Upload
duongkiet
View
220
Download
5
Embed Size (px)
Citation preview
Insurance and ActuarialAdvisory Services
Insurance and ActuarialAdvisory Services
Enterprise Risk Management and Stochastic Embedded Value ModelingEnterprise Risk Management and Stochastic Embedded Value Modeling
ALM Joint Regional Seminar, June 27, 2005 – July 4, 2005
Jonathan Zhao, FSA, FCIA, MAAA, MCA
ALM Joint Regional Seminar, June 27, 2005 – July 4, 2005
Jonathan Zhao, FSA, FCIA, MAAA, MCA
1
AgendaAgenda
• Need for Enterprise Risk Management and Modeling
• Embedded Value Concept
• European Embedded Value and CFO Forum Principles
• Stochastic EV Modeling Case Study
• Practitioners Guide & Closing Remarks
• Need for Enterprise Risk Management and Modeling
• Embedded Value Concept
• European Embedded Value and CFO Forum Principles
• Stochastic EV Modeling Case Study
• Practitioners Guide & Closing Remarks
2
Throughout history the industry has misjudged the behavior of its
environment, distribution channels and customers.
Throughout history the industry has misjudged the behavior of its
environment, distribution channels and customers.
• The unbundling of life insurance with the introduction of universal life policies
• “Vanishing premium” concept and no lapse guarantees• Single premium capital guaranteed products backed by inappropriate
assets• Embedded guarantees (e.g., GMDB, GMAB, GMWB) in variable and
unit-link products
• The unbundling of life insurance with the introduction of universal life policies
• “Vanishing premium” concept and no lapse guarantees• Single premium capital guaranteed products backed by inappropriate
assets• Embedded guarantees (e.g., GMDB, GMAB, GMWB) in variable and
unit-link products
“Adverse” customer behavior has cost the industry dearly in terms of lost profits.Enterprise risk management and modeling is now necessary in order to run the business.
“Adverse” customer behavior has cost the industry dearly in terms of lost profits.Enterprise risk management and modeling is now necessary in order to run the business.
3
Enterprise Risk Management Enterprise Risk Management
• Risk is an event or condition that impairs the organization’s ability to meet its goals
• Risk Management is a process of balancing the consequences of risk with the cost of avoiding it or hedging it
• Enterprise Risk Management is risk management that considers all the enterprise’s various business in aggregate and by considering the correlation of the risks
• Risk is an event or condition that impairs the organization’s ability to meet its goals
• Risk Management is a process of balancing the consequences of risk with the cost of avoiding it or hedging it
• Enterprise Risk Management is risk management that considers all the enterprise’s various business in aggregate and by considering the correlation of the risks
Enterprise Risk Modeling is the part of the enterprise risk management that you can model and quantify, and use as a benchmark to take appropriate actions
Enterprise Risk Modeling is the part of the enterprise risk management that you can model and quantify, and use as a benchmark to take appropriate actions
Insurance and ActuarialAdvisory Services
Insurance and ActuarialAdvisory Services
Enterprise Risk ModelingEnterprise Risk Modeling
5
Enterprise Risk Modeling Follows a Logical PathEnterprise Risk Modeling Follows a Logical Path
ResponseAvoidMinimizeMitigate risks
Qualify and PrioritizeUse actuarial tools Management judgment
ExploitOptimize RiskOther competitive advantages
Catalog & Identify– Enterprise concerns
& exposures– Universe of risks
• Catalog & Identify
• Qualify and Prioritize
• Response
• Exploit
• Catalog & Identify
• Qualify and Prioritize
• Response
• Exploit
6
Catalog the Universe of RisksCatalog the Universe of Risks
• Financial– Interest rates, equity market volatility, credit, liquidity
• Business– Mortality, morbidity, economic activity, management failure
• Operational– Process failure, fraud, litigation
• Event– Regulatory change, war, natural disaster
• Financial– Interest rates, equity market volatility, credit, liquidity
• Business– Mortality, morbidity, economic activity, management failure
• Operational– Process failure, fraud, litigation
• Event– Regulatory change, war, natural disaster
Assess the organization’s risk tolerance realistically. “What risk levels can we take on comfortably?”
Assess the organization’s risk tolerance realistically. “What risk levels can we take on comfortably?”
7
Quantify and prioritizeQuantify and prioritize
• Determine tolerance level
• Create accountability
– Who’s on first?
• Evaluate RBC, rating agency perspectives
– To see ourselves as others see us
• Use available resources first, such as cash flow testing models
– Do better, if possible
• Determine tolerance level
• Create accountability
– Who’s on first?
• Evaluate RBC, rating agency perspectives
– To see ourselves as others see us
• Use available resources first, such as cash flow testing models
– Do better, if possible
The internal and external audit functions may be valuable in this processThe internal and external audit functions may be valuable in this process
8
Respond - defensively firstRespond - defensively first
• Avoid• Avoid
• Diversify• Diversify
• Reinsure
• Offset
• Hedge
• Strengthen capital
• Reinsure
• Offset
• Hedge
• Strengthen capital
Monitor continuously …Monitor continuously …
9
Exploit!Exploit!
• Risk management is a tool for competitive advantage:
– PRICING PRECISION
– SELF HEDGING
– PRODUCT RATIONING
– NICHE EXPANSION
– PRICING ARBITRAGE
– OTHER– OTHER
• Risk management is a tool for competitive advantage:
– PRICING PRECISION
– SELF HEDGING
– PRODUCT RATIONING
– NICHE EXPANSION
– PRICING ARBITRAGE
10
Enterprise Risk Modeling needs a “Global” Financial Reporting
and Measurement Framework
Enterprise Risk Modeling needs a “Global” Financial Reporting
and Measurement Framework
• Actuarially robust
• Reflects local reserving, capital requirements and taxation
• Acts as an early warning system
• Reconciles with product pricing
• Allows for enterprise consolidation
• Actuarially robust
• Reflects local reserving, capital requirements and taxation
• Acts as an early warning system
• Reconciles with product pricing
• Allows for enterprise consolidation
Embedded Value reporting is that framework …Embedded Value reporting is that framework …
Insurance and ActuarialAdvisory Services
Insurance and ActuarialAdvisory Services
Embedded ValueEmbedded Value
12
Embedded Value … measures management’s added value.Embedded Value … measures management’s added value.
• Embedded Value
– Use of Embedded Value
– Basics, Deterministic vs. Stochastic EV
– European Embedded Value (EEV) Principles
• Embedded Value at Risk (EV@Risk)
– Financial statements that help optimize risk
• Embedded Value
– Use of Embedded Value
– Basics, Deterministic vs. Stochastic EV
– European Embedded Value (EEV) Principles
• Embedded Value at Risk (EV@Risk)
– Financial statements that help optimize risk
13
Uses of Embedded ValueUses of Embedded Value
• Published financials (China, UK, Europe)
• Incentive compensation (China, US)
• Information for rating agencies, analysts (Canada)
• Pricing and planning validation (Global)
• Enterprise risk modeling (New)
• Published financials (China, UK, Europe)
• Incentive compensation (China, US)
• Information for rating agencies, analysts (Canada)
• Pricing and planning validation (Global)
• Enterprise risk modeling (New)
14
Basic Components of Embedded ValueBasic Components of Embedded Value
Actuarial
Appraisal
Value
Actuarial
Appraisal
Value
Value ofFuture
Business
Value ofFuture
Business
AdjustedNet WorthAdjusted
Net Worth
Value ofInforce
Business
Value ofInforce
Business Embedded
Value
Embedded
Value
15
Value of InforceValue of Inforce
• Present Value (PV) of Distributable Earnings
= PV after-tax book profits
– PV Cost of Capital
+ Target Surplus (TS) at the valuation date
• Distributable Earnings (year t)
= After-tax Book Profit (year t)
+ TS Released (year t)
+ After-tax earnings on TS (year t)
• Present Value (PV) of Distributable Earnings
= PV after-tax book profits
– PV Cost of Capital
+ Target Surplus (TS) at the valuation date
• Distributable Earnings (year t)
= After-tax Book Profit (year t)
+ TS Released (year t)
+ After-tax earnings on TS (year t)
Allocate assets to business lines, project cash flows using best estimate assumptions, include statutory reserves, discount distributable earnings to
present value at a hurdle rate reflecting risk
Allocate assets to business lines, project cash flows using best estimate assumptions, include statutory reserves, discount distributable earnings to
present value at a hurdle rate reflecting risk
16
PV Cost of CapitalPV Cost of Capital
• Timing difference between TS now and future TS releases net of after-tax investment earnings on assets backing TS
= Target Surplus
– PV Future Target Surplus Released
– PV Future after-tax earnings on assets backing TS
• Timing difference between TS now and future TS releases net of after-tax investment earnings on assets backing TS
= Target Surplus
– PV Future Target Surplus Released
– PV Future after-tax earnings on assets backing TS
17
Adjusted Net WorthAdjusted Net Worth
= Statutory capital and surplus
+ Allocations of surplus, such as AVR
+ Non-admitted assets with realizable value
+ Reduced for the value of any obligations not considered in the value of inforce
+ Adjustment for difference between market value and book value of assets backing adjusted net worth
= Statutory capital and surplus
+ Allocations of surplus, such as AVR
+ Non-admitted assets with realizable value
+ Reduced for the value of any obligations not considered in the value of inforce
+ Adjustment for difference between market value and book value of assets backing adjusted net worth
Target surplus is usually included in adjusted net worth for presentation purposesTarget surplus is usually included in adjusted net worth for presentation purposes
18
Embedded Value Change = “Value Added” or “Achieved Profits”
Embedded Value Change = “Value Added” or “Achieved Profits”
AdjustedNet
Worth
AdjustedNet
Worth
Valueof Inforce
Valueof Inforce
S/H DividendS/H Dividend
AdjustedNet WorthAdjusted
Net Worth
Value fromNew SalesValue fromNew Sales
Value ofSurvivingInforce
Value ofSurvivingInforce
Achieved Profit Amount
Achieved Profit Amount
EndingEmbedded
Value
EndingEmbedded
ValueStarting
EmbeddedValue
StartingEmbedded
Value
19
Deterministic EVDeterministic EV
• Best estimate assumptions
• Reflects overall risks in the discount rate – higher discount rate for riskier products
• Easy to implement and understand
– Can often use the existing actuarial model
• However,
– Focused primarily on interest-rate risk
– Does not reflect tail exposure
– Unable to measure the interaction of risks
• Best estimate assumptions
• Reflects overall risks in the discount rate – higher discount rate for riskier products
• Easy to implement and understand
– Can often use the existing actuarial model
• However,
– Focused primarily on interest-rate risk
– Does not reflect tail exposure
– Unable to measure the interaction of risks
20
Stochastic EVStochastic EV
• Enables companies to capture the interaction of risks
• Quantifies risks (total enterprise basis & by line of business)
• Helps management to determine a comfortable level of risk and tooptimize the risk/reward relationships
• An approach that distinguishes a company from its competitors in terms of enterprise risk measurement and management
• Enables companies to capture the interaction of risks
• Quantifies risks (total enterprise basis & by line of business)
• Helps management to determine a comfortable level of risk and tooptimize the risk/reward relationships
• An approach that distinguishes a company from its competitors in terms of enterprise risk measurement and management
21
What is Stochastic EV?What is Stochastic EV?
• Examples of simple application– Various interest rate scenarios used in asset adequacy testing
– Sensitivity testing (e.g., increasing lapse rate, lower mortality rate)
• Formal stochastic approach– Identify risk elements (e.g., interest, mortality, and default)
– Use a stochastic process to define a range of selected risk elements (e.g., Monte Carlo, Economic Scenario Generator)
– Run EV model over a range for selected risk elements
• Start with a deterministic model
• Stochastic Assumption = deterministic assumption x stochastically generated factor
• Examples of simple application– Various interest rate scenarios used in asset adequacy testing
– Sensitivity testing (e.g., increasing lapse rate, lower mortality rate)
• Formal stochastic approach– Identify risk elements (e.g., interest, mortality, and default)
– Use a stochastic process to define a range of selected risk elements (e.g., Monte Carlo, Economic Scenario Generator)
– Run EV model over a range for selected risk elements
• Start with a deterministic model
• Stochastic Assumption = deterministic assumption x stochastically generated factor
Insurance and ActuarialAdvisory Services
Insurance and ActuarialAdvisory Services
European Embedded Value PrinciplesEuropean Embedded Value Principles
23
European Embedded Value PrinciplesEuropean Embedded Value Principles
• The CFO Forum launched European Embedded Value (EEV) Principles in 2003
• EEV principles are aimed to provide transparent and consistent financial information to the investors across major European insurance companies
• The CFO Forum launched European Embedded Value (EEV) Principles in 2003
• EEV principles are aimed to provide transparent and consistent financial information to the investors across major European insurance companies
24
European Embedded Value Principles - continuesEuropean Embedded Value Principles - continues
25
Embedded Value at Risk ConceptEmbedded Value at Risk Concept
• EV@Risk TM : Difference between the mean EV value and the fifth percentile EV for each risk element (other levels ofEV@Risk TM could also be used)
• Shows variance in EV over a range of economic and non-economic scenarios
– Quantifies impact to EV for each individual risk elements and in aggregate
– Demonstrates the correlation effect between different risk elements (i.e., sum of individual risk components is greater than when all the risks are run together)
• Allows management to determine a comfortable level of risk
• Requires stochastic EV modeling in order to determine different levels of EV@Risk TM
• EV@Risk TM : Difference between the mean EV value and the fifth percentile EV for each risk element (other levels ofEV@Risk TM could also be used)
• Shows variance in EV over a range of economic and non-economic scenarios
– Quantifies impact to EV for each individual risk elements and in aggregate
– Demonstrates the correlation effect between different risk elements (i.e., sum of individual risk components is greater than when all the risks are run together)
• Allows management to determine a comfortable level of risk
• Requires stochastic EV modeling in order to determine different levels of EV@Risk TM
26
EV@Risk™EV@Risk™• Embedded Value at Risk is similar to “VAR”
• Difference between mean and 5th percentile
• Embedded Value at Risk is similar to “VAR”
• Difference between mean and 5th percentile
Universal Life - Comparison of Stochastic Runs
$796
$798
$800
$802
$804
$806
$808
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90
Embe
dded
Val
ue a
t 12/
31/0
1 (m
)
Stoch Lapse Only
Stoch Mortality Only
Stoch Lapse &MortalityBase Run
5th Percentile5th Percentile
MeanMean
Insurance and ActuarialAdvisory Services
Insurance and ActuarialAdvisory Services
Stochastic EV Case StudyStochastic EV Case StudyFor a Sample Universal Life ProductFor a Sample Universal Life Product
28
Ernst & Young Stochastic EV Case Study for ACLIErnst & Young Stochastic EV Case Study for ACLI
• Universal Life (UL) product from the Ernst & Young deterministic EV case study developed for the American Council of Life Insurer (ACLI) CFO Roundtable
• UL risk elements selected– Interest
– Mortality
– Asset Default
• Used 100 iterations for each of the risk elements– Too many = long run time
– Too few = reduce accuracy
– Experience and testing will help identify optimal number of runs
• Universal Life (UL) product from the Ernst & Young deterministic EV case study developed for the American Council of Life Insurer (ACLI) CFO Roundtable
• UL risk elements selected– Interest
– Mortality
– Asset Default
• Used 100 iterations for each of the risk elements– Too many = long run time
– Too few = reduce accuracy
– Experience and testing will help identify optimal number of runs
29
Generating UL Stochastic Risk AssumptionsGenerating UL Stochastic Risk Assumptions
• The stochastic assumptions are generated for each risk element as follows:
– Stochastic Assumption (other than stochastic interest) = Deterministic Assumption x Stochastic Generated Factor
– Distribution Assumption = Normal Distribution
• Stochastic Interest– Economic Interest Rate scenarios were generated using Ernst & Young Economic
Scenario Engine (ESE)
• Stochastic Mortality– Mean =1, Standard Deviation = 5%, – Max = 2, Min = 0.5
• Stochastic Asset Default– Mean =1, Standard Deviation = 100%, – Max = 4, Min = 0
• The stochastic assumptions are generated for each risk element as follows:
– Stochastic Assumption (other than stochastic interest) = Deterministic Assumption x Stochastic Generated Factor
– Distribution Assumption = Normal Distribution
• Stochastic Interest– Economic Interest Rate scenarios were generated using Ernst & Young Economic
Scenario Engine (ESE)
• Stochastic Mortality– Mean =1, Standard Deviation = 5%, – Max = 2, Min = 0.5
• Stochastic Asset Default– Mean =1, Standard Deviation = 100%, – Max = 4, Min = 0
30
Results of Ernst & Young Universal Life ModelResults of Ernst & Young Universal Life Model
Deterministic Financial Results
• GAAP Earnings for 2004 $ 179,232• GAAP Earnings 2004-2007 $ 778,388*• Embedded Value 12/31/2004 $ 1,324,758*• Embedded Value without Target Surplus $ 561,872*
* Discounted @ 9.00%
Deterministic Financial Results
• GAAP Earnings for 2004 $ 179,232• GAAP Earnings 2004-2007 $ 778,388*• Embedded Value 12/31/2004 $ 1,324,758*• Embedded Value without Target Surplus $ 561,872*
* Discounted @ 9.00%
31
Results of Ernst & Young Universal Life ModelStochastic Mortality Results
Results of Ernst & Young Universal Life ModelStochastic Mortality Results
Deterministic Mean EaRisk 5th 25th 50th 75th 95thGAAP Eanings 2004 179,232$ 178,764$ 4,112$ 174,652$ 177,198$ 178,679$ 180,672$ 182,711$ GAAP Earnings 2004-2007 778,388$ 777,142$ 8,756$ 768,387$ 773,511$ 777,888$ 780,208$ 785,330$ Embedded Value 1,324,758$ 1,321,195$ 56,637$ 1,264,557$ 1,298,890$ 1,319,582$ 1,344,527$ 1,377,207$ Embedded Value w/o TS 561,872$ 558,309$ 56,637$ 501,672$ 536,004$ 556,696$ 581,641$ 614,321$
PercentileUniversal Life Results - Stochastic Mortality
Observations:2004 GAAP earnings at risk is about 2.3% versus the deterministic results while EV@Risk TM is about 4.3%
EV@Risk TM is 10.1% when calculated without target surplus
Observations:2004 GAAP earnings at risk is about 2.3% versus the deterministic results while EV@Risk TM is about 4.3%
EV@Risk TM is 10.1% when calculated without target surplus
32
Results of Ernst & Young Universal Life ModelStochastic Mortality Results
Results of Ernst & Young Universal Life ModelStochastic Mortality Results
Universal Life - Stochastic Mortality
(200,000)
-
200,000
400,000
600,000
800,000
1,000,000
1,200,000
1,400,000
1,600,000
0.01 0.11 0.21 0.31 0.41 0.51 0.61 0.71 0.81 0.91
Valu
e of
Exi
stin
g Bu
sine
ss
Stochastic Mortality Deterministic
Result % of MeanMean 558,309 100.00%Median 556,696 99.71%Minimum 484,634 86.80%Maximum 653,832 117.11%Deterministic 561,872 100.64%
Statistics
Result % of Mean5th 501,672 89.86%25th 536,004 96.00%50th 556,696 99.71%75th 581,641 104.18%95th 614,321 110.03%
Percentile
33
Results of Ernst & Young Universal Life ModelStochastic Interest Results
Results of Ernst & Young Universal Life ModelStochastic Interest Results
Deterministic Mean EaRisk 5th 25th 50th 75th 95thGAAP Eanings 2004 179,232$ 179,014$ 56$ 178,958$ 178,996$ 179,018$ 179,034$ 179,077$ GAAP Earnings 2004-2007 778,388$ 777,616$ 1,362$ 776,254$ 777,206$ 777,605$ 778,256$ 779,014$ Embedded Value 1,324,758$ 1,324,485$ 398,295$ 926,190$ 1,226,844$ 1,384,108$ 1,463,875$ 1,538,830$ Embedded Value w/o TS 561,872$ 561,599$ 398,295$ 163,304$ 463,958$ 621,222$ 700,990$ 775,944$
Universal Life Results - Stochastic InterestPercentile
Observations:2004 GAAP earnings at risk is about 0.0% versus the mean results while EV@Risk TM is about 30.1%.
EV@Risk TM is 70.9% when calculated without target surplus
Observations:2004 GAAP earnings at risk is about 0.0% versus the mean results while EV@Risk TM is about 30.1%.
EV@Risk TM is 70.9% when calculated without target surplus
34
Results of Ernst & Young Universal Life ModelStochastic Interest Results
Results of Ernst & Young Universal Life ModelStochastic Interest Results
Universal Life - Stochastic Interest
(200,000)
-
200,000
400,000
600,000
800,000
1,000,000
1,200,000
1,400,000
1,600,000
0.01 0.12 0.22 0.33 0.43 0.54 0.64 0.75 0.85 0.96
Valu
e of
Exi
stin
g Bu
sine
ss
Stochastic Interest Deterministic
Result % of MeanMean 561,599 100.00%Median 621,222 110.62%Minimum (142,731) -25.42%Maximum 879,532 156.61%Deterministic 561,872 100.05%
Statistics
Result % of Mean5th 163,304 29.08%25th 463,958 82.61%50th 621,222 110.62%75th 700,990 124.82%95th 775,944 138.17%
Percentile
35
Results of Ernst & Young Universal Life ModelStochastic Default Results
Results of Ernst & Young Universal Life ModelStochastic Default Results
Deterministic Mean EaRisk 5th 25th 50th 75th 95thGAAP Eanings 2004 179,232$ 178,931$ 2,349$ 176,582$ 177,572$ 178,645$ 180,024$ 182,448$ GAAP Earnings 2004-2007 778,388$ 777,722$ 9,758$ 767,964$ 774,227$ 777,732$ 782,375$ 786,094$ Embedded Value 1,324,758$ 1,320,977$ 213,532$ 1,107,445$ 1,234,911$ 1,314,282$ 1,443,076$ 1,554,656$ Embedded Value w/o TS 561,872$ 558,091$ 213,532$ 344,560$ 472,025$ 551,397$ 680,191$ 791,770$
Universal Life Results - Stochastic DefaultPercentile
Observations:2004 GAAP earnings at risk is about 1.3% versus the mean results while EV@Risk TM is about 16.2%.
EV@Risk TM is 38.3% when calculated without target surplus
Observations:2004 GAAP earnings at risk is about 1.3% versus the mean results while EV@Risk TM is about 16.2%.
EV@Risk TM is 38.3% when calculated without target surplus
36
Results of Ernst & Young Universal Life ModelStochastic Default Results
Results of Ernst & Young Universal Life ModelStochastic Default Results
Universal Life - Stochastic Default
(200,000)
-
200,000
400,000
600,000
800,000
1,000,000
1,200,000
1,400,000
1,600,000
0.01 0.11 0.21 0.31 0.41 0.51 0.61 0.71 0.81 0.91
Valu
e of
Exi
stin
g Bu
sine
ss
Stochastic Default Deterministic
Result % of MeanMean 558,091 100.00%Median 551,397 98.80%Minimum 244,159 43.75%Maximum 828,807 148.51%Deterministic 561,872 100.68%
Statistics
Result % of Mean5th 344,560 61.74%25th 472,025 84.58%50th 551,397 98.80%75th 680,191 121.88%95th 791,770 141.87%
Percentile
37
Results of Ernst & Young Universal Life ModelStochastic All Results
Results of Ernst & Young Universal Life ModelStochastic All Results
Deterministic Mean EaRisk 5th 25th 50th 75th 95thGAAP Eanings 2004 179,232$ 179,116$ 5,254$ 173,862$ 177,220$ 179,126$ 180,976$ 184,316$ GAAP Earnings 2004-2007 778,388$ 778,128$ 14,723$ 763,404$ 772,176$ 778,936$ 785,524$ 788,847$ Embedded Value 1,324,758$ 1,323,899$ 369,460$ 954,438$ 1,137,281$ 1,337,677$ 1,514,501$ 1,692,022$ Embedded Value w/o TS 561,872$ 561,013$ 369,460$ 191,553$ 374,396$ 574,791$ 751,616$ 929,136$
Universal Life Results - Stochastic AllPercentile
Observations:2004 GAAP earnings at risk is about 2.9% versus the mean results while EV@Risk TM is about 27.9%.
EV@Risk TM is 65.9% when calculated without target surplus
Observations:2004 GAAP earnings at risk is about 2.9% versus the mean results while EV@Risk TM is about 27.9%.
EV@Risk TM is 65.9% when calculated without target surplus
38
Results of Ernst & Young Universal Life ModelStochastic All Results
Results of Ernst & Young Universal Life ModelStochastic All Results
Universal Life - Stochastic All
(200,000)
-
200,000
400,000
600,000
800,000
1,000,000
1,200,000
1,400,000
1,600,000
0.01 0.11 0.22 0.32 0.43 0.53 0.64 0.74 0.84 0.95
Val
ue o
f Exi
stin
g B
usin
ess
Stochastic Interest/Mortality/Defaults Deterministic
Result % of MeanMean 561,013 100.00%Median 574,791 102.46%Minimum (115,201) -20.53%Maximum 1,070,196 190.76%Deterministic 561,872 100.15%
Statistics
Result % of Mean5th 191,553 34.14%25th 374,396 66.74%50th 574,791 102.46%75th 751,616 133.97%95th 929,136 165.62%
Percentile
39
Results of Ernst & Young Universal Life ModelSummary of Stochastic Results
Results of Ernst & Young Universal Life ModelSummary of Stochastic Results
Percentile All Interest Mortality Defaults5th 173,862 178,958 174,652 176,58225th 177,220 178,996 177,198 177,57250th 179,126 179,018 178,679 178,64575th 180,976 179,034 180,672 180,02495th 184,316 179,077 182,711 182,448
Mean 179,116 179,014 178,764 178,931EaRisk 5,254 56 4,112 2,349
Correlation (1,264) Deterministic 179,232
2004 Operating Earnings - Universal Life
Percentile All Interest Mortality Defaults5th 954,438 926,190 1,264,557 1,107,44525th 1,137,281 1,226,844 1,298,890 1,234,91150th 1,337,677 1,384,108 1,319,582 1,314,28275th 1,514,501 1,463,875 1,344,527 1,443,07695th 1,692,022 1,538,830 1,377,207 1,554,656
Mean 1,323,899 1,324,485 1,321,195 1,320,977EVaRisk 369,460 398,295 56,637 213,532
Correlation (299,004) Deterministic 1,324,758
2004 Embedded Value - Universal Life
40
Recap of Key LearningRecap of Key Learning
• Can be used to create an effective decision support framework
– Competitive advantage
– Risk optimization
• Risk measurement and management across risks & product lines is doable
• Profitability and productivity metrics for distribution system
• Facilitate capital allocation
• Segue to IAS accounting and Risk Adjusted Return on Capital (RAROC)
• Can be used to create an effective decision support framework
– Competitive advantage
– Risk optimization
• Risk measurement and management across risks & product lines is doable
• Profitability and productivity metrics for distribution system
• Facilitate capital allocation
• Segue to IAS accounting and Risk Adjusted Return on Capital (RAROC)
41
Closing RemarksClosing Remarks
• Stochastic valuation has arrived– Stochastic EV (EEV), ERM (EV @ Risk)
– RBC C3 Phase II, Economic Capital, Statutory Reserves, SOP Reserves
• Actuaries need to elevate their game to be ready– Finance theory and conceptual frameworks
– Modeling & valuation tools
– Technology
– Quality assurance
– Analysis and presentation
– Communications
• Stochastic valuation has arrived– Stochastic EV (EEV), ERM (EV @ Risk)
– RBC C3 Phase II, Economic Capital, Statutory Reserves, SOP Reserves
• Actuaries need to elevate their game to be ready– Finance theory and conceptual frameworks
– Modeling & valuation tools
– Technology
– Quality assurance
– Analysis and presentation
– Communications
42
Questions !Questions !