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Credit Risk Management in Banks: Hard Information, Soft Information and Manipulation

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Credit Risk Management in Banks: Hard Information, Soft Information and Manipulation

  1. 1. Background, Motivation & Aim Literature Survey Model Results Discussion Credit Risk Management in BanksHard Information, Soft Information and Manipulation B. Godbillon-Camus and C.J. Godlewski Institut d’Etudes Politiques Universit´ Robert Schuman e Strasbourg 3 EFMA 2006 Annual Conference 28 June - 1 July Universidade Complutense, Madrid, Spain Godbillon-Camus and Godlewski Credit Risk Management and Information 1/ 24
  2. 2. Background, Motivation & Aim Literature Survey Model Results DiscussionOutline 1 Background, Motivation & Aim 2 Literature Survey 3 Model 4 Results 5 Discussion Godbillon-Camus and Godlewski Credit Risk Management and Information 2/ 24
  3. 3. Background, Motivation & Aim Literature Survey Model Results DiscussionBackground, Motivation & aim Efficient information treatment is crucial for the banking industry (Fama, 1985) ⇒ credit risk management process Recent distinction of information’s type produced and treated by banks Hard versus Soft Information (Petersen, 2004) Different lending technologies : Transaction Lending versus Relationship Lending Organizational structure adapted to information’s type (Stein, 2002; Takats, 2004) AIM : Investigate the influence of information’s type on bank’s risk taking behaviour ⇒ principal-agent model with moral hazard with hidden information Godbillon-Camus and Godlewski Credit Risk Management and Information 3/ 24
  4. 4. Background, Motivation & Aim Literature Survey Model Results DiscussionBackground, Motivation & aim Efficient information treatment is crucial for the banking industry (Fama, 1985) ⇒ credit risk management process Recent distinction of information’s type produced and treated by banks Hard versus Soft Information (Petersen, 2004) Different lending technologies : Transaction Lending versus Relationship Lending Organizational structure adapted to information’s type (Stein, 2002; Takats, 2004) AIM : Investigate the influence of information’s type on bank’s risk taking behaviour ⇒ principal-agent model with moral hazard with hidden information Godbillon-Camus and Godlewski Credit Risk Management and Information 3/ 24
  5. 5. Background, Motivation & Aim Literature Survey Model Results DiscussionBackground, Motivation & aim Efficient information treatment is crucial for the banking industry (Fama, 1985) ⇒ credit risk management process Recent distinction of information’s type produced and treated by banks Hard versus Soft Information (Petersen, 2004) Different lending technologies : Transaction Lending versus Relationship Lending Organizational structure adapted to information’s type (Stein, 2002; Takats, 2004) AIM : Investigate the influence of information’s type on bank’s risk taking behaviour ⇒ principal-agent model with moral hazard with hidden information Godbillon-Camus and Godlewski Credit Risk Management and Information 3/ 24
  6. 6. Background, Motivation & Aim Literature Survey Model Results DiscussionBackground, Motivation & aim Efficient information treatment is crucial for the banking industry (Fama, 1985) ⇒ credit risk management process Recent distinction of information’s type produced and treated by banks Hard versus Soft Information (Petersen, 2004) Different lending technologies : Transaction Lending versus Relationship Lending Organizational structure adapted to information’s type (Stein, 2002; Takats, 2004) AIM : Investigate the influence of information’s type on bank’s risk taking behaviour ⇒ principal-agent model with moral hazard with hidden information Godbillon-Camus and Godlewski Credit Risk Management and Information 3/ 24
  7. 7. Background, Motivation & Aim Literature Survey Model Results DiscussionBackground, Motivation & aim Efficient information treatment is crucial for the banking industry (Fama, 1985) ⇒ credit risk management process Recent distinction of information’s type produced and treated by banks Hard versus Soft Information (Petersen, 2004) Different lending technologies : Transaction Lending versus Relationship Lending Organizational structure adapted to information’s type (Stein, 2002; Takats, 2004) AIM : Investigate the influence of information’s type on bank’s risk taking behaviour ⇒ principal-agent model with moral hazard with hidden information Godbillon-Camus and Godlewski Credit Risk Management and Information 3/ 24
  8. 8. Background, Motivation & Aim Literature Survey Model Results DiscussionBackground, Motivation & aim Efficient information treatment is crucial for the banking industry (Fama, 1985) ⇒ credit risk management process Recent distinction of information’s type produced and treated by banks Hard versus Soft Information (Petersen, 2004) Different lending technologies : Transaction Lending versus Relationship Lending Organizational structure adapted to information’s type (Stein, 2002; Takats, 2004) AIM : Investigate the influence of information’s type on bank’s risk taking behaviour ⇒ principal-agent model with moral hazard with hidden information Godbillon-Camus and Godlewski Credit Risk Management and Information 3/ 24
  9. 9. Background, Motivation & Aim Hard versus Soft Information Literature Survey Pros and Cons Model Impact of Soft Information on Default’s Risk Prediction Results Organizational Structure and Information DiscussionLiterature SurveyHard versus Soft Information “Hard information (. . . ) is when everyone agrees on its meaning. (. . . ) Honest disagreements arise when two people perfectly observe information yet interpret this information differently (i.e. soft information)” (Kirschenheiter, 2002) Nature : quantitative vs qualitative (numbers vs words) / backward versus forward looking Collecting method : impersonal vs personal (production’s context, role of the agent responsible for the production and treatment process) Cognitive factors : weakly present vs strongly present (subjective judgment, interpretation and perception, opinions . . . ) Lending technology : transaction lending vs relationship lending Organizational structure : centralized and hierarchical vs decentralized and non-hierarchical Godbillon-Camus and Godlewski Credit Risk Management and Information 4/ 24
  10. 10. Background, Motivation & Aim Hard versus Soft Information Literature Survey Pros and Cons Model Impact of Soft Information on Default’s Risk Prediction Results Organizational Structure and Information DiscussionLiterature SurveyHard versus Soft Information “Hard information (. . . ) is when everyone agrees on its meaning. (. . . ) Honest disagreements arise when two people perfectly observe information yet interpret this information differently (i.e. soft information)” (Kirschenheiter, 2002) Nature : quantitative vs qualitative (numbers vs words) / backward versus forward looking Collecting method : impersonal vs personal (production’s context, role of the agent responsible for the production and treatment process) Cognitive factors : weakly present vs strongly present (subjective judgment, interpretation and perception, opinions . . . ) Lending technology : transaction lending vs relationship lending Organizational structure : centralized and hierarchical vs decentralized and non-hierarchical Godbillon-Camus and Godlewski Credit Risk Management and Information 4/ 24
  11. 11. Background, Motivation & Aim Hard versus Soft Information Literature Survey Pros and Cons Model Impact of Soft Information on Default’s Risk Prediction Results Organizational Structure and Information DiscussionLiterature SurveyHard versus Soft Information “Hard information (. . . ) is when everyone agrees on its meaning. (. . . ) Honest disagreements arise when two people perfectly observe information yet interpret this information differently (i.e. soft information)” (Kirschenheiter, 2002) Nature : quantitative vs qualitative (numbers vs words) / backward versus forward looking Collecting method : impersonal vs personal (production’s context, role of the agent responsible for the production and treatment process) Cognitive factors : weakly present vs strongly present (subjective judgment, interpretation and perception, opinions . . . ) Lending technology : transaction lending vs relationship lending Organizational structure : centralized and hierarchical vs decentralized and non-hierarchical Godbillon-Camus and Godlewski Credit Risk Management and Information 4/ 24
  12. 12. Background, Motivation & Aim Hard versus Soft Information Literature Survey Pros and Cons Model Impact of Soft Information on Default’s Risk Prediction Results Organizational Structure and Information DiscussionLiterature SurveyHard versus Soft Information “Hard information (. . . ) is when everyone agrees on its meaning. (. . . ) Honest disagreements arise when two people perfectly observe information yet interpret this information differently (i.e. soft information)” (Kirschenheiter, 2002) Nature : quantitative vs qualitative (numbers vs words) / backward versus forward looking Collecting method : impersonal vs personal (production’s context, role of the agent responsible for the production and treatment process) Cognitive factors : weakly present vs strongly present (subjective judgment, interpretation and perception, opinions . . . ) Lending technology : transaction lending vs relationship lending Organizational structure : centralized and hierarchical vs decentralized and non-hierarchical Godbillon-Camus and Godlewski Credit Risk Management and Information 4/ 24
  13. 13. Background, Motivation & Aim Hard versus Soft Information Literature Survey Pros and Cons Model Impact of Soft Information on Default’s Risk Prediction Results Organizational Structure and Information DiscussionLiterature SurveyHard versus Soft Information “Hard information (. . . ) is when everyone agrees on its meaning. (. . . ) Honest disagreements arise when two people perfectly observe information yet interpret this information differently (i.e. soft information)” (Kirschenheiter, 2002) Nature : quantitative vs qualitative (numbers vs words) / backward versus forward looking Collecting method : impersonal vs personal (production’s context, role of the agent responsible for the production and treatment process) Cognitive factors : weakly present vs strongly present (subjective judgment, interpretation and perception, opinions . . . ) Lending technology : transaction lending vs relationship lending Organizational structure : centralized and hierarchical vs decentralized and non-hierarchical Godbillon-Camus and Godlewski Credit Risk Management and Information 4/ 24
  14. 14. Background, Motivation & Aim Hard versus Soft Information Literature Survey Pros and Cons Model Impact of Soft Information on Default’s Risk Prediction Results Organizational Structure and Information DiscussionLiterature SurveyPros and Cons HARD information = low cost, durable, comparable, verifiable, not manipulable ⇒ e.g.: scoring = increases credit’s availability and reduces credit’s cost (risk adjusted pricing), BUT doesn’t increase risk measurement’s precision as a complementary risk measurement tool (Feldman, 1997; Berger et al. 2002; Frame et al., 2002) SOFT information = multi-dimensional, richer, more precise, not verifiable, manipulable ⇒ output of a bank-borrower relationship (private information, multiple interactions) (Boot, 2000); can also increase credit’s availability and reduce its cost Godbillon-Camus and Godlewski Credit Risk Management and Information 5/ 24
  15. 15. Background, Motivation & Aim Hard versus Soft Information Literature Survey Pros and Cons Model Impact of Soft Information on Default’s Risk Prediction Results Organizational Structure and Information DiscussionLiterature SurveyPros and Cons HARD information = low cost, durable, comparable, verifiable, not manipulable ⇒ e.g.: scoring = increases credit’s availability and reduces credit’s cost (risk adjusted pricing), BUT doesn’t increase risk measurement’s precision as a complementary risk measurement tool (Feldman, 1997; Berger et al. 2002; Frame et al., 2002) SOFT information = multi-dimensional, richer, more precise, not verifiable, manipulable ⇒ output of a bank-borrower relationship (private information, multiple interactions) (Boot, 2000); can also increase credit’s availability and reduce its cost Godbillon-Camus and Godlewski Credit Risk Management and Information 5/ 24
  16. 16. Background, Motivation & Aim Hard versus Soft Information Literature Survey Pros and Cons Model Impact of Soft Information on Default’s Risk Prediction Results Organizational Structure and Information DiscussionLiterature SurveyImpact of Soft Information on Default’s Risk Prediction Empirical evidence by Grunert et al. (2002) and Lehmann (2003) Soft factors are more stable and precise Soft factors increase classification and discriminatory power of the default’s prediction models Godbillon-Camus and Godlewski Credit Risk Management and Information 6/ 24
  17. 17. Background, Motivation & Aim Hard versus Soft Information Literature Survey Pros and Cons Model Impact of Soft Information on Default’s Risk Prediction Results Organizational Structure and Information DiscussionLiterature SurveyImpact of Soft Information on Default’s Risk Prediction Empirical evidence by Grunert et al. (2002) and Lehmann (2003) Soft factors are more stable and precise Soft factors increase classification and discriminatory power of the default’s prediction models Godbillon-Camus and Godlewski Credit Risk Management and Information 6/ 24
  18. 18. Background, Motivation & Aim Hard versus Soft Information Literature Survey Pros and Cons Model Impact of Soft Information on Default’s Risk Prediction Results Organizational Structure and Information DiscussionLiterature SurveyOrganizational Structure and Information 1/2 Hyp.: Soft information is more precise but not verifiable and thus manipulable These characteristics imply an adapted organizational structure in order to avoid consequences and costs of soft information manipulation Bank-borrower relationship, which gives access to soft information, is a source of asymmetries between the agent in charge of the information’s treatment and the principal who takes his funds allocation and risk management decisions upon information transmitted by the agent The agent can extract private benefits and thus affect principal’s decisions efficiency Godbillon-Camus and Godlewski Credit Risk Management and Information 7/ 24
  19. 19. Background, Motivation & Aim Hard versus Soft Information Literature Survey Pros and Cons Model Impact of Soft Information on Default’s Risk Prediction Results Organizational Structure and Information DiscussionLiterature SurveyOrganizational Structure and Information 1/2 Hyp.: Soft information is more precise but not verifiable and thus manipulable These characteristics imply an adapted organizational structure in order to avoid consequences and costs of soft information manipulation Bank-borrower relationship, which gives access to soft information, is a source of asymmetries between the agent in charge of the information’s treatment and the principal who takes his funds allocation and risk management decisions upon information transmitted by the agent The agent can extract private benefits and thus affect principal’s decisions efficiency Godbillon-Camus and Godlewski Credit Risk Management and Information 7/ 24
  20. 20. Background, Motivation & Aim Hard versus Soft Information Literature Survey Pros and Cons Model Impact of Soft Information on Default’s Risk Prediction Results Organizational Structure and Information DiscussionLiterature SurveyOrganizational Structure and Information 1/2 Hyp.: Soft information is more precise but not verifiable and thus manipulable These characteristics imply an adapted organizational structure in order to avoid consequences and costs of soft information manipulation Bank-borrower relationship, which gives access to soft information, is a source of asymmetries between the agent in charge of the information’s treatment and the principal who takes his funds allocation and risk management decisions upon information transmitted by the agent The agent can extract private benefits and thus affect principal’s decisions efficiency Godbillon-Camus and Godlewski Credit Risk Management and Information 7/ 24
  21. 21. Background, Motivation & Aim Hard versus Soft Information Literature Survey Pros and Cons Model Impact of Soft Information on Default’s Risk Prediction Results Organizational Structure and Information DiscussionLiterature SurveyOrganizational Structure and Information 2/2 Stein (2002) : adequacy between organizational structure (hierarchical & centralized vs non hierarchical & decentralized) and information’s type (hard vs soft) (extensions by Takats, 2004) Small banks seem to have an advantage in processing soft information within a bank-borrower relationship framework (Berger 2004; De Young et al., 2004; Scott, 2004) Empirical evidence : Berger and Udell (2002) and Berger et al. (2001, 2002) Bernardo et al. (2001), Ozerturk (2004), Ozbas (2005) : role of the wages and budget allocation policy in implementing proper incentives structure for the agent responsable for information’s treatment Godbillon-Camus and Godlewski Credit Risk Management and Information 8/ 24
  22. 22. Background, Motivation & Aim Hard versus Soft Information Literature Survey Pros and Cons Model Impact of Soft Information on Default’s Risk Prediction Results Organizational Structure and Information DiscussionLiterature SurveyOrganizational Structure and Information 2/2 Stein (2002) : adequacy between organizational structure (hierarchical & centralized vs non hierarchical & decentralized) and information’s type (hard vs soft) (extensions by Takats, 2004) Small banks seem to have an advantage in processing soft information within a bank-borrower relationship framework (Berger 2004; De Young et al., 2004; Scott, 2004) Empirical evidence : Berger and Udell (2002) and Berger et al. (2001, 2002) Bernardo et al. (2001), Ozerturk (2004), Ozbas (2005) : role of the wages and budget allocation policy in implementing proper incentives structure for the agent responsable for information’s treatment Godbillon-Camus and Godlewski Credit Risk Management and Information 8/ 24
  23. 23. Background, Motivation & Aim Hard versus Soft Information Literature Survey Pros and Cons Model Impact of Soft Information on Default’s Risk Prediction Results Organizational Structure and Information DiscussionLiterature SurveyOrganizational Structure and Information 2/2 Stein (2002) : adequacy between organizational structure (hierarchical & centralized vs non hierarchical & decentralized) and information’s type (hard vs soft) (extensions by Takats, 2004) Small banks seem to have an advantage in processing soft information within a bank-borrower relationship framework (Berger 2004; De Young et al., 2004; Scott, 2004) Empirical evidence : Berger and Udell (2002) and Berger et al. (2001, 2002) Bernardo et al. (2001), Ozerturk (2004), Ozbas (2005) : role of the wages and budget allocation policy in implementing proper incentives structure for the agent responsable for information’s treatment Godbillon-Camus and Godlewski Credit Risk Management and Information 8/ 24
  24. 24. Background, Motivation & Aim Hard versus Soft Information Literature Survey Pros and Cons Model Impact of Soft Information on Default’s Risk Prediction Results Organizational Structure and Information DiscussionLiterature SurveyOrganizational Structure and Information 2/2 Stein (2002) : adequacy between organizational structure (hierarchical & centralized vs non hierarchical & decentralized) and information’s type (hard vs soft) (extensions by Takats, 2004) Small banks seem to have an advantage in processing soft information within a bank-borrower relationship framework (Berger 2004; De Young et al., 2004; Scott, 2004) Empirical evidence : Berger and Udell (2002) and Berger et al. (2001, 2002) Bernardo et al. (2001), Ozerturk (2004), Ozbas (2005) : role of the wages and budget allocation policy in implementing proper incentives structure for the agent responsable for information’s treatment Godbillon-Camus and Godlewski Credit Risk Management and Information 8/ 24
  25. 25. Background, Motivation & Aim Main Framework Literature Survey Banker & Credit Officer with Hard Information Model Gains and Losses with Soft Information Results Banker & Credit Officer with Hard and Soft Information DiscussionModelMain Framework 1/3 Bank’s Director (banker) = principal and Credit Officer = agent, both risk averse Banker’s decision = balance sheet’s structure, made upon the information produced by the credit officer Bank’s profit ˜ r Π = ˜A A − rD D − w (˜A ) − c r (1) ˜A : random assets’ (and credit officer’s budget) A return, rD : interest r rate on deposits D, w (˜A ) : credit officer salary (eventually function of the r random assets’ return ˜A ), c : credit officer’s unemployment insurance r cost (normalized to 0) Godbillon-Camus and Godlewski Credit Risk Management and Information 9/ 24
  26. 26. Background, Motivation & Aim Main Framework Literature Survey Banker & Credit Officer with Hard Information Model Gains and Losses with Soft Information Results Banker & Credit Officer with Hard and Soft Information DiscussionModelMain Framework 1/3 Bank’s Director (banker) = principal and Credit Officer = agent, both risk averse Banker’s decision = balance sheet’s structure, made upon the information produced by the credit officer Bank’s profit ˜ r Π = ˜A A − rD D − w (˜A ) − c r (1) ˜A : random assets’ (and credit officer’s budget) A return, rD : interest r rate on deposits D, w (˜A ) : credit officer salary (eventually function of the r random assets’ return ˜A ), c : credit officer’s unemployment insurance r cost (normalized to 0) Godbillon-Camus and Godlewski Credit Risk Management and Information 9/ 24
  27. 27. Background, Motivation & Aim Main Framework Literature Survey Banker & Credit Officer with Hard Information Model Gains and Losses with Soft Information Results Banker & Credit Officer with Hard and Soft Information DiscussionModelMain Framework 1/3 Bank’s Director (banker) = principal and Credit Officer = agent, both risk averse Banker’s decision = balance sheet’s structure, made upon the information produced by the credit officer Bank’s profit ˜ r Π = ˜A A − rD D − w (˜A ) − c r (1) ˜A : random assets’ (and credit officer’s budget) A return, rD : interest r rate on deposits D, w (˜A ) : credit officer salary (eventually function of the r random assets’ return ˜A ), c : credit officer’s unemployment insurance r cost (normalized to 0) Godbillon-Camus and Godlewski Credit Risk Management and Information 9/ 24
  28. 28. Background, Motivation & Aim Main Framework Literature Survey Banker & Credit Officer with Hard Information Model Gains and Losses with Soft Information Results Banker & Credit Officer with Hard and Soft Information DiscussionModelMain Framework 2/3 Banker’s utility (β : constant risk aversion’s coefficient) ˜ UB = − exp−β(Π) (2) Credit officer’s utility (γ : constant risk aversion’s coefficient) UC = − exp−γ(˜A A+w (˜A )) r r (3) Information concerns ˜A ⇒ modelled as a signal µ ∼ N(¯, υ 2 ) r ˜ µ (following Bhattacharya and Pfleiderer, 1982) ⇒ linked to ˜A as r ˜A = µ + ε, r ˜ ˜ (4) 2 with ε ∼ N(0, σ ) ⇒ conditional distribution upon realization of µ is ˜ (˜A | µ) ∼ N(µ, σ 2 ) r Godbillon-Camus and Godlewski Credit Risk Management and Information 10/ 24
  29. 29. Background, Motivation & Aim Main Framework Literature Survey Banker & Credit Officer with Hard Information Model Gains and Losses with Soft Information Results Banker & Credit Officer with Hard and Soft Information DiscussionModelMain Framework 2/3 Banker’s utility (β : constant risk aversion’s coefficient) ˜ UB = − exp−β(Π) (2) Credit officer’s utility (γ : constant risk aversion’s coefficient) UC = − exp−γ(˜A A+w (˜A )) r r (3) Information concerns ˜A ⇒ modelled as a signal µ ∼ N(¯, υ 2 ) r ˜ µ (following Bhattacharya and Pfleiderer, 1982) ⇒ linked to ˜A as r ˜A = µ + ε, r ˜ ˜ (4) 2 with ε ∼ N(0, σ ) ⇒ conditional distribution upon realization of µ is ˜ (˜A | µ) ∼ N(µ, σ 2 ) r Godbillon-Camus and Godlewski Credit Risk Management and Information 10/ 24
  30. 30. Background, Motivation & Aim Main Framework Literature Survey Banker & Credit Officer with Hard Information Model Gains and Losses with Soft Information Results Banker & Credit Officer with Hard and Soft Information DiscussionModelMain Framework 2/3 Banker’s utility (β : constant risk aversion’s coefficient) ˜ UB = − exp−β(Π) (2) Credit officer’s utility (γ : constant risk aversion’s coefficient) UC = − exp−γ(˜A A+w (˜A )) r r (3) Information concerns ˜A ⇒ modelled as a signal µ ∼ N(¯, υ 2 ) r ˜ µ (following Bhattacharya and Pfleiderer, 1982) ⇒ linked to ˜A as r ˜A = µ + ε, r ˜ ˜ (4) 2 with ε ∼ N(0, σ ) ⇒ conditional distribution upon realization of µ is ˜ (˜A | µ) ∼ N(µ, σ 2 ) r Godbillon-Camus and Godlewski Credit Risk Management and Information 10/ 24
  31. 31. Background, Motivation & Aim Main Framework Literature Survey Banker & Credit Officer with Hard Information Model Gains and Losses with Soft Information Results Banker & Credit Officer with Hard and Soft Information DiscussionModelMain Framework 3/3 Hyp.: Hard versus Soft Information ⇔ less / more precise ⇒ σ S < σH Credit risk management ⇔ capital K allocation for Value at Risk coverage Banker states bank’s default probability α (exogenous) as p (A(1 + ˜A ) − D(1 + rD ) < 0) = α r (5) following Broll and Wahl (2003) we infer VaR per risky assets unit as follows rD − µ − uα σ rα = (6) 1 + rD uα : fractile of a standardized distribution, rα = “VaR rate” ⇒ increases with σ, as uα < 0, VaRα = rα A (7) Godbillon-Camus and Godlewski Credit Risk Management and Information 11/ 24
  32. 32. Background, Motivation & Aim Main Framework Literature Survey Banker & Credit Officer with Hard Information Model Gains and Losses with Soft Information Results Banker & Credit Officer with Hard and Soft Information DiscussionModelMain Framework 3/3 Hyp.: Hard versus Soft Information ⇔ less / more precise ⇒ σ S < σH Credit risk management ⇔ capital K allocation for Value at Risk coverage Banker states bank’s default probability α (exogenous) as p (A(1 + ˜A ) − D(1 + rD ) < 0) = α r (5) following Broll and Wahl (2003) we infer VaR per risky assets unit as follows rD − µ − uα σ rα = (6) 1 + rD uα : fractile of a standardized distribution, rα = “VaR rate” ⇒ increases with σ, as uα < 0, VaRα = rα A (7) Godbillon-Camus and Godlewski Credit Risk Management and Information 11/ 24
  33. 33. Background, Motivation & Aim Main Framework Literature Survey Banker & Credit Officer with Hard Information Model Gains and Losses with Soft Information Results Banker & Credit Officer with Hard and Soft Information DiscussionModelMain Framework 3/3 Hyp.: Hard versus Soft Information ⇔ less / more precise ⇒ σ S < σH Credit risk management ⇔ capital K allocation for Value at Risk coverage Banker states bank’s default probability α (exogenous) as p (A(1 + ˜A ) − D(1 + rD ) < 0) = α r (5) following Broll and Wahl (2003) we infer VaR per risky assets unit as follows rD − µ − uα σ rα = (6) 1 + rD uα : fractile of a standardized distribution, rα = “VaR rate” ⇒ increases with σ, as uα < 0, VaRα = rα A (7) Godbillon-Camus and Godlewski Credit Risk Management and Information 11/ 24
  34. 34. Background, Motivation & Aim Main Framework Literature Survey Banker & Credit Officer with Hard Information Model Gains and Losses with Soft Information Results Banker & Credit Officer with Hard and Soft Information DiscussionModelMain Framework 3/3 Hyp.: Hard versus Soft Information ⇔ less / more precise ⇒ σ S < σH Credit risk management ⇔ capital K allocation for Value at Risk coverage Banker states bank’s default probability α (exogenous) as p (A(1 + ˜A ) − D(1 + rD ) < 0) = α r (5) following Broll and Wahl (2003) we infer VaR per risky assets unit as follows rD − µ − uα σ rα = (6) 1 + rD uα : fractile of a standardized distribution, rα = “VaR rate” ⇒ increases with σ, as uα < 0, VaRα = rα A (7) Godbillon-Camus and Godlewski Credit Risk Management and Information 11/ 24
  35. 35. Background, Motivation & Aim Main Framework Literature Survey Banker & Credit Officer with Hard Information Model Gains and Losses with Soft Information Results Banker & Credit Officer with Hard and Soft Information DiscussionModelMain Framework 3/3 Hyp.: Hard versus Soft Information ⇔ less / more precise ⇒ σ S < σH Credit risk management ⇔ capital K allocation for Value at Risk coverage Banker states bank’s default probability α (exogenous) as p (A(1 + ˜A ) − D(1 + rD ) < 0) = α r (5) following Broll and Wahl (2003) we infer VaR per risky assets unit as follows rD − µ − uα σ rα = (6) 1 + rD uα : fractile of a standardized distribution, rα = “VaR rate” ⇒ increases with σ, as uα < 0, VaRα = rα A (7) Godbillon-Camus and Godlewski Credit Risk Management and Information 11/ 24
  36. 36. Background, Motivation & Aim Main Framework Literature Survey Banker & Credit Officer with Hard Information Model Gains and Losses with Soft Information Results Banker & Credit Officer with Hard and Soft Information DiscussionModelBanker & Credit Officer with Hard Information 1/3 First step: Banker decides of the credit officer’s salary Second step: Principal’s decisions concerning capital K , assets A and deposits D are made upon the signal µ on the random assets’ ˜ return ˜A distribution, transmitted by the agent r Hyp.: Hard Information (e.g. a score) provided by the credit officer is verifiable and non manipulable Credit officer’s salary w (˜A ) = w0 . r (8) Godbillon-Camus and Godlewski Credit Risk Management and Information 12/ 24
  37. 37. Background, Motivation & Aim Main Framework Literature Survey Banker & Credit Officer with Hard Information Model Gains and Losses with Soft Information Results Banker & Credit Officer with Hard and Soft Information DiscussionModelBanker & Credit Officer with Hard Information 1/3 First step: Banker decides of the credit officer’s salary Second step: Principal’s decisions concerning capital K , assets A and deposits D are made upon the signal µ on the random assets’ ˜ return ˜A distribution, transmitted by the agent r Hyp.: Hard Information (e.g. a score) provided by the credit officer is verifiable and non manipulable Credit officer’s salary w (˜A ) = w0 . r (8) Godbillon-Camus and Godlewski Credit Risk Management and Information 12/ 24
  38. 38. Background, Motivation & Aim Main Framework Literature Survey Banker & Credit Officer with Hard Information Model Gains and Losses with Soft Information Results Banker & Credit Officer with Hard and Soft Information DiscussionModelBanker & Credit Officer with Hard Information 1/3 First step: Banker decides of the credit officer’s salary Second step: Principal’s decisions concerning capital K , assets A and deposits D are made upon the signal µ on the random assets’ ˜ return ˜A distribution, transmitted by the agent r Hyp.: Hard Information (e.g. a score) provided by the credit officer is verifiable and non manipulable Credit officer’s salary w (˜A ) = w0 . r (8) Godbillon-Camus and Godlewski Credit Risk Management and Information 12/ 24
  39. 39. Background, Motivation & Aim Main Framework Literature Survey Banker & Credit Officer with Hard Information Model Gains and Losses with Soft Information Results Banker & Credit Officer with Hard and Soft Information DiscussionModelBanker & Credit Officer with Hard Information 1/3 First step: Banker decides of the credit officer’s salary Second step: Principal’s decisions concerning capital K , assets A and deposits D are made upon the signal µ on the random assets’ ˜ return ˜A distribution, transmitted by the agent r Hyp.: Hard Information (e.g. a score) provided by the credit officer is verifiable and non manipulable Credit officer’s salary w (˜A ) = w0 . r (8) Godbillon-Camus and Godlewski Credit Risk Management and Information 12/ 24
  40. 40. Background, Motivation & Aim Main Framework Literature Survey Banker & Credit Officer with Hard Information Model Gains and Losses with Soft Information Results Banker & Credit Officer with Hard and Soft Information DiscussionModelBanker & Credit Officer with Hard Information 2/3 Optimization program with Hard information max EUB , w0 ,K ,A,D  ¯  EUC ≥ U,   K , A, D ∈ arg max EUB   ˆ ˆ ˆ   K ,A,D (9) ˆ ˆ ˆ  K + D − A = 0,   ˆ   K − VaRα ≥ 0,    ˆ ˆ VaRα = rα A = rD −µ−uα σ A. 1+rD Godbillon-Camus and Godlewski Credit Risk Management and Information 13/ 24
  41. 41. Background, Motivation & Aim Main Framework Literature Survey Banker & Credit Officer with Hard Information Model Gains and Losses with Soft Information Results Banker & Credit Officer with Hard and Soft Information DiscussionModelBanker & Credit Officer with Hard Information 3/3 with +∞ EUB = − exp[−β(˜A A−rD D−w0 )] η(˜A |µ)drA r r −∞ +∞ EUC = − exp[−γ(˜A A+w0 )] η(˜A |µ)drA , r r −∞ ¯ U = − exp−γv Godbillon-Camus and Godlewski Credit Risk Management and Information 14/ 24
  42. 42. Background, Motivation & Aim Main Framework Literature Survey Banker & Credit Officer with Hard Information Model Gains and Losses with Soft Information Results Banker & Credit Officer with Hard and Soft Information DiscussionModelGains and Losses with Soft Information 1/2 GAINS : economy of capital K for VaR coverage (more precise information) LOSSES : manipulation problem if it gives the agent’s a higher expected utility compared to its reservation value ⇒ the credit officer transmits a signal µ while observing a signal µ + f with f > 0 or f < 0 f γ(µ−rD (1+uα σ)) M − βσ 2 (1+rD ) EUC = − exp−γv exp (10) ¯ E (U) Godbillon-Camus and Godlewski Credit Risk Management and Information 15/ 24
  43. 43. Background, Motivation & Aim Main Framework Literature Survey Banker & Credit Officer with Hard Information Model Gains and Losses with Soft Information Results Banker & Credit Officer with Hard and Soft Information DiscussionModelGains and Losses with Soft Information 1/2 GAINS : economy of capital K for VaR coverage (more precise information) LOSSES : manipulation problem if it gives the agent’s a higher expected utility compared to its reservation value ⇒ the credit officer transmits a signal µ while observing a signal µ + f with f > 0 or f < 0 f γ(µ−rD (1+uα σ)) M − βσ 2 (1+rD ) EUC = − exp−γv exp (10) ¯ E (U) Godbillon-Camus and Godlewski Credit Risk Management and Information 15/ 24
  44. 44. Background, Motivation & Aim Main Framework Literature Survey Banker & Credit Officer with Hard Information Model Gains and Losses with Soft Information Results Banker & Credit Officer with Hard and Soft Information DiscussionModelGains and Losses with Soft Information 2/2 Result : Downgrading manipulation ⇔ credit officer transmits a signal µ while he observes µ + f ¯ ⇒ in order to get more than U, the agent must induce the principal in error so that the latter under-estimates what he actually attributes to the credit officer ⇒ the agent’s utility depends upon his budget’s development and his salary ⇒ the principal always guarantees the reservation utility level ⇒ under-estimating the signal allows the credit officer to limit the expropriation by the banker and increases his expected utility Godbillon-Camus and Godlewski Credit Risk Management and Information 16/ 24
  45. 45. Background, Motivation & Aim Main Framework Literature Survey Banker & Credit Officer with Hard Information Model Gains and Losses with Soft Information Results Banker & Credit Officer with Hard and Soft Information DiscussionModelGains and Losses with Soft Information 2/2 Result : Downgrading manipulation ⇔ credit officer transmits a signal µ while he observes µ + f ¯ ⇒ in order to get more than U, the agent must induce the principal in error so that the latter under-estimates what he actually attributes to the credit officer ⇒ the agent’s utility depends upon his budget’s development and his salary ⇒ the principal always guarantees the reservation utility level ⇒ under-estimating the signal allows the credit officer to limit the expropriation by the banker and increases his expected utility Godbillon-Camus and Godlewski Credit Risk Management and Information 16/ 24
  46. 46. Background, Motivation & Aim Main Framework Literature Survey Banker & Credit Officer with Hard Information Model Gains and Losses with Soft Information Results Banker & Credit Officer with Hard and Soft Information DiscussionModelGains and Losses with Soft Information 2/2 Result : Downgrading manipulation ⇔ credit officer transmits a signal µ while he observes µ + f ¯ ⇒ in order to get more than U, the agent must induce the principal in error so that the latter under-estimates what he actually attributes to the credit officer ⇒ the agent’s utility depends upon his budget’s development and his salary ⇒ the principal always guarantees the reservation utility level ⇒ under-estimating the signal allows the credit officer to limit the expropriation by the banker and increases his expected utility Godbillon-Camus and Godlewski Credit Risk Management and Information 16/ 24
  47. 47. Background, Motivation & Aim Main Framework Literature Survey Banker & Credit Officer with Hard Information Model Gains and Losses with Soft Information Results Banker & Credit Officer with Hard and Soft Information DiscussionModelBanker & Credit Officer with Hard and Soft Information 1/3 Hyp.: Soft Information produced by the credit officer (e.g. relationship lending) is non verifiable and manipulable (moral hazard problem with hidden information) but more precise ⇒ modification of the credit decision process organization Incentive salary package w = w0 + w1 (˜A − bm) r (11) with m being the transmitted signal by the credit officer (“predicted mean return”), which might be different from the observed signal µ, and b : weighting factor for an objective to attain, with a bonus in case of out-performance Godbillon-Camus and Godlewski Credit Risk Management and Information 17/ 24
  48. 48. Background, Motivation & Aim Main Framework Literature Survey Banker & Credit Officer with Hard Information Model Gains and Losses with Soft Information Results Banker & Credit Officer with Hard and Soft Information DiscussionModelBanker & Credit Officer with Hard and Soft Information 1/3 Hyp.: Soft Information produced by the credit officer (e.g. relationship lending) is non verifiable and manipulable (moral hazard problem with hidden information) but more precise ⇒ modification of the credit decision process organization Incentive salary package w = w0 + w1 (˜A − bm) r (11) with m being the transmitted signal by the credit officer (“predicted mean return”), which might be different from the observed signal µ, and b : weighting factor for an objective to attain, with a bonus in case of out-performance Godbillon-Camus and Godlewski Credit Risk Management and Information 17/ 24
  49. 49. Background, Motivation & Aim Main Framework Literature Survey Banker & Credit Officer with Hard Information Model Gains and Losses with Soft Information Results Banker & Credit Officer with Hard and Soft Information DiscussionModelBanker & Credit Officer with Hard and Soft Information 2/3 Optimization program with Soft information max EUB (µ),  w0 ,w1 ,K ,A,D  EUC (µ) ≥ U, ¯   µ ∈ arg max EU (m),   C   m  K , A, D ∈ arg max EU (m),  B (12) ˆ ˆ ˆ K ,A,D   K + D − A = 0,   ˆ ˆ ˆ      ˆ − VaRα ≥ 0, K    ˆ VaRα = rα A = rD −µ−uα σ A.ˆ 1+rD Godbillon-Camus and Godlewski Credit Risk Management and Information 18/ 24
  50. 50. Background, Motivation & Aim Main Framework Literature Survey Banker & Credit Officer with Hard Information Model Gains and Losses with Soft Information Results Banker & Credit Officer with Hard and Soft Information DiscussionModelBanker & Credit Officer with Hard and Soft Information 3/3 with +∞ EUB (m) = − exp[−β(˜A A−rD D−(w0 +w1 (˜A −bm)))] η(˜A |m)drA r r r −∞ +∞ EUB (µ) = − exp[−β(˜A A−rD D−(w0 +w1 (˜A −bµ)))] η(˜A |µ)drA r r r −∞ +∞ EUC (m) = − exp[−γ(˜A A+(w0 +w1 (˜A −bm)))] η(˜A |µ)drA r r r −∞ +∞ EUC (µ) = − exp[−γ(˜A A+(w0 +w1 (˜A −bµ)))] η(˜A |µ)drA r r r −∞ Godbillon-Camus and Godlewski Credit Risk Management and Information 19/ 24
  51. 51. Background, Motivation & Aim Literature Survey Analytical Results Model Comparison of Results with Hard versus Hard & Soft Information Results DiscussionResultsAnalytical Results Information Hard (µ−rD (1+uα σ))(γ(µ−rD (1+uα σ))−2βµ(1+rD )) Ew ∗ = v + 2β 2 σ 2 (1+rD )2 (µ−rD (1+uα σ)) A∗ = βσ 2 (1+r ) D rD −µ−uα σ µ−rD (1+uα σ) K∗ = 1+rD βσ 2 (1+rD ) (µ−rD (1+uα σ))(β(rD (1+uα σ)−µ(3+2rD ))+γ(µ−rD (1+uα σ))) +βv 2βσ 2 (1+rD )2 EUB = − exp ∗ Information Hard et Soft 2µγ 2 (µ−rD (1+uα σ)) Ew ∗∗ = v − βσ 2 (1+rD )(bβ+2γ)2 + b(µ − rD (1 + uα σ))Φ (µ−rD (1+uα σ))(γ+bβ)+βµ(1+rD ) A∗∗ = βσ 2 (1+rD )(2γ+bβ) (µ−rD (1+uα σ))(γ+bβ)+βµ(1+rD ) K ∗∗ rD −µ−uα σ 1+rD βσ 2 (bβ+2γ) EUB∗∗ = . . . Godbillon-Camus and Godlewski Credit Risk Management and Information 20/ 24
  52. 52. Background, Motivation & Aim Literature Survey Analytical Results Model Comparison of Results with Hard versus Hard & Soft Information Results DiscussionResultsComparison of Results with Hard versus Hard & Soft Information Comparison of the hard versus hard & soft solutions in terms of differences of: expected salary Ew , capital K , assets A and banker’s expected utility E (UB ) Numerical simulations for fixed parameters except the signal µ Parameters values are (with respect to the analytical constraints) rD = 0.025, v = 0, uα=0.01 = −2.3263, β = γ = 1, σ = 0.2, λ = 0.1 (level of hard’s signal imprecision as σH = σS + λ), b = 2 and µ : [0.03; 0.75]. Godbillon-Camus and Godlewski Credit Risk Management and Information 21/ 24
  53. 53. Background, Motivation & Aim Literature Survey Analytical Results Model Comparison of Results with Hard versus Hard & Soft Information Results DiscussionResultsComparison of Results with Hard versus Hard & Soft Information Comparison of the hard versus hard & soft solutions in terms of differences of: expected salary Ew , capital K , assets A and banker’s expected utility E (UB ) Numerical simulations for fixed parameters except the signal µ Parameters values are (with respect to the analytical constraints) rD = 0.025, v = 0, uα=0.01 = −2.3263, β = γ = 1, σ = 0.2, λ = 0.1 (level of hard’s signal imprecision as σH = σS + λ), b = 2 and µ : [0.03; 0.75]. Godbillon-Camus and Godlewski Credit Risk Management and Information 21/ 24
  54. 54. Background, Motivation & Aim Literature Survey Analytical Results Model Comparison of Results with Hard versus Hard & Soft Information Results DiscussionResultsComparison of Results with Hard versus Hard & Soft Information Comparison of the hard versus hard & soft solutions in terms of differences of: expected salary Ew , capital K , assets A and banker’s expected utility E (UB ) Numerical simulations for fixed parameters except the signal µ Parameters values are (with respect to the analytical constraints) rD = 0.025, v = 0, uα=0.01 = −2.3263, β = γ = 1, σ = 0.2, λ = 0.1 (level of hard’s signal imprecision as σH = σS + λ), b = 2 and µ : [0.03; 0.75]. Godbillon-Camus and Godlewski Credit Risk Management and Information 21/ 24
  55. 55. Background, Motivation & Aim Literature Survey Analytical Results Model Comparison of Results with Hard versus Hard & Soft Information Results DiscussionResults 3.5 4 3 3 2.5 2 2 1.5 1 1 0.5 0 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 dEw = Ew ∗ − Ew ∗∗ curve f (µ) dK = K ∗ − K ∗∗ curve f (µ) 0 0 -2 -0.05 -0.1 -4 -0.15 -6 -0.2 -8 -0.25 -10 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 dA = A∗ − A∗∗ curve f (µ) dE (UB ) = E (UB ) − E (UB ) curve f (µ) ∗ ∗∗ Godbillon-Camus and Godlewski Credit Risk Management and Information 22/ 24
  56. 56. Background, Motivation & Aim Literature Survey Model Results DiscussionDiscussion Importance of information for bank risk management (Hakenes, 2004) Distinction of Hard versus Soft Information (Petersen, 2004) ⇔ Different lending technologies : Transaction Lending versus Relationship lending (Hakenes and Schnabel 2005; Berger, 2004; Danielsson et al., 2001)⇔ Different organization’s types : hierarchical and centralized vs non hierarchical and decentralized (Stein, 2002; Takats, 2004) Investigating the impact of information’s type on credit risk management’ organization in a principal-agent framework with moral hazard with hidden information ⇒ Soft information is more precise (pro) but manipulable (con) ⇒ decreases capital’s for VaR coverage and increases lending and bankers’ utility although incentive salary package is implemented for the credit officer Godbillon-Camus and Godlewski Credit Risk Management and Information 23/ 24
  57. 57. Background, Motivation & Aim Literature Survey Model Results DiscussionDiscussion Importance of information for bank risk management (Hakenes, 2004) Distinction of Hard versus Soft Information (Petersen, 2004) ⇔ Different lending technologies : Transaction Lending versus Relationship lending (Hakenes and Schnabel 2005; Berger, 2004; Danielsson et al., 2001)⇔ Different organization’s types : hierarchical and centralized vs non hierarchical and decentralized (Stein, 2002; Takats, 2004) Investigating the impact of information’s type on credit risk management’ organization in a principal-agent framework with moral hazard with hidden information ⇒ Soft information is more precise (pro) but manipulable (con) ⇒ decreases capital’s for VaR coverage and increases lending and bankers’ utility although incentive salary package is implemented for the credit officer Godbillon-Camus and Godlewski Credit Risk Management and Information 23/ 24
  58. 58. Background, Motivation & Aim Literature Survey Model Results DiscussionDiscussion Importance of information for bank risk management (Hakenes, 2004) Distinction of Hard versus Soft Information (Petersen, 2004) ⇔ Different lending technologies : Transaction Lending versus Relationship lending (Hakenes and Schnabel 2005; Berger, 2004; Danielsson et al., 2001)⇔ Different organization’s types : hierarchical and centralized vs non hierarchical and decentralized (Stein, 2002; Takats, 2004) Investigating the impact of information’s type on credit risk management’ organization in a principal-agent framework with moral hazard with hidden information ⇒ Soft information is more precise (pro) but manipulable (con) ⇒ decreases capital’s for VaR coverage and increases lending and bankers’ utility although incentive salary package is implemented for the credit officer Godbillon-Camus and Godlewski Credit Risk Management and Information 23/ 24
  59. 59. Background, Motivation & Aim Literature Survey Model Results DiscussionCurrent Research & Perspectives Theoretical axe : take into account errors in soft information’s processing (e.g. collusion between the credit officer and the debtor); introduce soft information’s treatment cost (e.g. in terms of effort); introduce trust; investigate the impact of different information’s type on bank competition Empirical strategy : focus on syndicated loans (decision based on hard and soft information, following Dennis and Mullineux, 2000) using Dealscan (LPC, Reuters) - build hard (scoring) and soft (efficiency score) information’s proxies and investigate their impact on bank debt’s contract characteristics, . . . Godbillon-Camus and Godlewski Credit Risk Management and Information 24/ 24
  60. 60. Background, Motivation & Aim Literature Survey Model Results DiscussionCurrent Research & Perspectives Theoretical axe : take into account errors in soft information’s processing (e.g. collusion between the credit officer and the debtor); introduce soft information’s treatment cost (e.g. in terms of effort); introduce trust; investigate the impact of different information’s type on bank competition Empirical strategy : focus on syndicated loans (decision based on hard and soft information, following Dennis and Mullineux, 2000) using Dealscan (LPC, Reuters) - build hard (scoring) and soft (efficiency score) information’s proxies and investigate their impact on bank debt’s contract characteristics, . . . Godbillon-Camus and Godlewski Credit Risk Management and Information 24/ 24

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