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Michael K. On Professor and Director, Finance Progra Executive Director, Center for Financial Market Stuart Graduate School of Busines Illinois Institute of Technolog 312.906.6568. [email protected] Managing Credit Managing Credit Risk Risk in Volatile in Volatile Environments Environments Energy Credit Risk Congress 2003 The Houstonian Hotel, Houston, TX November 12-13, 2003 Organized by Risk Limited Return to Risk Limited website: www.RiskLimited.com

Michael K. Ong Professor and Director, Finance Program

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Michael K. Ong Professor and Director, Finance Program Executive Director, Center for Financial Markets Stuart Graduate School of Business Illinois Institute of Technology 312.906.6568. [email protected]. Return to Risk Limited website: www.RiskLimited.com. Managing Credit Risk - PowerPoint PPT Presentation

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Page 1: Michael K. Ong Professor and Director, Finance Program

Michael K. OngProfessor and Director, Finance Program

Executive Director, Center for Financial MarketsStuart Graduate School of Business

Illinois Institute of Technology312.906.6568. [email protected]

Managing Credit Managing Credit RiskRisk

in Volatile in Volatile EnvironmentsEnvironments

Energy Credit Risk Congress 2003The Houstonian Hotel, Houston, TXNovember 12-13, 2003Organized by Risk Limited

Return to Risk Limited website: www.RiskLimited.com

Page 2: Michael K. Ong Professor and Director, Finance Program

Widening of Credit Spreads - Widening of Credit Spreads - a Precursor of Something Ominousa Precursor of Something Ominous

Source: William J. Bernstein, “Credit Risk: How Much? When?”, www.efficientfrontier.com.

Page 3: Michael K. Ong Professor and Director, Finance Program

Sharp Increase in Default RatesSharp Increase in Default Rates

Source: Moody’s Special Comment “Default & Recovery Rates of Corporate Bond Issuers”, February 2003.

Page 4: Michael K. Ong Professor and Director, Finance Program

Default Rate for All Corporate IssuersDefault Rate for All Corporate IssuersReached Extreme in 2001Reached Extreme in 2001

Source: David Hamilton, “Historical Corporate Rating Migration, Default, and Recovery Rates”, 2002

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

4.5%

5.0%

0.5%

1.0%

0.0%

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

Average

+1 Std Dev

+2 Std Dev

Page 5: Michael K. Ong Professor and Director, Finance Program

10 Largest Rated Corporate Bond Defaults10 Largest Rated Corporate Bond Defaults

1) 1) WorldCom, Inc.WorldCom, Inc. $23.2 Billion$23.2 Billion

2) 2) Enron Corp. Enron Corp. $9.9 Billion$9.9 Billion

3) 3) NTL Communications Corp.NTL Communications Corp. $8.5 Billion$8.5 Billion

4) 4) Adelphia Communications Corp.Adelphia Communications Corp. $6.9 Billion$6.9 Billion

5) 5) Finova Capital Corp.Finova Capital Corp. $6.3 Billion$6.3 Billion

6) 6) United Pan-Europe CommunicationsUnited Pan-Europe Communications $5.1 Billion$5.1 Billion

7) 7) Pacific Gas & Electric Co.Pacific Gas & Electric Co. $5.0 Billion$5.0 Billion

8) 8) XO Communications Inc.XO Communications Inc. $4.9 Billion$4.9 Billion

9) 9) Southern California Edison Co. Southern California Edison Co. $4.7 Billion$4.7 Billion

10) 10) Global Crossing Holdings LTD Global Crossing Holdings LTD $3.8 Billion$3.8 Billion

Total:Total: $78.3 Billion$78.3 BillionSource: David Hamilton, “Historical Corporate Rating Migration, Default, and Recovery Rates”, 2002

Page 6: Michael K. Ong Professor and Director, Finance Program

Energy Company ProfilesEnergy Company ProfilesFunded BankFunded Bank Bank MaturitiesBank MaturitiesExposure MaturingExposure Maturing As % of Total DebtAs % of Total Debtin 2003-2006in 2003-2006 MaturitiesMaturities

RatingRating OutlookOutlook ($ Millions)($ Millions) 2003-20062003-2006

American Electric PowerAmerican Electric Power BBB+BBB+ StableStable 1,2441,244 1616AES Corp.AES Corp. B+B+ Watch NegativeWatch Negative 2,4832,483 4747Allegheny EnergyAllegheny Energy BBBB Watch Negative Watch Negative 1,0401,040 4646AquilaAquila BBB-BBB- NegativeNegative 356356 3636Black Hills CorpBlack Hills Corp BBBBBB StableStable 558558 9999Calpine CorpCalpine Corp BBBB NegativeNegative 5,5005,500 7575CMS EnergyCMS Energy BBBB NegativeNegative 2,7402,740 6969Constellation Energy GroupConstellation Energy Group A-A- StableStable 296296 1919Dominion ResourcesDominion Resources BBB+BBB+ StableStable 640640 1010Duke EnergyDuke Energy AA StableStable 2,3502,350 2727DynegyDynegy B+B+ Watch NegativeWatch Negative 1,9001,900 6464El Paso CorpEl Paso Corp BBB+BBB+ Watch NegativeWatch Negative 920920 1717Edison Mission EnergyEdison Mission Energy BBB-BBB- Watch NegativeWatch Negative 1,8381,838 100100EntergyEntergy BBBBBB StableStable 950950 3636MirantMirant BBBB NegativeNegative 3,6143,614 7171PG& E National Energy GroupPG& E National Energy Group B-B- Watch NegativeWatch Negative 2,4582,458 9191NRG EnergyNRG Energy DD -- 4,2874,287 9292PPLPPL BBBBBB NegativeNegative 248248 1616Public Service Enterprise GroupPublic Service Enterprise Group BBBBBB StableStable 833833 2727Teco EnergyTeco Energy BBBBBB Watch NegativeWatch Negative 340340 5959TXUTXU BBBBBB NegativeNegative 1,0941,094 1313Williams CompaniesWilliams Companies B+B+ Watch NegativeWatch Negative 2,0002,000 3232

TotalTotal 37,57037,570 Mean 48Mean 48

Source: Standard and Poors, November 2002, as reported by James Ockendon, “Delaying the Inevitable?”, Energy Power Risk Management, May 2003

Page 7: Michael K. Ong Professor and Director, Finance Program

Behavioral Reactions to UncertaintyBehavioral Reactions to Uncertainty

• Defining credit cultureDefining credit culture

• Corporate prioritiesCorporate priorities

““How much risk?” versus “How much return?”How much risk?” versus “How much return?”

• Credit disciplineCredit discipline

Proper metrics for risk and performance measurement.Proper metrics for risk and performance measurement.

““A credit culture is made up of principles that need to be A credit culture is made up of principles that need to be communicated. A credit culture is rooted in corporate attitudes, communicated. A credit culture is rooted in corporate attitudes, philosophies, traditions, and standards that require philosophies, traditions, and standards that require administrative underpinnings. The role of credit culture is to administrative underpinnings. The role of credit culture is to create a risk management climate that will foster … good create a risk management climate that will foster … good banking …” banking …” Henry MullerHenry Muller**

* Henry Muller, “Risk Management and the Credit Culture - A Necessary Interaction”, Credit Risk Management (Robert Morris Associates) 1995.

Page 8: Michael K. Ong Professor and Director, Finance Program

Quantitative Tools for Managing Credit RiskQuantitative Tools for Managing Credit Risk• Loan Loss Reserves and Capital Allocation Loan Loss Reserves and Capital Allocation

Commitment and Outstanding (in $Millions) *

1458113587 13178

1189310904

9530

7095 6359 58635436 4968 4501 4310 4033 3670 3059 2740 2582

$-$2,000

$4,000$6,000

$8,000$10,000$12,000$14,000

$16,000

Q1 2001 Q2 2001 Q3 2001 Q4 2001 Q1 2002 Q2 2002 Q3 2002 Q4 2002 Q1 2003

General Loan Loss Reserve (in $Millions) *

116129

115125 130 129

99 10190

$-

$20

$40

$60

$80

$100

$120

$140

Q1 2001 Q2 2001 Q3 2001 Q4 2001 Q1 2002 Q2 2002 Q3 2002 Q4 2002 Q1 2003

* Sample for illustrative purposes only.

Page 9: Michael K. Ong Professor and Director, Finance Program

Quantitative Tools for Managing Credit RiskQuantitative Tools for Managing Credit Risk• Measures for Concentration Risk Measures for Concentration Risk

* Sample for illustrative purposes only.

Industry Sector Concentration Report: Entire US. Lending Portfolio at 5/30/03 *

Household & Personal Products

0%

Hotels Restaurants & Leisure

1%

Health Care Equipment & Services

1%

Insurance2%

Food Beverage & Tobacco6%

Food & Drug Retailing2%

Energy11%

Materials7%

Other6%

Real Estate0%

Pharmaceuticals & Biotechnology

2%

Securitization12%

Softw are & Services0%

Utilities8%

Transportation1%

Telecommunication Services

3%

Media3%

Technology Hardw are & Equipment

2%

Automobiles & Components4%

Banks12%

Diversif ied Financials6%

Retailing2%

Commercial Services & Supplies

2%Capital Goods

7%

Consumer Durables & Apparel

2%

Page 10: Michael K. Ong Professor and Director, Finance Program

Quantitative Tools for Managing Credit RiskQuantitative Tools for Managing Credit Risk• Top 10 Exposures, Risk and Return Characteristics, etc.Top 10 Exposures, Risk and Return Characteristics, etc.

Expected LossExpected Loss CommitmentCommitment OutstandingOutstanding1) 1) American AirlinesAmerican Airlines $16,765,137$16,765,137 $65,000,000$65,000,000 $65,000,000$65,000,0002) 2) Enron Corp. Enron Corp. $14,223,195$14,223,195 $92,500,000$92,500,000 $50,000,000$50,000,0003) 3) FMI International, LLCFMI International, LLC $14,052,022$14,052,022 $28,000,000$28,000,000 $19,342,252$19,342,2524) 4) Xerox Corp.Xerox Corp. $13,996,139 $13,996,139 $95,000,000$95,000,000 $45,000,100$45,000,1005) 5) Viasystems, Inc.Viasystems, Inc. $10,000,001$10,000,001 $18,250,000$18,250,000 $12,500,000$12,500,0006) 6) Napoleon Holdings, Inc.Napoleon Holdings, Inc. $9,875,325$9,875,325 $27,500,000$27,500,000 $25,333,333$25,333,3337) 7) Pacific Gas & Electric Co.Pacific Gas & Electric Co. $9,750,386$9,750,386 $20,000,000$20,000,000 $20,000,000$20,000,0008) 8) Revlon Consumer Products Corp.Revlon Consumer Products Corp. $8,491,111$8,491,111 $15,000,000$15,000,000 $14,923,021$14,923,0219) 9) Texas Industries, Inc.Texas Industries, Inc. $8,477,299$8,477,299 $25,750,000$25,750,000 $15,000,000$15,000,00010) 10) Hamilton Beach Proctor Silex Hamilton Beach Proctor Silex $7,768,894$7,768,894 $17,000,000$17,000,000 $17,000,000$17,000,000

Chicago Portfolio’s 10 Largest Expected LossesChicago Portfolio’s 10 Largest Expected Losses**

* Sample for illustrative purposes only.

Page 11: Michael K. Ong Professor and Director, Finance Program

Quantitative Tools for Managing Credit RiskQuantitative Tools for Managing Credit Risk• Top 10 Exposures, Risk and Return Characteristics, etc.Top 10 Exposures, Risk and Return Characteristics, etc.

Commitment Amount *Commitment Amount *

General Index6%

Oil Companies-Major6%

Remaining Industries

35%

Banks12%

Diversified Financial12%

Electric Utilities9%

Communications Technology

3%

Securities Brokers3% Chemicals

4%Gas utilities

5%

Food5%

* Sample for illustrative purposes only.

Page 12: Michael K. Ong Professor and Director, Finance Program

Quantitative Tools for Managing Credit RiskQuantitative Tools for Managing Credit Risk• Top 10 Exposures, Risk and Return Characteristics, etc.Top 10 Exposures, Risk and Return Characteristics, etc.

Utilization *Utilization *

Banks15%

Diversified Financial2%

Electric Utilities14%

General Index4%

Oil Companies-Major7%

Remaining Industries37%

Food6%

Gas utilities9%

Chemicals3%

Securities Brokers1%

Communications Technology

2%

* Sample for illustrative purposes only.

Page 13: Michael K. Ong Professor and Director, Finance Program

Quantitative Tools for Managing Credit RiskQuantitative Tools for Managing Credit Risk• Top 10 Exposures, Risk and Return Characteristics, etc.Top 10 Exposures, Risk and Return Characteristics, etc.

Projected Annual Income *Projected Annual Income *

Electric Utilities9%

General Index1%

Oil Companies-Major6%

Food5%

Remaining Industries53%

Securities Brokers0%

Chemicals2%

Communications Technology

4%

Banks6%

Diversified Financial2%

Gas utilities12%

* Sample for illustrative purposes only.

Page 14: Michael K. Ong Professor and Director, Finance Program

Quantitative Tools for Managing Credit RiskQuantitative Tools for Managing Credit Risk• Top 10 Exposures, Risk and Return Characteristics, etc.Top 10 Exposures, Risk and Return Characteristics, etc.

Expected Loss *Expected Loss *

Electric Utilities18%

General Index2%

Oil Companies-Major1%

Securities Brokers0%

Remaining Industries

52%

Food2%

Communications Technology

2%

Diversified Financial0%Banks

1%

Chemicals13%

Gas utilities9%

* Sample for illustrative purposes only.

Page 15: Michael K. Ong Professor and Director, Finance Program

Quantitative Tools for Managing Credit RiskQuantitative Tools for Managing Credit Risk• Top 10 Exposures, Risk and Return Characteristics, etc.Top 10 Exposures, Risk and Return Characteristics, etc.

Remaining Industries53%

Gas utilities20%

Chemicals6%

Communications Technology

3%

Food1%

Electric Utilities7%

Oil Companies-Major2%

General Index3%

Diversified Financial1%

Securities Brokers0%

Banks4%

Risk Capital *Risk Capital *

* Sample for illustrative purposes only.

Page 16: Michael K. Ong Professor and Director, Finance Program

Quantitative Tools for Managing Credit RiskQuantitative Tools for Managing Credit Risk• Top 10 Exposures, Risk and Return Characteristics, etc.Top 10 Exposures, Risk and Return Characteristics, etc.

* Sample for illustrative purposes only.

Commitment and Outstanding (in $Millions) *

$9,604 $9,249 $9,137 $8,783 $8,645 $8,414 $8,119 $8,133 $7,902 $7,813 $7,845 $8,014

$3,985 $4,265 $4,179 $4,033 $3,816 $3,776 $3,596 $3,671 $3,503 $3,313 $3,278 $3,275

$-

$2,000

$4,000

$6,000

$8,000

$10,000

$12,000

Jun-02 Jul-02 Aug-02 Sep-02 Oct-02 Nov-02 Dec-02 Jan-03 Feb-03 Mar-03 Apr-03 May-03

Expected Loss (in $Millions) *

$207 $201 $204 $207 $208 $220

$168 $166 $173 $176 $176 $180

$-

$50

$100

$150

$200

$250

Jun-02 Jul-02 Aug-02 Sep-02 Oct-02 Nov-02 Dec-02 Jan-03 Feb-03 Mar-03 Apr-03 May-03

Page 17: Michael K. Ong Professor and Director, Finance Program

Quantitative Tools for Managing Credit RiskQuantitative Tools for Managing Credit Risk• Top 10 Exposures, Risk and Return Characteristics, etc.Top 10 Exposures, Risk and Return Characteristics, etc.

* Sample for illustrative purposes only.

Unexpected Loss (in $Millions) *

$69 $66 $64 $65 $67 $64 $63$69 $68

$59 $60 $59

$-$10$20

$30$40

$50$60$70

$80

Jun-02 Jul-02 Aug-02 Sep-02 Oct-02 Nov-02 Dec-02 Jan-03 Feb-03 Mar-03 Apr-03 May-03

Risk Capital (in $Millions) *

$498 $486 $511$463 $493

$457 $479 $491 $467 $457 $439 $462

$-

$100

$200

$300

$400

$500

$600

Jun-02 Jul-02 Aug-02 Sep-02 Oct-02 Nov-02 Dec-02 Jan-03 Feb-03 Mar-03 Apr-03 May-03

Page 18: Michael K. Ong Professor and Director, Finance Program

Quantitative Tools for Managing Credit RiskQuantitative Tools for Managing Credit Risk• Top 10 Exposures, Risk and Return Characteristics, etc.Top 10 Exposures, Risk and Return Characteristics, etc.

* Sample for illustrative purposes only.

RAROC *

9% 9%8%

10%11%

10% 10%9% 9%

11%12% 12%

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%Jun-02 Jul-02 Aug-02 Sep-02 Oct-02 Nov-02 Dec-02 Jan-03 Feb-03 Mar-03 Apr-03 May-03

RAROC by Risk Rating *

-40%

-20%

0%

20%

40%

60%

80%A B C D E F

Jun-02

Jul-02

Aug-02

Sep-02

Oct-02

Nov-02

Dec-02

Jan-03

Feb-03

Mar-03

Apr-03

May-03

Page 19: Michael K. Ong Professor and Director, Finance Program

Quantitative Tools for Managing Credit RiskQuantitative Tools for Managing Credit Risk• Top 10 Exposures, Risk and Return Characteristics, etc.Top 10 Exposures, Risk and Return Characteristics, etc.

Commitment by Risk Rating (in $Millions)

0

500

1,000

1,500

2,000

2,500

3,000

A B C D E F

Jun-02

Jul-02

Aug-02

Sep-02

Oct-02

Nov-02

Dec-02

Jan-03

Feb-03

Mar-03

Apr-03

May-03

Outstanding by Risk Rating (in $Millions)

0

200

400

600

800

1,000

1,200

A B C D E F

Jun-02

Jul-02

Aug-02

Sep-02

Oct-02

Nov-02

Dec-02

Jan-03

Feb-03

Mar-03

Apr-03

May-03

* Sample for illustrative purposes only.

Page 20: Michael K. Ong Professor and Director, Finance Program

Quantitative Tools for Managing Credit RiskQuantitative Tools for Managing Credit Risk• Scenario analysis Scenario analysis

Changes in exposure, risk ratings, etc.Changes in exposure, risk ratings, etc.

Loss Distribution

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

0.0%

1.5%

2.9%

4.4%

5.9%

7.4%

8.8%

10.3

%

11.8

%

13.3

%

14.7

%

16.2

%

17.7

%

19.2

%

20.6

%

22.1

%

23.6

%

25.1

%

26.5

%

28.0

%

29.5

%

30.9

%

32.4

%

33.9

%

35.4

%

36.8

%

Loss/Outstanding

Pro

babi

lity

* Sample for illustrative purposes only.

Page 21: Michael K. Ong Professor and Director, Finance Program

Quantitative Tools for Managing Credit RiskQuantitative Tools for Managing Credit Risk

Loss Distribution

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

0.0%

1.5%

2.9%

4.4%

5.9%

7.4%

8.8%

10.3

%

11.8

%

13.3

%

14.7

%

16.2

%

17.7

%

19.2

%

20.6

%

22.1

%

23.6

%

25.1

%

26.5

%

28.0

%

29.5

%

30.9

%

32.4

%

33.9

%

35.4

%

36.8

%

Loss/Outstanding

Pro

babi

lity

• Scenario analysis Scenario analysis

Changes in exposure, risk ratings, etc.Changes in exposure, risk ratings, etc.

* Sample for illustrative purposes only.

Page 22: Michael K. Ong Professor and Director, Finance Program

Quantitative Tools for Managing Credit RiskQuantitative Tools for Managing Credit Risk

Loss Distribution

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

0.0%

1.5%

2.9%

4.4%

5.9%

7.4%

8.8%

10.3

%

11.8

%

13.3

%

14.7

%

16.2

%

17.7

%

19.2

%

20.6

%

22.1

%

23.6

%

25.1

%

26.5

%

28.0

%

29.5

%

30.9

%

32.4

%

33.9

%

35.4

%

36.8

%

Loss/Outstanding

Pro

babi

lity

• Scenario analysis Scenario analysis

Changes in exposure, risk ratings, etc.Changes in exposure, risk ratings, etc.

* Sample for illustrative purposes only.

Page 23: Michael K. Ong Professor and Director, Finance Program

Credit Approval ProcessCredit Approval Process• Credit culture and corporate governanceCredit culture and corporate governance

• Committee structure and accountabilityCommittee structure and accountability

• Loan commitments (“intermediary”) versus investment banking activitiesLoan commitments (“intermediary”) versus investment banking activities

“ “How many 364-day facilities to commit? How many loan How many 364-day facilities to commit? How many loan commitments are at risk of being drawn in a corporation downgrade? commitments are at risk of being drawn in a corporation downgrade?

• Vital role of credit ratingsVital role of credit ratings

Internal risk ratings systems versus agencies ratings.Internal risk ratings systems versus agencies ratings.

• Links to private equityLinks to private equity

Page 24: Michael K. Ong Professor and Director, Finance Program

Loan Review Process and the Role of WorkoutLoan Review Process and the Role of Workout• Review processReview process

Monitoring frequency.Monitoring frequency.

What drives an upgrade/downgrade?What drives an upgrade/downgrade?

• Disciplined approachDisciplined approach

• Close involvement of relationship managers, risk managers, and Close involvement of relationship managers, risk managers, and investment bankersinvestment bankers

• When does “Workout” begin?When does “Workout” begin?

Asset and collateral valuationAsset and collateral valuation

Expected lossExpected loss

Restructuring schemesRestructuring schemes

Page 25: Michael K. Ong Professor and Director, Finance Program

Credit Culture RevisitedCredit Culture Revisited• Communicating risk and hedging policiesCommunicating risk and hedging policies

Limits on concentration risk.Limits on concentration risk.

Use of credit derivatives.Use of credit derivatives.

• List of qualified buy/sell protection namesList of qualified buy/sell protection names

• Curse of concentrationCurse of concentration

Page 26: Michael K. Ong Professor and Director, Finance Program

New Paradigm in Lending - “Credit Trading”New Paradigm in Lending - “Credit Trading”

• Traditional approach - “buy-and-hold”Traditional approach - “buy-and-hold”

New paradigm in lending - “strategic credit risk management”New paradigm in lending - “strategic credit risk management”

Try to GetI-BankingBusiness

Underwriting Approval Hold onBalance Sheet

Monitoring andAdministration

Credit RiskDispersionUnderwriting Approval Securitize

Page 27: Michael K. Ong Professor and Director, Finance Program

Concluding RemarkConcluding Remark

A good credit risk management process built onA good credit risk management process built onsound credit culture and solid principles can withstand sound credit culture and solid principles can withstand

any volatile market conditions.any volatile market conditions.