frequency,8,Measuring Credit Risk: Overview,Key input parameters common to all models,obligors information,exposures,recovery rate (loss given default: LGD),default correlations (concentration risk),9,IIThe Credit Migration Approach,Credit Migration Approach,Key input parameters:,Credit data:,Credit horizon,Credit rating system: Moodys, S&Ps, internal,Transition matrix,11,Credit Migration Approach,Key input parameters:,Market data,Yield curve (base curve),Spread curve for each rating,FX rates,Correlations between market indices,12,Credit Migration Approach,Key input parameters:,Obligor data:,Credit rating,Country weights,Industry weights,Idiosyncratic standard deviations,13,Credit Migration Approach,Key input parameters:,Issue (facility) data:,Instrument type: fixed coupon bond/loan, FRN, interest rate swap, loan commitment, letter of credit, credit derivative,Recovery rate (1-LGD) and LGD standard deviation,Usage given default (UGD),14,Credit Migration Approach: One Bond,Example:,Credit,VaR,for a senior unsecured BBB rated bond maturing exactly in 5 years, and paying an annual coupon of 6%.,15,Credit Migration Approach:For a Bond,Transition matrix: probabilities of credit rating migrating from one rating quality to another, within one year.,Initial,Rating at year-end (%),Rating,AAA,AA,A,BBB,BB,B,CCC,Default,AAA,90.81,8.33,0.68,0.06,0.12,0,0,0,AA,0.70,90.65,7.79,0.64,0.06,0.14,0.02,0,A,0.09,2.27,91.05,5.52,0.74,0.26,0.01,0.06,BBB,0.02,0.33,5.95,86.93,5.30,1.17,1.12,0.18,BB,0.03,0.14,0.67,7.73,80.53,8.84,1.00,1.06,B,0,0.11,0.24,0.43,6.48,83.46,4.07,5.20,CCC,0.22,0,0.22,1.30,2.38,11.24,64.86,19.79,Source: Standard & Poors,CreditWeek,(April 15, 1996),Step 1: Credit horizon,Step 2: Specify the credit rating system,Step 3: Specify the transition matrix,16,Credit Migration Approach:For a Bond,Step 4: Specify the spread curve,One year forward zero curves for each credit rating (%),Category,Year 1,Year 2,Year 3,Year 4,AAA,3.60,4.17,4.73,5.12,AA,3.65,4.22,4.78,5.17,A,3.72,4.32,4.93,5.32,BBB,4.10,4.67,5.25,5.63,BB,5.55,6.02,6.78,7.27,B,6.05,7.02,8.03,8.52,CCC,15.05,15.02,14.03,13.52,Source:,CreditMetrics, J.P. Morgan,17,Credit Migration Approach:For a Bond,Seniority Class,Mean (%),Standard Deviation (%),Senior Secured,53.80,26.86,Senior Unsecured,51.13,25.45,Senior subordinated,38.52,23.81,Subordinated,32.74,20.18,Junior subordinated,17.09,10.90,Source:,Carty,& Lieberman 1996,Recovery rates by seniority class (% of face value, i.e., “par”),Step 5: Specify the recovery rate,18,Credit Migration Approach:For a Bond,0,1,2,3,4,5,Time,Cash flows,106,6,6,6,6,(,Forward price = 107.55),V,BBB,0525,55,.,107,),0563,.,1,(,106,),.,1,(,6,),0467,.,1,(,6,041,.,1,6,6,4,3,2,=,+,+,+,+,=,BBB,V,Step 6: Specify the forward pricing model,19,Credit Migration Approach:For a Bond,Year-end rating,Value ($),AAA,109.37,AA,109.19,A,108.66,BBB,107.55,BB,102.02,B,98.10,CCC,83.64,Default,51.13,Source:,CreditMetrics, J.P. Morgan,One year forward values for a BBB bond,20,Credit Migration Approach:For a Bond,Year-end,rating,Probability,of state:,p(%),Forward,price: V ($),Change in,value,:,D,V,($),AAA,0.02,109.37,1.82,AA,0.33,109.19,1.64,A,5.95,108.66,1.11,BBB,86.93,107.55,0,BB,5.30,102.02,-5.53,B,1.17,98.10,-9.45,CCC,0.12,83.64,-23.91,Default,0.18,51.13,-56.42,Source:,CreditMetrics, J.P. Morgan,Step 7: Derive the forward distribution of the changes in portfolio value,Distribution of the bond values, and changes in value of a BBB bond, in one year.,21,Credit Migration Approach:For a Bond,First percentile = -23.91,First percentile, assuming normality = - 7.43,Default,51.13,83.64,98.10,-23.91,-56.42,CCC,B,0,86.93,BB,.02,.33,1.17,5.30,5.95,Frequency,BBB,107.55,109.37,1.82,AAA,A,Probability,of State,(%),Forward Price: V,Change in value: V,D,-9.45,-5.53,102.2,.,.,AA,22,Credit Migration Approach: For a Bond/Loan Portfolio,Obligor #2 (single-A),Obligor #1,AAA,AA,A,BBB,BB,B,CCC,Default,(,BB),0.09,2.27,91.05,5.52,0.74,0.26,0.01,0.06,AAA,0.03,0.00,0.00,0.03,0.00,0.00,0.00,0.00,0.00,AA,0.14,0.00,0.00,0.13,0.01,0.00,0.00,0.00,0.00,A,0.67,0.00,0.02,0.61,0.40,0.00,0.00,0.00,0.00,BBB,7.73,0.01,0.18,7.04,0.43,0.06,0.02,0.00,0.00,BB,80.53,0.07,1.83,73.32,4.45,0.60,0.20,0.01,0.05,B,8.84,0.01,0.20,8.05,0.49,0.07,0.02,0.00,0.00,CCC,1.00,0.00,0.02,0.91,0.06,0.01,0.00,0.00,0.00,Default,1.06,0.00,0.02,0.97,0.06,0.01,0.00,0.00,0.00,Joint migration probabilities (%) with zero correlation for 2 issuers rated BB and A,23,Credit Migration Approach: For a Bond/Loan Portfolio,Joint migration probabilities when asset returns are correlated involves 3 steps:,Step 8: Estimate asset return correlations,Step 9: Assume that the joint normalized returndistribution is,bivariate,normal,Note: Equity returns are typically used as a proxy for asset returns.,24,Credit Migration Approach:,For a Bond/Loan Portfolio,Step 10:Derive the credit quality thresholds for each credit rating,Default,B,AA,CCC,AAA,BBB,Firm remains BB,A,1.06,1.00,8.84,80.53,7.73,0.67,0.14,0.03,Prob,(%):,Z-threshold(s),-2.30,-2.04,-1.23,1.37,2.39,2.93,3.43,Standard normal distribution for a BB-rated firm,Rating:,Z,ccc,Z,B,Z,BB,Z,BBB,Z,AAA,Z,A,Z,AA,25,Transition probabilities and credit quality thresholds for rated BB and A obligors,Credit Migration Approach: Bond/Loan portfolio,Rated-A obligor,Rated-BB obligor,Rating in one,year,Probabilities,(%),Thresholds,Z,(,s),Probabilities,(%),Thresholds,Z,(,s),AAA,0.09,3.12,0.03,3.43,AA,2.27,1.98,0.14,2.93,A,91.05,-1.51,0.67,2.39,BBB,5.52,-2.30,7.73,1.37,BB,0.74,-2.72,80.53,-1.23,B,0.26,-3.19,8.84,-2.04,CCC,0.01,-3.24,1.00,-2.30,Default,0.06,1.06,26,Credit Migration Approach: For a Bond/Loan Portfolio,Step 11:Calculation of the joint rating probabilities,Joint rating probabilities (%) for BB and A rated obligors when correlation between asset returns is 20%.,Rating of first company (BB),Rating of second company (A),AAA,AA,A,BBB,BB,B,CCC,Def,Total,AAA,0.00,0.00,0.03,0.00,0.00,0.00,0.00,0.00,0.03,AA,0.00,0.01,0.13,0.00,0.00,0.00,0.00,0.00,0.14,A,0.00,0.04,0.61,0.01,0.00,0.00,0.00,0.00,0.67,BBB,0.02,0.35,7.10,0.20,0.02,0.01,0.00,0.00,7.73,73.65,4.24,0.56,0.18,B,0.00,0.08,7.80,0.79,0.13,0.05,0.00,0.01,8.84,CCC,0.00,0.01,0.85,0.11,0.02,0.01,0.00,0.00,1.00,Def,0.00,0.01,0.90,0.13,0.02,0.01,0.00,0.00,1.06,Total,0.09,2.27,91.05,5.52,0.74,0.26,0.01,0.06,100,0.01,0.04,80.53,BB,0.07,1.79,27,Step 12:Probability of joint defaults,Probability of joint defaults as a function of asset return correlation,0,0.02,0.04,0.06,0,Source:,CreditMetrics, J.P. Morgan,0.2,0.4,0.6,0.8,1.0,Correlation,Joint default probability,Credit Migration Approach: Bond/Loan portfolio,28,Practical implementation: Monte-Carlo simulation,Credit Migration ApproachFor a Bond/Loan Portfolio,Input:,Derivation of the asset return thresholds for each rating category (Step 10),Estimation of the correlation between each pair of obligors asset returns,29,Credit Migration Approach: Implementation,Multifactor equity model (,CreditVar,and Equity Market,VaR,),Regression model for stock returns (Equity Market,VaR,model):,R - stock return,R,i,- country/industry index return,e,- residual (,E,(,e,) = 0),Same model in terms of standardized returns is used in,CreditVar,:,30,Credit Migration Approach: Implementation,31,Credit Migration Approach: Implementation,Correlation between two obligors B and C,32,Credit Migration Approach: Implementation,Monte Carlo simulation:,Generation of asset return scenarios according to their joint normal distribution. Each scenario is characterized by n standardized asset returns, one for each of the n obligors in the portfolio. (Step 9),For each scenario, and for each obligor, the standardized asset return is mapped into the corresponding rating, according to the threshold levels derived in Step 1,r,1,r,n,Obligor 1 credit rating,Obligor n credit rating,Scenario,r,1,r,2,.,.,.,.,r,n,Correlation,matrix,C, f(r,1,r,n,C,) ,Joint density function,33,Monte Carlo simulation:,Credit Migration Approach : Implementation,Given the spread curves which apply for each rating, the portfolio is revalued. (Steps 6 & 7),CreditVaR,allows for risk analysis of complicated portfolios of different instruments: fixed coupon bonds, floating rate notes, swaps, loan commitments, etc.,34,Credit Migration Approach : Implementation,Repeat the procedure a large number of times, say 100,000 times, and plot the distribution of the portfolio values to obtain a graph which looks like Figure 2. (Steps 3-5),Monte Carlo simulation:,35,Credit Migration Approach: Implementation,Results:,Derive the percentiles of the distribution of the future values of the portfolio.,Practical implementation: Monte-Carlo simulation,36,Expected Loss,20000,30000,40000,50000,Oct-99,Nov-99,Dec-99,Jan-00,Feb-00,Mar-00,Apr-00,Period,VaR,(1,000 US$),99.865%,VaR,75000,100000,125000,150000,175000,200000,Oct-,99,Nov-,99,Dec-,99,Jan-,00,Feb-,00,Mar-,00,Apr-,00,Period,VaR,(1,000 US$),99.865%,VaR,Upper 95% bound,Lower 95% bound,EVaR,99%,VaR,50000,75000,100000,125000,150000,175000,200000,Oct-99,Nov-,99,Dec-,99,Jan-00,Feb-,00,Mar-,00,Apr-00,Period,VaR,(1,000 US$),10,Hot Exposures (,DeltaVaR,),#,Exposure ID,Obligor Name,Commitment,Drawn,Maturity,Type,1,AUDCORPFIS1A,CADCORPGNRL2A,98,431,493,50%,01-,Jul-01,Revolver,2,460,787,1.8%,2,AUDCORPFOD1A,CADCORPGNRL3A,90,441,897,20%,01-,Jul-01,Revolver,1,808,838,1.3%,3,AUDCORPGNRL1A,CADCORPGNRL4A,28,008,962,40%,01-,Jul-01,Revolver,1,120,358,0.8%,4,CADCORPAUT1A,CADCORPGNRL5A,9,443,021,100%,01-,Jul-01,Revolver,944,302,0.7%,5,AUDCORPCHM1A,CADCORPGNRL1A,15,845,070,35%,01-,Jul-01,Revolver,554,577,0.4%,6,CADCORPCHM1A,CADCORPGNRL6A,4,852,980,100%,01-,Jul-01,Revolver,485,298,0.3%,7,CADCORPFIS1A,CADCORPBFIN1A,4,441,180,100%,01-,Jul-01,Revolver,444,118,0.3%,8,CADCORPELQ1A,CADCORPGNRL7A,4,852,980,73%,01-,Jul-01,Revolver,354,268,0.3%,9,CADCORPFOD1A,CADCORPBFIN2A,2,941,200,100%,01-,Jul-01,Revolver,294,120,0.2%,10,CADCORPGNRL1A,CADCORPBFIN3A,2,500,020,100%,01-,Jul-01,Revolver,250,002,0.2%,Risk Contribution,$ %,Sensitivity Analysis,Normal,Worst case,transition,matrix,Asset,Corr,.,= 0,Asset,Corr,.,= 1,Idiosync,.,-10%,Recovery,-10%,Double,Spreads,99.865%,VaR,139,000,000,342,482,783,75,737,521,424,230,768,182,107,577,156,264,993,149,636,030,99%,VaR,90,000,000,144,465,513,61,209,225,285,109,647,115,357,810,100,689,765,95,138,106,Expected Loss,38,000,000,93,628,387,20,705,222,115,976,757,49,784,805,42,719,926,40,907,692,10,Hot Obligors (,DeltaVaR,),Obligor Name,Credit Rating,Notional,1,CADCORPGNRL1A,6,126,658,646,8,950,228,6%,2,CADCORPGNRL2A,7,126,658,646,8,107,078,6%,3,CADCORPBFIN1A,6,120,400,000,7,921,545,6%,4,CADCORPGNRL7A,7,120,869,023,6,479,533,5%,5,CADCORPGNRL6A,6,117,862,804,5,914,433,4%,6,CADCORPGNRL4A,6,122,611,887,5,489,519,4%,7,CADCORPBFIN3A,7,124,780,560,5,375,374,4%,8,CADCORPGNRL3A,6.5,122,224,360,5,319,663,4%,9,CADCORPGNRL5A,7,120,869,023,4,569,089,3%,10,CADCORPBFIN2A,6,120,400,000,2,906,935,2%,Risk Contribution,$ %,#,99%,VaR,Upper 5% bound,Lower 95% bound,EVaR,37,对上述模型的评论：,creditmetrics,优点：,这种方法首次将受险价值（,VAR,）的方法运用到信用风险度量管理上，,利用信用转移矩阵并根据不同信用等级下的贴现率就可以计算出信用,工具的市场价值从而得到该信用工具在不同信用风险状态下的概率分布，,达到用传统的期望和标准差来度量信用风险的目的。,38,对上述模型的评论：,creditmetrics,（,2,）,缺点：,1),模型中违约率直接取自历史数据平均值,但实证研究表明,违约率与宏观经济状况有直接关系,不是固定不变的,在经济,高速增长阶段,违约率较低,;,而在经济衰退时期,违约率则很高。,2),模型假定资产收益服从正态分布,它是进行模拟的基础,但资,产收益的实际分布有待进一步研究。,3),模型中假定企业资产收益之间的相关度等于公司证券收益,之间的相关度,该假设有待进一步验证,模型计算结果对于这一,假定的敏感性很高。,4),模型中假定无风险利率是固定不变的,影响投资组合价值的,只有各种信用事件,市场风险对于投资组合价值没有影响。,39,对上述模型的评论：,KMV,优点：,首先，该模型可充分地利用资本市场信息，,对所有公开上市企业进行信用风险的量化度量和分析。,其次，由于这种方法所获得的数据来自股票市场的资料，,而非企业历史账面价值，因此更能反映企业当前的信用状况。,再次，预期违约频率指标在本质上是一种对风险的基数衡量法，,从而可以反映风险水平差异的程度，因而更准确,40,对上述模型的评论：,KMV(2),缺点：,首先，它只适用于上市公司的信用风险评估，,对非上市公司则要借助某些会计信息,或其他能反映借款企业特征值的指标来替代模型中一些变量。,其次，该模型假定借款企业的资产价值呈正态分布。,再次，该模型不能够对长期债务的不同类型进行分辨。但实际上，,可以依据其优先偿还顺序、有否担保、有否契约、能否转换,等来区别不同的长期债务，因而导致违约点,DP,的不确定。,最后，该模型属于静态模型，因为,KMV,模型基础的默顿期权模型是假设：,借款企业管理层一旦将企业的债务结构确定下来之后，,该企业的这一结构就不变化了。,41,Internal Ratings-Based approach,42,Changing Regulatory Environment,1988Regulators recognized need for risk-based Capital for Credit Risk (Basel Accord),1995Capital Regulations for Market Risk Published,1996-98Capital Regulations for Credit Derivatives,1997Discussion of using credit risk models for selected portfolios in the banking books,1999New Credit Risk Recommendations, Bucket Approach - External and Possibly Internal Ratings, Expected Final Recommendations by Fall 2001, Postpone Internal Models (Portfolio Approach),2001Revised Basel Guidelines, Revised Buckets - Still Same Problems, Foundation and Advanced Internal Models, Final Guidelines Expected in Fall 2002 - Implemented by 2005,43,Capital Adequacy Risk Weights from Various BIS Accords(,Corporate Assets Only),Original 1988 Accord,All Ratings 100% of Minimum Capital (e.g. 8%),1999 (June) Consultative BIS Proposal,Rating/Weight,AAA to AA- A+ to B-Below B-Unrated,20%,100%,150%,100%,2001 (January) Consultative BIS Proposal,AAA to AA-A+ to A-BBB+ to BB-Below BB-Unrated,20%,50%,100%,150%,100%,Altman/Saunders Proposal (2000,2001),AAA to AA-A+ to BBB-BB+ to B-Below B-Unrated,10%,30%,100%,150%,Internally Based Approach,44,The Importance of Credit Ratings,For Risk Management in General,Greater Understanding Between Borrowers and Lenders,Trade off between risk and return,45,Rating Systems,Bond Rating Agency Systems,US (3) - Moodys, S&P (20+ Notches), Fitch/IBCA,Bank Rating Systems,1 9, A F, Ratings since 1995,Office of Controller of Currency System,Pass (0%), Substandard (20%), Doubtful (50%), Loss (100%),NAIC (Insurance Agency),1 6,Local Rating Systems,Three (Japan),SERASA (Brazil),RAM (Malaysia),New Zealand (NEW),etc.,46,Debt Ratings,47,S&PS Debt rating process,Request,Rating,Assign,analytical,team,Conduct,basic,research,Meet,issuer,Rating,Committee,Meeting,Issue,Rating,Appeals,Process,48,Moodys rating analysis of an industrial company,Rating process includes quantitative, qualitative and legal analyses,Quantitative analysis is mainly based on the firms financial reports,Qualitative analysis is concerned with management quality, reviews the firms competitive situation as well as an assessment of expected growth within the firms industry plus the vulnerability to technological changes, regulatory changes, labor relations, etc,49,Issue,Company,Structure,Operating /,Financial Position,Management,Quality,Industry /,Regulatory Trends,Sovreign / macroeconomic,Analysis,50,Scoring Systems,Qualitative (Subjective),Univariate (Accounting/Market Measures),Multivariate (Accounting/Market Measures),Discriminant, Logit, Probit Models (Linear, Quadratic),Non-Linear Models (e.g., RPA, NN),Discriminant and Logit Models in Use,Consumer Models - Fair Isaacs,Z-Score (5) - Manufacturing,ZETA Score (7) - Industrials,Private Firm Models (,eg,. Risk Calc (Moodys), Z” Score),EM Score (4) - Emerging Markets, Industrial,Other - Bank Specialized Systems,51,Scoring Systems,(continued),Artificial Intelligence Systems,Expert Systems,Neural Networks (,eg,. Credit Model (S&P), CBI (Italy),Option/Contingent Models,Risk of Ruin,KMV Credit Monitor Model,52,Basic Architecture of an Internal Ratings-Based (IRB) Approach to Capital,In order to become eligible for the IRB approach, a bank would first need to demonstrate that its internal rating system and processes are in accordance with the minimum standards and sound practice guidelines which will be set forward by the Basel Committee.,The bank would furthermore need to provide to supervisors exposure amounts and estimates of some or all of the key loss statistics associated with these exposures, such as Probability of Default (PD), by internal rating grade (Foundation Approach).,Based on the banks estimate of the probability of default, as well as the estimates of the loss given default (LGD) and maturity of loan, a banks exposures would be assigned to capital “buckets” (Advanced Approach). Each bucket would have an associated risk weight that incorporates the expected (up to 1.25%) and unexpected loss associated with estimates of PD and LGD, and possibly other risk characteristics.,53,Recent (2001) Basel Credit Risk Management Recommendations,May establish two-tier system for banks for use of internal rating systems to set regulatory capital. Ones that can set loss given default estimates, OR,Banks that can only calculate,default,probability,may do so and have,loss,(recovery) probability estimates provided by regulators.,Revised plan (January 2001) provides substantial guidance for banks and regulators on what Basel Committee considers as a strong, best practice risk rating system.,Preliminary indications are that a large number of banks will attempt to have their internal rating system accepted.,Basel Committee working to develop capital charge for operational risk. May not complete this work in time for revised capital rules.,Next round of recommendations to take effect in 2004.,54,Risk Weights for Sovereign and Banks(Based on January 2001 BIS Proposal),Sovereigns,Credit Assessment AAA A+ BBB+BB+Below,of Sovereignto AA-to A-to BBB-to B- B- Unrated,Sovereign risk,weights 0%20% 50%100% 150%