2006 Seminar for the Appointed Actuary Colloque pour l’actuaire désigné 2006
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Transcript of 2006 Seminar for the Appointed Actuary Colloque pour l’actuaire désigné 2006
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September 21, 2006
2006 Seminar for the Appointed ActuaryColloque pour l’actuaire désigné 2006
Canadian Institute
of Actuaries
L’Institut canadien desactuaires
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September 21, 2006
Stochastic Equity Modeling
Dr. Julia Lynn Wirch-ViinikkaAVP Investment Products Pricing
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AEGON Canada 3
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September 21, 2006
Agenda
1) Equity Risk – where is it?2) Stochastic Modeling – what is it?3) What options do we have for
modeling equity risk?4) How do we start?5) How do we improve our model?6) How do we illustrate our results?7) How complicated can it get?
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AEGON Canada 4
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September 21, 2006
Where is Your Equity Risk?
• Assets backing Liabilities (LTC)• Surplus• Liabilities
– Seg Fund, VA and UL Guarantees– Equity Linked Products
• Fee Income based on Fund Value• Hedging Mismatch
– Tracking error, basis risk
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AEGON Canada 5
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September 21, 2006
Why Manage Equity Risk?
• Regulatory requirement– Equity Limits, MCCSR requirements– DCAT testing
• Valuation– Reserves and Capital Requirements
• CGAAP, IFRS, US GAAP results– Income and Surplus volatility
• Risk Management objectives
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AEGON Canada 6
2006
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September 21, 2006
Market risk vs. Insurance risk
• Traditional insurance risks, such as mortality and longevity are less risky when pooled together: each individual follows their own “scenario” and the insurance company pays off on the average
• Capital market risks don’t diversify: every policyholder follows the same market scenario at the same time
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AEGON Canada 7
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September 21, 2006
Which Risks can be Managed?
• Risks that are identifiable and well understood
• Risks that are monitored and controlled• Risks where there is the knowledge and
expertise to effectively manage them.• Where the reward is sufficient for the
remaining risk• Where financial instruments and
methods are available to hedge or control risk
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September 21, 2006
What is a Model
• Imitation/simplification of a real world system
• Tool that provides statistical estimates and not exact results
• Computational, statistical or judgment-based
• Helpful for product design and pricing, valuation, forecasting, risk management, financial reporting, and performance management.
• Understand how your liability value changes over time, when your liability value needs to be calculated stochastically
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AEGON Canada 9
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September 21, 2006
What is a Stochastic Model?
A model that involves probability or randomness
• Random inputs (Normal, Lognormal, Uniform)
• Generally run many times (1000, 10000+)• Representative sampling (Yvonne Chueh)
• Distribution of outputs• Estimates of statistics (mean, %ile, std.dev)• Error estimates (direct or bootstrapping)
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AEGON Canada 10
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September 21, 2006
What is Model Risk?Model risk: the possibility of loss or error resulting from the use of models.
• Model misspecification• Assumption misspecification• Inappropriate use or application• Inadequate testing, validation, and
documentation• Lack of knowledge or understanding, user
and/or management• Error and negligence
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AEGON Canada 11
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September 21, 2006
How do you Model Equity Risk?
• Flat Return(8%) with an Extreme MfAD(-30%)• Set of deterministic scenarios (stress tests)• Purchase sets of stochastic scenarios• Stochastic Scenarios:
– Normal/Lognormal Returns – Autocorrelated Returns (time series)– Regime Switching LogNormal (RSLN)
• One correlation matrix• Different correlation matrices for each regime
– Other stochastic model (Wilkie, Smith, Lognormal, Stoch Volatility, empirical)
– Risk Neutral or Real World
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AEGON Canada 12
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September 21, 2006
Yield Curve vs. Equity
• Are they related?– Direct relation shows zero correlation
• However…– Bond Funds and Equity Indices show 30%-
60% correlation– Duration analysis can explain 90%+ of bond
fund returns: an( in
t – int-1) = Bond Fund Return (t-1,t)
– One way to connect Yield Curves with Equity Returns
– Leads to interest rates driving equity returns
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AEGON Canada 13
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September 21, 2006
What Equity Risk do you model?
• Indices:– Stock Market Indices:
• North America: S&P500, TSX, NASDAQ• Europe: FTSE, DAX
• Industry specific? Company specific?• Public Equity / Private EquityDo you model:• Hedge Funds? Pass-through products?• Real Estate? REITs?• Credit Spreads/Counterparty Risk?• Currency Risk?
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AEGON Canada 14
2006
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September 21, 2006
Is Equity Related to other Returns?• NO Independent• Correlation Matrix (Normal/Lognormal)• Regime Switching Assumptions• Time Series, Volatility Jumps • Macro-Economic Drivers (Wilkie
Model)• Does it matter?
– It depends on what you are trying to do
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AEGON Canada 15
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September 21, 2006
Scenario GeneratorsIssues:
• Is the focus on the mean, median, or tail events?• Economic vs. Risk Neutral model• Calibration (current/historical data)• Numerous Scenario Generators to choose from
Desirable Characteristics to check for:• Integrated model • Incorporates the principle of parsimony• Flexible. A component approach.
Beware: Often there is a false sense of precision
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AEGON Canada 16
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September 21, 2006
Why “risk-neutral”?
• Financial derivatives: value depends on the value of another financial instrument– Their prices do not depend on the particular
risk-preferences of the purchaser…… so we can assume any risk-preferences
• Mathematically convenient to assume purchaser is risk-neutral
• If you project market movements along a risk-neutral random walk and discount asset payoffs at the risk-free rate, you will obtain the “fair value” of that asset
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AEGON Canada 17
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September 21, 2006
“Fair Value”• Two portfolios with identical payoffs must
have the same price
”Arbitrage” - opportunity for profit: buy the less expensive portfolio and sell the more expensive portfolio
FOR INSURANCE Liabilities:• “No Arbitrage” doesn’t work perfectly: the market
cannot freely buy and sell the insurance liability
• Risk-neutral pricing tells you what it would cost to buy the same payoffs in the market. (not necessarily a good estimate of the expected cost of the guarantee if left unhedged)
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AEGON Canada 18
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September 21, 2006
Risk-Neutral Valuation• Brownian Motion:
• μ = expected risk free forward rate• σ = implied volatility, ε = random error
Does “Risk-Neutral” = “Market-Consistent”?• If μ and σ are market-consistent, the prices that the
model produces are market consistent• Both μ and σ can be functions of time• σ is often considered to be a function of market levels
(market volatility increases when market levels fall)
ttSttSSS
),(),(
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AEGON Canada 19
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September 21, 2006
Real World Model• Brownian Motion for the stochastic model:
– Drift rate: historical long-term avg returns (not the risk-free forward curve)
– Volatility: long term average or stochastic (GARCH, jump diffusion, RSLN)
– Goal: to reflect a reasonable distribution of potential future returns
• Fewer expected payoffs of the embedded option than under risk-neutral valuation (on average, the stock market has a better return than risk-free investments)
• Higher variability of profit by scenario• The “bad tail” can be very bad
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AEGON Canada 20
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September 21, 2006
Rule of Thumb• Tail risk:
– Use real-world valuation to measure tail risk
• Average cost:
– Use “real world” inputs when you are willing to accept the “average” result with a high amount of variability
– Use risk neutral when you want results (e.g. a price or a profit measure) which you can be very confident can be realized (through hedging)
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AEGON Canada 21
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September 21, 2006
Who uses your Equity Models?
• Hedging (Financial Engineering)– Market-consistent pricing - RN
• Risk Management, Valuation and Pricing (Actuarial Modeling)– Tail exposures – RW– Volatility - RW– Averages – RW/RN– Static Hedging - RN– Dynamic Hedging – RW/RN
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AEGON Canada 22
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September 21, 2006
Regime Switching Models• Discrete time (e.g. daily, monthly) • Any model with different parameters in each regime
(Normal, AR(1), ARCH….)• 2-Regime Lognormal Monthly – estimation software
free from SOA website• Very simple stoch vol model• Tractable, intuitive, 2 Regimes are usually enough for
monthly data - 6 parameters: {1, 2, 1, 2, p12, p21}• Regime 1: Low Vol, High Mean, High Persistence
(small p12)• Regime 2: High Vol, Low Mean, Low Persistence
(large p21)
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September 21, 2006
REGIME 1 1
Low Volatility 1
High Mean 1
2-Regime LogNormal
12p 21p
ttY 11
ttY 22 REGIME 2 2
High Volatility 2
Low Mean 2
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AEGON Canada 24
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September 21, 2006
Simple Stochastic Model• 3-year 100% Seg Fund Maturity Guarantee• MER = 3%
Regime 1 Regime 2Fund LN1(11%,16%) LN2(-8%, 20%)
P(Switch) p12=4% p21=22%
% Time in RegimeRegime 1 84.6%Regime 2 15.4%
Mean Std.Dev
8.4% 18.1%
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AEGON Canada 25
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September 21, 2006
Simple Stochastic Model: Scen 13-yr Maturity Guarantee: No death / lapseInitial Deposit = $1; Top-up = $0
Time: 0 1 2 3
Uniform =RAND()=0.934 0.641 0.135 0.053
Regime 2 2 1 1
Normal =NORMSINV(RAND()) -0.1635 0.7642 0.9195
Return = (1+it)
exp[mu*t +sigma*sqrt(t)*RN]
0.89 1.26 1.29
Fund Fund(t-1)*Return 0.89 1.13 1.46
Fund Less MER
FundLessMER(t-1)*Return
*(1-MER)0.87 1.06 1.33
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September 21, 2006
Simple Stochastic Model: Scen 23-yr Maturity Guarantee: No death / lapseInitial Deposit= $1; Top-up = $0.19
Time: 0 1 2 3
Uniform =RAND()=0.649 0.039 0.827 0.154
Regime 1 2 2 1
Normal =NORMSINV(RAND()) 0.1635 -0.7642 0.3195
Return = (1+it)
exp[mu*t +sigma*sqrt(t)*RN]
0.95 0.79 1.17
Fund Fund(t-1)*Return 0.95 0.76 0.88
Fund Less MER
FundLessMER(t-1)*Return
*(1-MER)0.93 0.71 0.81
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AEGON Canada 27
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September 21, 2006
More Advanced Stochastic Models
Other Modeling Considerations:– Death and Lapse (dynamic lapse?)– Death Benefits and Living Benefits– Ratchets and Resets– Policyholder Behaviour– Commissions / Surrender Charges / DAC– Reserves / Capital– Net Income / Tax / Distributable Earnings– Discount Rates for Present Values– Illustrating Results– Hedging Strategies
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AEGON Canada 28
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September 21, 2006
Summary Statistics
• Mean, Standard Deviation, Skewness, Kurtosis,…
• Percentiles (Quantiles)– Confidence intervals:
http://www.fenews.com/fen47/topics_act_analysis/topics-act-analysis.htm
• CTE 95%: Mean of worst 5% of results– Variance Estimate: Hancock and Manistre
NAAJ 9(2): 129-156
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AEGON Canada 29
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September 21, 2006
Box Plots
1 2 3 4 5 6 7 8 9 10-3000
-2000
-1000
0
1000
2000
3000
Net
Inco
me
Year
75/100 Guarantee - Income Projections
+
+ +
+
Maximum
75th Percentile
Median
25th Percentile
Minimum
Outliers
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AEGON Canada 30
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September 21, 2006
Histograms and CTE’s
0 50 100 150 200 250 300 350 400 450 500-7
-6
-5
-4
-3
-2
-1
0
1
2
3x 10
4
CTE 95% = -2.01
• Histogram of scenario outcomes
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AEGON Canada 31
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September 21, 2006
How Many Scenarios are Enough?
Present Value of Cash Flows ($M) under Various Scenario Sets
-$300
-$200
-$100
$0
$100
$200
$300
$400
$500
$600
1 - 1
0000
1 - 1
000
1001
- 20
00
2001
- 30
00
3001
- 40
00
4001
- 50
00
5001
- 60
00
6001
- 70
00
7001
- 80
00
8001
- 90
00
9001
- 10
000
Scenario Set
CTE(95) CTE(80) CTE(60) CTE(0)
• Convergence / Sampling error• Variance Reduction Techniques may help
• Many techniques work for averages not tails
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AEGON Canada 32
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September 21, 2006
Are you taking a Holistic Approach?
• ERM Approach: takes advantage of synergies across products– Consistent set of RW and/or RN scenarios
used for all lines of business– Projections aggregated by scenario across
lines of business– Yield curve and equity return assumptions
must be consistent– More difficult if two Tier Stochastic
simulation is required
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AEGON Canada 33
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September 21, 2006
1-Tier Stochastic Simulation• Projected Liability Payouts
Time
V0
0 T
•Can determine t=0 reserve(CTE70-80% and TBSR (CTE95%)
•Can determine liability payout projections
•Can not accurately determine future reserve and capital projections (approximations: NPATH, Black-Scholes)
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AEGON Canada 34
2006
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September 21, 2006
2-Tier Stochastic Simulation• Projected Liability Payouts, Reserves,
Capital, Net Income ….
Time
V0
0 T
•Can determine t=3 reserve for each stochastic scenario (CTE70-80%)
•Can determine future capital needs and net income projections
•Much more time consuming
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AEGON Canada 35
2006
Sem
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September 21, 2006
2-Tier Stochastic Simulation• Projected Liability Payouts, Reserves,
Capital, Net Income ….
Time
V0
0 T
Much more time consuming:
•1000 Tier 1 Scenarios
•10 time steps each
•1000*10 points to perform a second tier simulation
•500 scenarios at each point = 5,000,000 Tier 2 scenarios
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AEGON Canada 36
2006
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September 21, 2006
Insurance Options• Embedded options in insurance liabilities are
different from financial options– Sub-optimal exercise behavior
• FPDA: can pay surrender charges and get a new contract if new money rates rise– Evidence: PHs are inefficient in using this option– Some PH will not surrender their contracts no matter how
uncompetitive their renewal rate
• Segregated Funds (VA/VL) GMAB: should invest in the most aggressive funds available– CAPM: more risk implies more return– Evidence: PHs invest in conservative and balanced funds
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AEGON Canada 37
2006
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September 21, 2006
Stochastic Modeling Challenges• Option payoffs that depend on policyholder
behavior will reflect:– Historical behavior patterns– Actuarial judgment
• Path-dependent behavior (ie. lower lapses for in the money guarantees) can be modeled– Introduces uncertainty to valuation results– Practitioners have argued about the “proper” way to
model behavior in a risk-neutral framework • (library.soa.org/library-pdf/RRN0608.pdf by M. Evans)
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AEGON Canada 38
2006
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September 21, 2006
Stochastic Modeling Challenges (Continued)
• Long-term nature of liabilities:– Expected market forward rates past 30 years
is needed for valuation– Instruments that will hedge the yield curve
past 30 years or equity risks past 10 years are illiquid or unavailable
• Computational Requirements– Distributed processing (AXIS, MatLab, ….)– 2-Tier Stochastic Analysis (Stochastic-in-
Stochastic)
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AEGON Canada 39
2006
Sem
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September 21, 2006
Conclusions• Equity risk is not like traditional
insurance risk.• Stochastic Modeling is a tool that can
help us understand complex dynamic processes.
• Start simple and build.• Test uncertain assumptions.• Develop expertise.