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03.2 homogeneous debt portfolios
1.
Copyright © 2018
CapitaLogic Limited This presentation file is prepared in accordance with Chapter 3 of the text book “Managing Credit Risk Under The Basel III Framework, 3rd ed” Website : https://sites.google.com/site/crmbasel E-mail : crmbasel@gmail.com Chapter 3 Homogeneous Debt Portfolios
2.
Copyright © 2018
CapitaLogic Limited 2 Declaration Copyright © 2018 CapitaLogic Limited. All rights reserved. No part of this presentation file may be reproduced, in any form or by any means, without written permission from CapitaLogic Limited. Authored by Dr. LAM Yat-fai (林日辉), Director, CapitaLogic Limited, Adjunct Professor of Finance, City University of Hong Kong, Doctor of Business Administration, CFA, CAIA, CAMS FRM, PRM.
3.
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CapitaLogic Limited 3 Outline Credit risk identification Independent homogeneous portfolio Gaussian copula Finite homogeneous portfolio Infinite homogeneous portfolio Appendices
4.
Copyright © 2018
CapitaLogic Limited 4 Portfolio 1-year EL A debt portfolio comprising NOB different debts k NOB k k=1 NOB RM k k k k k=1 NOB k k k k k=1 Portfolio 1-year EL = 1-year EL = EAD × LGD × Min PD , 1 - 1 - PD EAD × LGD × PD × Min 1, RM
5.
Copyright © 2018
CapitaLogic Limited 5 Portfolio 1-year EL Failed to incorporate diversification effect More borrowers with smaller EADs => lower risk Lower default dependency => lower risk Not an effective credit risk measure for a debt portfolio Example 3.1 Example 3.2
6.
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CapitaLogic Limited 6 Credit risk identification Credit risk Default loss Exposure at default Default chance Loss given default Probability of default Diversification effect Concentration of debts Default dependency Residual maturity
7.
Copyright © 2018
CapitaLogic Limited 7 Diversification effect For a fixed portfolio EAD Concentration of borrowers Higher concentration among very few borrowers => higher credit risk Lower concentration among many borrowers => lower credit risk Default dependency Higher default dependency => higher credit risk Lower default dependency => lower credit risk
8.
Copyright © 2018
CapitaLogic Limited 8 Homogeneous portfolio Theory development Simplicity Highly analytical tractability Analytical approximation to a real debt portfolio Similar debts are managed under the same portfolio Around 5% to 10% model error
9.
Copyright © 2018
CapitaLogic Limited 9 Unified maturity RM is artificially set to one year The lender invests in many debts with maturity longer than one year or without fixed maturity will review and control the credit risk at the end of the following one year The lender invests in many debts with maturity shorter than one year will invest the proceeds at maturity in similar debts up to one year The debts with maturity short than one year accounts for the minority of the homogeneous portfolio (< 10%)
10.
Copyright © 2018
CapitaLogic Limited 10 Outline Credit risk identification Independent homogeneous portfolio Gaussian copula Finite homogeneous portfolio Infinite homogeneous portfolio Appendices
11.
Independent homogeneous portfolio
Portfolio EAD Shared equally among all borrowers LGD Same for all debts PD Same for all borrowers NOB > 30 Borrowers totally independent of one another Copyright © 2018 CapitaLogic Limited 11
12.
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CapitaLogic Limited 12 Credit risk factors – Independent homogeneous portfolio Credit risk Default loss Portfolio exposure at default Default chance Loss given default Probability of default Diversification effect No. of Borrowers (-)
13.
Copyright © 2018
CapitaLogic Limited 13 Combination The number of ways to place k objects in N slots where the order of the k objects does NOT matter N k N × N - 1 × N - 2 × ... × 3 × 2 × 1 C = k × k - 1 × k - 2 × ... × 3 × 2 × 1 × N - k × N - k - 1 × N - k - 2 × ... × 3 × 2 × 1
14.
Copyright © 2018
CapitaLogic Limited 14 Combination A and B in five slots [AB***] [A*B**] [A**B*] [A***B] [*AB**] [*A*B*] [*A**B] [**AB*] [**A*B] [***AB] B and A in five slots [BA***] [B*A**] [B**A*] [B***A] [*BA**] [*B*A*] [*B**A] [**BA*] [**B*A] [***BA]
15.
Copyright © 2018
CapitaLogic Limited 15 Default status of five borrowers One default (1) among five borrowers [10000] [01000] [00100] [00010] [00001] Two defaults (1,1) among five borrowers [11000] [10100] [10010] [10001] [01100] [01010] [01001] [00110] [00101] [00011]
16.
Copyright © 2018
CapitaLogic Limited 16 Binomial distribution Probability mass function Cumulative probability distribution function Average = PD × NOB NOB-kk NOB k M NOB-kk NOB k k=0 Probability k defaults out of NOB borrowers = C × PD × 1 - PD Probability Up to M defaults out of NOB borrowers = C × PD × 1 - PD Example 3.3
17.
Copyright © 2018
CapitaLogic Limited 17 Extreme case default rate Extreme case no. of defaults In Microsoft Excel Extreme case default rate Q NOB-kk NOB k k=0 C × PD × 1 - PD = 99.9% Q = Critbinom NOB, PD, 99.9% Q XCDR = NOB Example 3.4
18.
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CapitaLogic Limited 18 Cumulative probability of up to k defaults out of NOB borrowers
19.
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CapitaLogic Limited 19 Portfolio credit risk measure Extreme case loss XCL = Portfolio EAD × LGD × XCDR
20.
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CapitaLogic Limited 20 Diversification effect to extreme case loss For fixed portfolio EAD, LGD and PD Lower concentration of borrowers Larger NOB Smaller XCDR Smaller XCL Higher concentration of borrowers Smaller NOB Larger XCDR Larger XCL
21.
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CapitaLogic Limited 21 Outline Credit risk identification Independent homogeneous portfolio Gaussian copula Finite homogeneous portfolio Infinite homogeneous portfolio Appendices
22.
Copyright © 2018
CapitaLogic Limited 22 Bernoulli random variable A random no. B Either 1 with probability PD Or 0 with probability 1 - PD If B = 1, then the borrower defaults
23.
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CapitaLogic Limited 23 Standard uniform random variable A random no. u between 0 and 1 If u < PD, then the borrower defaults
24.
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CapitaLogic Limited 24 Standard normal random variable A real random no. z Mapped to a standard uniform random variable u If u < PD, then the borrower defaults 2 z - 1 τ u = exp - dτ = Normsdist z 22π 0 u 1 Example 3.5
25.
Copyright © 2018
CapitaLogic Limited 25 Standard normal random variable If y and z are independent standard normal random variables then x is also a standard normal random variable 2 2 x = y ρ + z 1 - ρ E x = E y ρ + z 1 - ρ = E y ρ + E z 1 - ρ = 0 ρ + 0 1 - ρ = 0 Var x = Var y ρ + z 1 - ρ = Var y ρ + Var z 1 - ρ = 1 ρ + 1 1 - ρ = 1 SD x = Var x =1
26.
Copyright © 2018
CapitaLogic Limited 26 Modelling one borrower A systematic standard normal random variable y A specific standard normal random variable z If u < PD, then the borrower defaults u = Φ y ρ + z 1 - ρ Example 3.6
27.
Copyright © 2018
CapitaLogic Limited 27 Correlated standard normal random variables If y, z1 and z2 are independent standard normal random variables then x1 and x2 are standard normal random variables with copula correlation coefficient ρ 1 1 2 2 1 2 1 2 1 2 1 2 1 1 2 x = y ρ + z 1 - ρ x = y ρ + z 1 - ρ Cov x ,x = Cov y ρ + z 1 - ρ, y ρ + z ρ = Cov y, y ρ ρ + Cov y,z ρ 1 - ρ + Cov y,z ρ 1 - ρ + Cov z ,z 1 - ρ 1 - ρ = 1 ρ + 0 + 0 + 0 = ρ Cov x ,x Corr x ,x = 2 1 2 ρ = = ρ = CCC SD x SD x 1 1
28.
Copyright © 2018
CapitaLogic Limited 28 Modelling two borrowers with same PD A systematic standard normal random variable y Two specific standard normal random variables z1 and z2 Mapped to standard uniform random variables u1 and u2 If u1 < PD, then borrower 1 defaults If u2 < PD, then borrower 2 defaults The larger the CCC, the higher the default dependency between the two borrowers 1 1 2 2 u = Φ y CCC + z 1 - CCC u = Φ y CCC + z 1 - CCC Example 3.7
29.
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CapitaLogic Limited 29 Homogeneous borrowers NOB different borrowers Same PD Same CCC between any two borrowers
30.
Copyright © 2018
CapitaLogic Limited 30 Modelling NOB homogeneous borrowers A systematic standard normal random variable y NOB specific standard normal random variables z1, z2, z3, … zNOB Mapped to standard uniform random variables u1, u2, u3, … uNOB If uk < PD, then borrower k defaults The larger the CCC, the higher the default dependency among the NOB borrowers k ku = Φ y CCC + z 1 - CCC k=1,2,3,...NOB Example 3.8
31.
Copyright © 2018
CapitaLogic Limited 31 CCC for retail exposures Residential mortgage Qualifying revolving retail exposure Other retail exposure CCC = 0.15 CCC = 0.04 CCC = 0.03 + 0.13exp -35PD
32.
Copyright © 2018
CapitaLogic Limited 32 CCC for institution exposures Institution exposures Small and medium enterprise Annual revenue (S) between EUR 5 mn and 50 mn Large financial institution Total assets > USD 100 bn CCC = 0.12 1 + exp -50PD CCC = 0.12 1 + exp -50PD CCC = S - 50 + 1125 0 1 + ex.15 p -50PD
33.
Copyright © 2018
CapitaLogic Limited 33 CCC under Basel III
34.
Copyright © 2018
CapitaLogic Limited 34 Outline Credit risk identification Independent homogeneous portfolio Gaussian copula Finite homogeneous portfolio Infinite homogeneous portfolio Appendices
35.
Finite homogeneous portfolio
Portfolio EAD Shared equally among all borrowers LGD Same for all debts PD Same for all borrowers NOB > 30 CCC Same between any two borrowers Copyright © 2018 CapitaLogic Limited 35
36.
Copyright © 2018
CapitaLogic Limited 36 Credit risk factors – Finite homogeneous portfolio Credit risk Default loss Portfolio exposure at default Default chance Loss given default Probability of default Diversification effect No. of Borrowers (-) Copula correlation coefficient
37.
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CapitaLogic Limited 37 Diversification effect Concentration of debts Measured by the NOB Approaching one when fully concentrated Approaching infinity when fully granular Default dependency Quantified by the CCC
38.
Copyright © 2018
CapitaLogic Limited 38 Monte Carlo simulation Generate a systematic standard normal random no. y For each borrower k (k = 1 to NOB) Generate a specific standard normal random no. zk Map to standard uniform random no. uk If uk < PD, then borrower k defaults Register the no. of borrowers in default Repeat the above steps for 1,000,000 time k ku = Φ y CCC + z 1 - CCC Example 3.12 Example 3.13
39.
Copyright © 2018
CapitaLogic Limited 39 Portfolio credit risk measure Extreme case no. of defaults Extreme case default rate Extreme case loss Extreme case no. of defaults = Percentile No. of defaults, 99.9% Extreme case no. of defaults XCDR = NOB XCL = Portfolio EAD × LGD × XCDR
40.
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CapitaLogic Limited 40 XCL of finite homogeneous portfolio CCC NOB PD XCDR LGD Portfolio EAD XCL (-) (+) (+) (+) (+) (+)
41.
Copyright © 2018
CapitaLogic Limited 41 Properties of the XCL Smaller XCL for Smaller portfolio EAD and LGD – less loss upon default Smaller PD – higher credit quality Larger NOB – lower concentration Smaller CCC – lower default dependency Lower risk for Larger portfolio EAD and LGD – more loss upon default Larger PD – lower credit quality Smaller NOB – higher concentration Larger CCC – higher default dependency XCL is a good quantitative measure of credit risk for finite homogeneous portfolio Having taken into account the diversification effect
42.
Copyright © 2018
CapitaLogic Limited 42 Outline Credit risk identification Independent homogeneous portfolio Gaussian copula Finite homogeneous portfolio Infinite homogeneous portfolio Appendices
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Infinite homogeneous portfolio
Portfolio EAD Shared equally among all borrowers LGD Same for all debts PD Same for all borrowers NOB → Infinity CCC Same between any two borrowers Default rate (“DR”) The percentage of borrowers in default Copyright © 2018 CapitaLogic Limited 43
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CapitaLogic Limited 44 Credit risk factors – Infinite homogeneous portfolio Credit risk Default loss Portfolio exposure at default Default chance Loss given default Probability of default Diversification effect Copula correlation coefficient
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CapitaLogic Limited 45 Vasicek default rate distribution 22 -1 -1-1 2 -1 -1-1 DR 0 -1 -1 1 - CCC Φ DR - Φ PDΦ DR1 - CCC f DR = exp - CCC 2 2CCC Φ PD - 1 - CCC Φ τΦ τ1 - CCC F DR = exp - dτ CCC 2 2CCC 1 - CCC Φ DR - Φ PD = Φ CCC Probability density function Cumulative probability distribution function
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CapitaLogic Limited 46 Vasicek default rate distribution 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 0% 20% 40% 60% 80% 100% Default rate Defaultratedensity.
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CapitaLogic Limited 47 Vasicek default rate model Mean Extreme case default rate -1 -1 Mean = PD Φ PD + CCC × Φ 99.9% XCDR = Φ 1 - CCC Example 3.9
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CapitaLogic Limited 48 XCDR vs PD and CCC
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CapitaLogic Limited 49 Portfolio credit risk measure Extreme case loss XCL = Portfolio EAD × LGD × XCDR Example 3.10
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CapitaLogic Limited 50 Diversification effect to extreme case loss For fixed portfolio EAD, LGD and PD Lower default dependency among borrowers Smaller CCC Smaller XCDR Smaller XCL Higher default dependency among borrowers Larger CCC Larger XCDR Larger XCL
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CapitaLogic Limited 51 Application of infinite homogeneous portfolio To approximate a real debt portfolio with similar debts lent to many similar but different borrowers Similar debts Similar EAD Similar LGD Similar PD RM unified to one year Similar borrowers – borrowers with Similar credit quality Similar default dependency between any two borrowers
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CapitaLogic Limited 52 Model validity of infinite homogeneous portfolio Risk factor Criteria EAD Coefficient of variation < 10% LGD Coefficient of variation < 10% PD Same credit rating or FICO score category RM Longer/Non-fixed term debts subject to review and control Short term debts subject to re-investment Short term debts < 10% NOB > 300 CCC Same CCC formula Example 3.11
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CapitaLogic Limited 53 Loss distribution of a single debt
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CapitaLogic Limited 54 Loss distribution of a debt portfolio
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CapitaLogic Limited 55 Outline Credit risk identification Independent homogeneous portfolio Gaussian copula Finite homogeneous portfolio Infinite homogeneous portfolio Appendices
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CapitaLogic Limited 56 Debt basket A collection of debts lent to a smaller number of borrowers from the same lender 1 1 2 2 3 3 NOB NOB EL 1-year EL EL 1-year EL = EL = 1-year EL EL 1-year EL Basket EL Basket 1- year EL
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CapitaLogic Limited 57 Finite homogeneous portfolio k -12 NOB k NOB-k- -1 -12 NOB k Probability k defaults out of NOB borrowers Φ PD - τ CCCτ exp - Φ 2 1 - CCCC = dτ 2π Φ PD - τ CCC 1 - Φ 1 - CCC Φ PD - τ CCCτ exp - Φ 2 1 - CCCC 2π k 5 NOB-k-5 -1 dτ Φ PD - τ CCC 1 - Φ 1 - CCC Example 3.14
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CapitaLogic Limited 58 Finite homogeneous portfolio k -1 M NOB k NOB-k- -1k=0 Probability Up to M defaults out of NOB borrowers Φ PD - τ CCCτ exp - Φ 2 1-CCCC dτ 2π Φ PD - τ CCC 1 - Φ 1-CCC Ave rage = PD NOB
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CapitaLogic Limited 59 Finite homogeneous portfolio Extreme case no. of defaults Extreme case default rate k -12 Q NOB k NOB-k- -1k=0 Φ PD - τ CCCτ exp - Φ 2 1 - CCCC dτ = 99.9% 2π Φ PD - τ CCC 1 - Φ 1 - CCC Q XCDR = NOB Example 3.15