Transcript ERM!!! - University of Nebraska–Lincoln

ERM
David L. Olson, University of Nebraska-Lincoln
Desheng Wu, University of Reykjavik, University of Toronto
Enterprise Risk Management
Not just insurance, auditing, risk analysis
A philosophy – A way of business
Definition
• Systematic, integrated approach
– Manage all risks facing organization
•
External
–
–
–
–
–
•
Economic (market - price, demand change)
Financial (insurance, currency exchange)
Political/Legal
Technological
Demographic
Internal
–
–
–
–
Human error
Fraud
Systems failure
Disrupted production
• Means to anticipate, measure, control risk
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DIFFERENCES
Traditional Risk Mgmt
ERM
Individual hazards
Context - business strategy
Identification & assessment
Risk portfolio development
Focus on discrete risks
Focus on critical risks
Risk mitigation
Risk optimization
Risk limits
Risk strategy
No owners
Defined responsibilities
Haphazard quantification
Monitor & measure
“Not my job”
“Everyone’s responsibility”
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Risk & Business
• Taking risk is fundamental to doing
business
– Insurance
• Lloyd’s of London
– Hedging
• Risk exchange swaps
• Derivatives/options
• Catastrophe equity puts (cat-e-puts)
– ERM seeks to rationally manage these risks
• Be a Risk Shaper
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Types of Risk
Stroh [2005]
• External environment
– Competitors; Legal; Medical; Markets
• Business strategies & policies
– Capital allocation; Product portfolio; Policies
• Business process execution
– Planning; Technology; Resources
• People
– Leadership; Skills; Accountability; Fraud
• Analysis & reporting
– Performance; Budgeting; Accounting; Disclosure
• Technology & data
– Architecture; Integrity; Security; Recovery
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Another view
Slywotzky & Drzik, HBR [2005]
• Financial
– Currency fluctuation
• DEFENSE: Hedging
• Hazard
– Chemical spill
• DEFENSE: Insurance
• Operational
– Computer system failure
• DEFENSE: Backup (dispersion, firewalls)
• New technology overtaking your product
– ACE inhibitors, calcium channel blockers ate into hypertension
drug market of beta-blockers & diuretics
• Demand shifts
– Gradual – Oldsmobile; Rapid - Station wagons to Minivans
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Technology Shift
• Loss of patent protection
• Outdated manufacturing process
– DEFENSE: Double bet
•
•
•
•
Invest in multiple versions of technology
Microsoft: OS/2 & Windows
Intel: RISC & CISC
Motorola didn’t – Nokia, Samsung entered
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Brand Erosion
• Perrier – contamination
• Firestone – Ford Explorer
• GM Saturn – not enough new models
– DEFENSE: Redefine scope
• Emphasize service, quality
– DEFENSE: Reallocate brand investment
• AMEX – responded to VISA campaign, reduced
transaction fees, sped up payments, more ads
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One-of-a-kind Competitor
• Competitor redefines market
• Wal-Mart
– DEFENSE: Create new, non-overlapping
business design
• Target – unique product selection
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Customer Priority Shift
– DEFENSE: Analyze proprietary information
• Identify next customer shift
– Coach leather goods – competes with Gucci
– Went trendy, aggressive in-market testing
» Customer interviews, in-store product tests
– DEFENSE: Market experiments
• Capital One – 65,000 experiments annually
– Identify ever-smaller customer segments for credit cards
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New Project Failure
• Edsel
– DEFENSE: Initial analysis
• Best defense
– DEFENSE: Smart sequencing
• Do better-controllable projects first
– Applied Materials – chip-making
– DEFENSE: Develop excess options
• Improve odds of eventual success
– Toyota – hybrid: proliferation of Prius options
– DEFENSE: Stepping-stone method
• Create series of projects
– Toyota – rolling out Prius
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COSO
Committee of Sponsoring Organizations
Treadway Committee – 1990s
Smiechewicz [2001]
• Assign responsibility
– Board of directors
• Establish organization’s risk appetite
• establish audit & risk management policies
– Executives assume ownership
• Policies express position on integrity, ethics
• Responsibilities for insurance, auditing, loan review, credit,
legal compliance, quality, security
• Common language
– Risk definitions specific to organization
• Value-adding framework
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COSO Integrated Framework 2004
Levinsohn [2004]; Bowling & Rieger [2005]
• Internal environment – describe domain
• Objective setting – objectives consistent with
mission, risk appetite
• Event identification – risks/opportunities
• Risk assessment - analysis
• Risk response – based on risk tolerance &
appetite
• Control activities
• Information & communication – to responsible
people
• Monitoring
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Risk Management Tools
• Simulation (Beneda [2005])
– Monte Carlo – Crystal Ball
• Multiple criteria analysis
– Tradeoffs between risk & return
• Balanced Scorecard
– Organizational performance measurement
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ERM Software
Rhoden [2006]
Penny [2002]
• Algorithmics Incorporated – ERM software, global financial institutions
Jane’s Defence Industry [2005]
• Strategic Thought – Active Risk Manager – defence industry
Rhoden [2006]
• Q5AIMS
– From Q5 Systems Ltd
– Safety audit & corrective action tracking
– Mobile devices, Web-link
•
Preceptor
– Learning management system
– Regulatory compliance, technical training
•
PicketdynaQ
– Workplace audit & assessment management
– Regulatory references built in
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SIMULATION
• Crystal Ball
– Spreadsheet add-in
– Value at Risk (VaR)
• Distribution of expected value at specified
probability level
• >3.42 @ 0.95
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Spreadsheet
Sales
Year
1
10000
2
11000
3
12100
4
13310
5
14641
COGS
4500
5500
6500
7500
8500
Gross
5500
5500
5600
5810
6141
Fixed
5400
5500
5600
5700
5800
Net
100
0 1.82E-12
110
341
ATP
62
0 1.13E-12
68.2
211.42
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Stochastic Elements
these PRO FORMA models include a number of
inherently STOCHASTIC elements
– costs are really guesses
• can base variance on subjective estimates
• for repetitive operations, collect data
– revenues are even more uncertain
– discount rates in NPV uncertain
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Net Present Value
in i  out i
NPV = 
i
(1+
r)
i=0
n
where n = number of time periods in analysis
ini = revenues in period i
outi = cash outflow in period i
r = discount rate
i = END of time period
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EXCEL RN generation
• Options
– Analysis Tools
– Random Number Generation
» Output Range
» Number of Variables
» Number of Random Numbers
» Distribution
» Parameters
» Random Seed
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Sharpe Ratio
• Consider variance of stock as measure of
risk
– Tradeoff between mean and variance
– Efficient investment opportunities
7
6
5
4
mean
3
var
2
1
0
0
1
2
3
4
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6
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Simulation studies involving the
Sharpe ratio
• Opdyke – Journal of Asset Management [2008] 8:5, 308-336
– Simulated to reflect autocorrelation of distributions
• Yu et al. – Journal of Asset Management [2007] 8:2, 133-145
– Value-at-risk = max expected loss over a given time period at a given confidence
level
– Simulation showed simply using Sharpe ratio insufficient – need to reflect
covariance
• Chen & Estes – Journal of Financial Planning [2007] 20:2, 56-59
– Dollar-cost averaging for 401k contributions
– Simulated different strategies for contributions, allocation ratios, growth targets
as decision variables
• Boscaljon & Sun – Journal of Financial Service Professionals [2006]
60:5, 60-65
– Value-at-risk & return-at-risk more conservative than variance
– Simulated all 3
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Simulation studies involving
Black-Scholes model
• Alam – Journal of Economics & Finance [1992] 16:3, 1-20
• Figlewski et al. – Financial Analysts Journal [1993] 49:4, 46-56
• Barraquand & Martineau – Journal of Financial & Quantitative
Analysis [1995] 30:3, 383-405
• Frey – Finance & Stochastics [2000] 4:2, 161-187
• Gopal et al. – Decision Sciences [2005] 36:3, 397-425
• Fink & Fink – Journal of Applied Finance [2006] 16:2, 92-105
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Black-Scholes Option Pricing
• Model to value options
Price of call = Prob{x<d1}*S – Prob{x<d2}*E*e-rT
where
S = price of stock
E = exercise price
r = risk-free interest rate
T = time to maturity (years)
ln(S / E )  (r   2 / 2)T
d1 
 T
d2  d1   T
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Estimation of specification error biases
– Black-Scholes & Cox-Ross models
Alam, Journal of Economics & Finance, Fall 1992, 16:3, 1-20
• Black-Scholes
– assumes constant variance of returns
– Tends to underprice options at-the-money,
overprices at extremes (“u-shaped”)
• Cox-Ross
– Variance changes with stock price
– Analytically intractable
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Evaluating Performance of
Protective Put Strategy
Figlewski et al., Financial Analysts Journal, Jul/Aug 1993, 49:4, 46-56
• Having put in place protects portfolio from loss below
strike price
• Simulated 3 put strategies:
– Fixed strike price
– Strike price a fixed % below asset price
– Upward ratcheting policy
• Ignores buying, selling, settlement costs (taxes)
• Cost of put strategy is path dependent, thus only cost
effective if expect high volatility in market
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Numerical Valuation
Barraquand & Martineau, Journal of Financial & Quantitative Analysis, Sep 1995, 30:3, 383-405
• Cox-Ross does well for one asset, but
computational demands increase
exponentially
• Closed form solution unfound
• Monte-Carlo only tractable method
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Advanced Option Pricing
Fink & Fink, Journal of Applied Finance, Fall/Winter 2006, 16:2, 92-105
• Foreign currency options have volatility smiles (“ushaped”)
• Equity options have volatility skews (higher volatility for
lower strike prices)
• Bates model uses mean reversion for volatility estimates
• Simulated Black-Scholes, Merton & Heston, Bates
– Bates won easily
– Black Scholes inflexible (Merton & Heston better here)
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More efficient super-hedging
Frey, Finance & Stochastics, 2000, 4:2, 161-187
• Add descriptive, predictive power by
allowing variation of volatility estimate
• Hedge what you intend to hedge
– Minimize transactions costs
• Probabilistic argument
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Online Auction Risk
Gopal et al., Decision Sciences, Aug 2005, 36:3, 397-425
• Buyer’s risk – loser’s lament (bid too low &
lose; bid too high & pay too much)
• Seller’s risk – accept too low
• Simulation used to estimate volatility
• Searches through combinations of strike
price & option price
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Financial Simulations
• a very rich field for simulation
– high degrees of uncertainty in cash flows
• SPREADSHEETS for the most-part
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Iceland heating pipes
Mean Lognormal (30.76,38.61) – offset 30
MONTH Seasonal Differential from Mean
Apr
3.604167
May
10.45833
Jun
72.3125
Jul
46.5
Aug
-24.6458
Sep
1.875
Oct
29.0625
Nov
22.0833
Dec
-27.8958
Jan
-15.375
Feb
-26.5208
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Supply Chain Simulation
Produce to Forecast
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Supply Chain Simulation
Produce to ROP/Q
Q30
Q40
Q50
Q60
To forecast – 0 to 643, mean 50
AVG STOCKOUTS
ROP 30
468
495
440
393
ROP 40
421
366
398
352
ROP 50
377
324
287
313
ROP 60
334
283
249
223
To forecast – 81 to 559, mean 253
AVG HOLD
ROP 30
39
38
45
51
ROP 40
43
51
49
56
ROP 50
47
55
63
61
ROP 60
52
61
68
76
To forecast – 452 to 1281, mean 1032
AVG SALES
ROP 30
612
585
640
687
ROP 40
658
714
682
728
ROP 50
703
756
793
767
ROP 60
746
797
831
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Monte Carlo Simulation
Quoted
price
Exchange
distribution
Produc
t
failure
Organizati
onal
failure
Political
failure
China
0.82
No(1.3,.2)
0.10
0.15
0.05
2.13
Taiwan
1.36
No(1.03,.02)
0.01
0.01
0.10
1.81
Vietnam
0.85
No(1.1,.1)
0.15
0.25
0.05
2.51
Germany
3.20
No(1.05,.02)
0.01
0.02
0.01
3.43
Alabama
2.05
1
0.03
0.20
0.03
2.78
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Expected
price
37
China vendor price distribution
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Taiwan vendor price distribution
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Simulation Output
Mean cost
Min cost
Prob{failure}
Prob{low}
China
2.06
0.54
0.253
0.406
Taiwan
1.84
1.30
0.123
0.103
Vietnam
2.60
0.58
0.410
0.479
Germany
3.43
3.14
0.040
0.003
Alabama
2.05
2.05
0.254
0.009
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MCDM
j alternatives, I criteria
weights, scores
valuej   wi  u xij 
K
i 1
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MCDM Weights
Criteria
Base 100
Base 10
Best (100)
Worst (10)
Average
Quality
100
60
0.2299
0.2308
0.23
Experience
90
55
0.2069
0.2115
0.21
Cost
85
50
0.1954
0.1923
0.19
Flexibility
60
40
0.1379
0.1538
0.14
Technical
50
30
0.1149
0.1154
0.11
Exchange
30
15
0.0690
0.0577
0.06
Capital
20
10
0.0460
0.0385
0.06
435
260
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Scores
Quality
Experience
China
Problems
2 years
Taiwan
High
Vietnam
Cost
Flexibility
Technical
Exchange
Capital
0.82
High
Average
High
Weak
17 years
1.36
High
High
Moderate
High
Concerns
1 year
0.85
Low
Low
Moderate
Weak
Germany
High
5 years
3.20
Low
High
Moderate
High
Alabama
good
7 years
2.05
Low
High
None
Average
China
0.20
0.30
1.00
1.00
0.60
0.00
0.20
Taiwan
1.00
1.00
0.50
1.00
1.00
0.50
1.00
Vietnam
0.40
0.10
0.95
0.20
0.20
0.50
0.20
Germany
1.00
0.70
0.00
0.20
1.00
0.50
1.00
Alabama
0.70
0.90
0.30
0.20
1.00
1.00
0.50
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Values
Criteria
Weights
CHINA
TAIWAN
VIETNAM
GERMANY
ALABAMA
Quality
0.23
0.20
1.00
0.40
1.00
0.70
Experience
0.21
0.30
1.00
0.10
0.70
0.90
Cost
0.19
1.00
0.50
0.95
0.00
0.30
Flexibility
0.14
1.00
1.00
0.20
0.20
0.20
Technical
0.11
0.60
1.00
0.20
1.00
1.00
Exchange
0.06
0.00
0.50
0.50
0.50
1.00
Capital
0.06
0.20
1.00
0.20
1.00
0.50
Score
0.52
0.88
0.39
0.61
0.64
Rank
4
1
5
3
2
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Balanced Scorecard
Perspectives
Goals
Measures
Financial
Survive
Succeed
Prosper
Cash flow
Sales, growth, income
Increase in Market share, ROI
Customer
New products
Responsive supply
Preferred suppliers
Customer partnerships
% sales new products
On-time delivery
Share of key accounts’ purchases
# Cooperative engineering efforts
Internal
business
Technology capability
Manufacturing experience
Design productivity
New product innovation
Benchmark vs. competition
Cycle time, unit cost, yield
Engineering efficiency
Planned vs. actual schedule
Innovation &
learning
Technology leadership
Manufacturing learning
Product focus
Time to market
Time to develop next generation
Process time to maturity
% products yielding 80% sales
New product innovation vs. competition
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Conclusions
• Outsourcing provides competitive access
– Broader opportunities
• Demonstrate 3 tools
– Monte Carlo simulation
• Evaluate probabilistic elements
– MCDM
• Consider multiple criteria
• Select vendor by decision maker preference
– Balanced Scorecard
• Measure effectiveness of selected vendor
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ERM Research
•
•
Mostly descriptive, frameworks
SURVEY
– Lynch-Bell [2002] surveyed 52 companies
• Examined practices of governance, strategy, processes, technology, functions, culture
– Milladge [2005]; Gates [2006] surveyed 271 members of the Conference Board
•
Skelton & Thamhain [2003]; Thamhain [2004]
– 3 year field study R&D product development
– Suggest look-ahead simulation, rapid prototyping to anticipate problems
•
Beasley et al. [2005]
– Gathered data on 123 organizations, found ERM implementation positively
related to:
•
•
•
•
•
•
Chief risk officer presence
Board independence
Top management support
Big Four auditor presence
Entity size
Banking, Education, Insurance
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