ERMDavid L. Olson, University of Nebraska-LincolnDesheng Wu, University of Reykjavik, University of Toronto
Enterprise Risk ManagementNot 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
2Finland May 2010
3Finland May 2010
4Finland May 2010
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”
5Finland May 2010
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 Shaper6Finland May 2010
Types of RiskStroh [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
7Finland May 2010
Another viewSlywotzky & 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
8Finland May 2010
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• Outdated manufacturing process– DEFENSE: Double bet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
9Finland May 2010
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
10Finland May 2010
One-of-a-kind Competitor
• Competitor redefines market• Wal-Mart
– DEFENSE: Create new, non-overlapping business design
• Target – unique product selection
11Finland May 2010
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
12Finland May 2010
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
13Finland May 2010
COSOCommittee of Sponsoring Organizations
Treadway Committee – 1990sSmiechewicz [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
14Finland May 2010
COSO Integrated Framework 2004Levinsohn [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
15Finland May 2010
Risk Management Tools
• Simulation (Beneda [2005])– Monte Carlo – Crystal Ball
• Multiple criteria analysis– Tradeoffs between risk & return
• Balanced Scorecard– Organizational performance measurement
16Finland May 2010
ERM SoftwareRhoden [2006]
Penny [2002]• Algorithmics Incorporated – ERM software, global financial institutionsJane’s Defence Industry [2005]• Strategic Thought – Active Risk Manager – defence industryRhoden [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
17Finland May 2010
SIMULATION
• Crystal Ball– Spreadsheet add-in– Value at Risk (VaR)
• Distribution of expected value at specified probability level
• >3.42 @ 0.95
18Finland May 2010
Spreadsheet
Year 1 2 3 4 5Sales 10000 11000 12100 13310 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
19Finland May 2010
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
20Finland May 2010
Net Present Value
where n = number of time periods in analysisini = revenues in period i
outi = cash outflow in period i
r = discount rate
i = END of time period
NPV =in out
(1+ r)i i
ii=0
n
21Finland May 2010
EXCEL RN generation
• Options– Analysis Tools
– Random Number Generation» Output Range» Number of Variables» Number of Random Numbers» Distribution» Parameters» Random Seed
22Finland May 2010
Sharpe Ratio
• Consider variance of stock as measure of risk– Tradeoff between mean and variance– Efficient investment opportunities
23Finland May 2010
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
24Finland May 2010
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
25Finland May 2010
Black-Scholes Option Pricing
• Model to value optionsPrice 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)
T
TrESd
)2/()/ln( 2
1
Tdd 12
26Finland May 2010
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
27Finland May 2010
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
28Finland May 2010
Numerical ValuationBarraquand & 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
29Finland May 2010
Advanced Option Pricing
Fink & Fink, Journal of Applied Finance, Fall/Winter 2006, 16:2, 92-105
• Foreign currency options have volatility smiles (“u-shaped”)
• 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)
30Finland May 2010
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
31Finland May 2010
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
32Finland May 2010
Financial Simulations
• a very rich field for simulation– high degrees of uncertainty in cash flows
• SPREADSHEETS for the most-part
33Finland May 2010
Iceland heating pipesMean Lognormal (30.76,38.61) – offset 30
MONTH Seasonal Differential from MeanApr 3.604167May 10.45833Jun 72.3125Jul 46.5Aug -24.6458Sep 1.875Oct 29.0625Nov 22.0833Dec -27.8958Jan -15.375Feb -26.5208
34Finland May 2010
Supply Chain SimulationProduce to Forecast
35Finland May 2010
Supply Chain SimulationProduce to ROP/Q
Q30 Q40 Q50 Q60
AVG STOCKOUTS To forecast – 0 to 643, mean 50
ROP 30 468 495 440 393
ROP 40 421 366 398 352
ROP 50 377 324 287 313
ROP 60 334 283 249 223
AVG HOLD To forecast – 81 to 559, mean 253
ROP 30 39 38 45 51
ROP 40 43 51 49 56
ROP 50 47 55 63 61
ROP 60 52 61 68 76
AVG SALES To forecast – 452 to 1281, mean 1032
ROP 30 612 585 640 687
ROP 40 658 714 682 728
ROP 50 703 756 793 767
ROP 60 746 797 831 85736Finland May 2010
Monte Carlo Simulation
Quoted price
Exchange distribution
Product failure
Organizational failure
Political failure
Expected price
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
Finland May 201037
China vendor price distribution
Finland May 201038
Taiwan vendor price distribution
Finland May 201039
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
Finland May 201040
MCDM j alternatives, I criteria
weights, scores
Finland May 2010
K
iijij xuwvalue
1
41
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
Finland May 201042
ScoresQuality Experience Cost Flexibility Technical Exchange Capital
China Problems 2 years 0.82 High Average High Weak
Taiwan High 17 years 1.36 High High Moderate High
Vietnam 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
Finland May 201043
ValuesCriteria 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
Finland May 201044
Balanced Scorecard
Perspectives Goals Measures
Financial SurviveSucceedProsper
Cash flowSales, growth, incomeIncrease in Market share, ROI
Customer New productsResponsive supplyPreferred suppliersCustomer partnerships
% sales new productsOn-time deliveryShare of key accounts’ purchases# Cooperative engineering efforts
Internal business
Technology capabilityManufacturing experienceDesign productivityNew product innovation
Benchmark vs. competitionCycle time, unit cost, yieldEngineering efficiencyPlanned vs. actual schedule
Innovation & learning
Technology leadershipManufacturing learningProduct focusTime to market
Time to develop next generationProcess time to maturity% products yielding 80% salesNew product innovation vs. competition
Finland May 201045
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
Finland May 2010 46
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
47Finland May 2010