Unique Challenges of Modeling Variable Annuities
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Presentation for 2014 Valuation Actuary Symposium (New York). After an introduction to the history of variable annuity financial modeling and current modeling paradigms, this presentation covers the unique modeling considerations related to variable annuities.
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Unique Challenges of Modeling Variable Annuities
- 1. © 2014 Oliver Wyman Guillaume Briere-Giroux, FSA, MAAA, CFA Unique Challenges of Modeling Variable Annuities 2014 Valuation Actuary Symposium New York – August 26, 2014
- 2. © 2014 Oliver Wyman 11© 2014 Oliver Wyman Agenda I. Recap of variable annuity (VA) modeling history II. What is unique about modeling VAs? III. Modeling considerations IV. Lessons learned
- 3. © 2014 Oliver Wyman 22© 2014 Oliver Wyman The modeling of VAs has become increasingly complex Today 1995 2000 2005 2010 2015 200MHz processor 2+ GHz processor GPUs Cloud Computing 500+ Cores grid 2000+ Cores grid Clustering Nested stochastic modeling Stochastic modeling Distributed processing Computing technology Modeling techniques Valuation paradigm Compression Replicating portfolios Behavioral cohort modeling Deterministic modeling Enhanced behavior modeling
- 4. © 2014 Oliver Wyman 33© 2014 Oliver Wyman Where do VAs fit in today’s modeling spectrum? Real World Risk NeutralValue lenses SimpleComplex Dynamic policyholder behavior Static behavior scenarios None Behavior “scenarios” Size of bubbles represents order of scale for recent new business volumes (LTC converted to single premium equivalent) Sales data from LIMRA Deterministic+ sensitivities Stochastic Nested stochasticDeterministic Integrated dynamic behavior scenarios Economicscenarios
- 5. © 2014 Oliver Wyman 44© 2014 Oliver Wyman In particular, the modeling of VA GLWBs has many moving parts Product Stochastic equity returns (RW) Stochastic interest rates (RW) RN cost of guarantees Dynamic hedge modeling Behavioral cohorts Dynamic behavior VA GMAB ? VA GLWB VA GMIB ? FIA GLWB ? IUL SPIA ? DIA ? LTC ? ?
- 6. © 2014 Oliver Wyman 55© 2014 Oliver Wyman Consideration #1: Impact of AG 43 reserves and hedging Profitability is highly path dependent and driven by market events Projected interest rates Realized volatilitiesCumulative annualized returns Scenario 1 is not performing well with a Delta hedging strategy due to hedge losses in the first four years, followed by a spike in interest rates which unfavorably impacts persistency PV After Tax Profits at Risk Discount Rate / Initial Separate Account Assets Scenario 1 Scenario 2 Standard Scenario Only 10.3% -6.4% With Stochastic AG 43 6.8% -9.5% With Delta Hedging -6.7% -13.0%
- 7. © 2014 Oliver Wyman 66© 2014 Oliver Wyman Consideration #2: GLWB policyholder behavior cohorts Emerging experience shows distinct cohorts that exhibit “integrated” behavior Cohort Observed behavior “Efficient” users • Utilize 100% of GLWB maximum income • Strong utilization “feature skew” • Low lapse rate • More efficient dynamic lapses “Partial” users • Utilize less than 100% of GLWB maximum income • Weaker utilization skew • Higher lapse rate than efficient users • Less efficient dynamic lapses “Excess” users • Utilize more than 100% of GLWB maximum income • Very high lapse rates • Least efficient dynamic lapses “Waiting” users • Have not yet utilized • Low lapse rates • Efficient dynamic lapses • Waiting for rollup? Are “waiting” users going to emerge as “efficient” users?
- 8. © 2014 Oliver Wyman 77© 2014 Oliver Wyman Other selected VA modeling considerations Assumption / feature Considerations Fund mapping • Length, frequency and granularity of mapping Target volatility features • Sensitive to economic scenario generator specifications Calibration of risk neutral scenarios • Fully market-consistent or “modified” market-consistent? Implied volatility modeling in real world projections • Impact on real world projections with dynamic hedging Asset modeling in real world projections • Has a bigger impact when guarantees become in-the-money, or for certain “CPPI-based” designs Dynamic lapses • Make in-the-moneyness sensitive to interest rates? Mortality improvement • Systemic mortality improvement trends • Health of lapsers leads to cohort seasoning? Joint payout options • Appropriate data capture • Modeling of joint mortality
- 9. © 2014 Oliver Wyman 88© 2014 Oliver Wyman Lessons learned 1 Review assumptions in their context 2 Start modeling with simple cells 3 Run a few easy to interpret scenarios 4 Design robust drill down and analytics 5 Design runs to tell a meaningful story