Unique Challenges of Modeling Variable Annuities

© 2014 Oliver Wyman

Guillaume Briere-Giroux, FSA, MAAA, CFA

Unique Challenges of Modeling Variable Annuities

2014 Valuation Actuary Symposium

New York – August 26, 2014

© 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


The modeling of VAs has become increasingly complexToday

1995 2000 2005 2010 2015

200MHzprocessor

2+ GHzprocessor

GPUs

CloudComputing

500+ Cores grid2000+ Cores grid

Clustering

Nested stochasticmodeling

Stochastic modeling

Distributed processing

Computingtechnology

Modelingtechniques

Valuationparadigm

Compression Replicatingportfolios

Behavioral cohortmodeling

Deterministicmodeling

Enhanced behaviormodeling


Where do VAs fit in today’s modeling spectrum?

Real World Risk NeutralValue lenses

Sim

ple

Com

plex

Dynamic policyholder behavior

Static behavior scenarios

None

Behavior “scenarios”

Size of bubbles represents orderof scale for recent new businessvolumes (LTC converted tosingle premium equivalent)

Sales data from LIMRA

Det

erm

inis

tic+

sens

itivi

ties

Sto

chas

ticN

este

dst

ocha

stic

Det

erm

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Integrated dynamic behavior scenarios

Econ

omic

scen

ario

s


In particular, the modeling of VA GLWBs has many movingparts

Product Stochastic equityreturns (RW)

Stochastic interestrates (RW)

RN cost ofguarantees

Dynamichedge

modeling

Behavioralcohorts

Dynamicbehavior

VA GMAB ?VA GLWB

VA GMIB ?FIA GLWB ?IUL

SPIA ?DIA ?LTC ? ?


Consideration #1: Impact of AG 43 reserves and hedgingProfitability 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 firstfour 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 2Standard Scenario Only 10.3% -6.4%With Stochastic AG 43 6.8% -9.5%With Delta Hedging -6.7% -13.0%


Consideration #2: GLWB policyholder behavior cohortsEmerging 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?


Other selected VA modeling considerations

Assumption / feature ConsiderationsFund mapping • Length, frequency and granularity of mapping

Target volatility features • Sensitive to economic scenario generator specifications

Calibration of risk neutralscenarios

• Fully market-consistent or “modified” market-consistent?

Implied volatility modelingin real world projections

• Impact on real world projections with dynamic hedging

Asset modeling in realworld projections

• Has a bigger impact when guarantees become in-the-money, or forcertain “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


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

Economy & Finance

Unique Challenges of Modeling Variable Annuities