Economic Scenario Generators in Pricing - Larochelle Moreno

Economic scenario generators in pricing
Jean-Philippe Larochelle, FSA, CERA
David Moreno Jr., FSA, CERA, FRM

Ernst & Young LLP disclaimer
► The views expressed by the presenters are not
necessarily those of Ernst & Young LLP (EY).
► This material has been prepared for general informational
purposes only and is not intended to be relied upon as
accounting, tax or other professional advice.
► Please refer to your advisors for specific advice reflecting
your circumstances, objectives and needs.
Economic scenario generators in pricingMay 8, 2017

Key questions
How can actuaries think
critically about their
Economic Scenario
Generators (ESG) in the
current low interest rate
environment?
How can actuaries
effectively use ESGs in
their pricing analyses?
2
How can actuaries think
critically about their
economic scenario
generators (ESG) in the
current interest rate
environment?
1

Discussion topics
Real-world interest ESG
► Background
► Implications of the current interest rate
environment
1 Case study
► Compare the Academy Interest Rate
Generator (AIRG) to an alternative ESG
► Evaluate the impact on a deferred
income annuity (DIA) product pricing
2

1. Real-world interest rate models

Real-world ESG in the life insurance industry
► North American life insurance companies use real-world stochastic interest
rates for a variety of applications including, but not limited to:
AIRG**
47%
Homegrown
28%
Third party
25%
ESG used for real-world interest rates modeling*
US regulatory capital
(C-3 Phase 1)
US statutory valuation,
VM-20
Actuarial Guideline (AG) 43
Asset adequacy analysis /
cash flow testing
US GAAP, SOP 03-1
Risk analysis, economic
capital
Pricing
*Based on a EY proprietary survey of U.S. life insurance companies **Academy’s Interest Rate Generator (AIRG)

Implications of the current interest rate
environment
Widening Credit Spreads
Rapid Increase in Interest Rates
Adverse Equity Market Movements
Prolonged Low Interest Rates
Economic scenario that poses the most significant economic
stress to insurance companies*
40%
24%
24%
12%
*Based on a EY proprietary survey of US life insurance companies
Capture the uncertain transition
to higher interest rates
Easily adaptable to different
views
Design an alternative real-world interest rate
scenario generator
Capture the possibility of
reverting back to a low interest
rate environment
Prolonged low interest rates
Adverse equity market movements
Rapid increase in interest rates
Widening credit spreads

2. Case study
Impact on actuarial liabilities

Historical Treasury rates
Overview
► Calibrated historical interest rates to two regimes based on different macroeconomic conditions
Low regime
► Simulates interest rates based on
the post-financial crisis
environment (2009-2017).
► Lower mean reversion point and
stronger mean reversion speed
► Interest rates move around the
MRP with low volatility
High regime
► Simulates an interest rate
environment similar to the 1977-
2008 period.
► Mean reversion point (MRP)
closer to historical average
► Interest rates move towards MRP
with high volatility
High regime Low regime

Alternative ESG
Regime-switching key rate Cox-Ingersoll-Ross (CIR)
► Regime-switching: Uses a Markov chain process to model the transition
between regimes
► Calibrated to maximum likelihood using historical Treasury spot rates
► Uses the CIR stochastic differential equation
► Selected CIR over Vasicek and Black-Karasinski
► Validated using backtesting
Annual transition probability To low regime To high regime
From low regime 80.0% 20.0%
From high regime 2.9% 97.1%

Projected interest rates – AIRG
Rates in 2047 AIRG
5% percentile 2.09 %
Average 4.06 %
AIRG 10-year Treasury spot rate projection

Projected interest rates – Alternative ESG
Rates in 2047
Alternative ESG
Phigh(t=0) = 25%
Average 4.63 %
Alternative ESG 10-year Treasury spot rate projection

Deferred income annuity
Business mix assumptions
Business mix by age
Business mix by wait time
3.00%
8.00%
11.00%
22.00%
33.00%
20.25%
2.25% 0.50% 0.00%
0%
10%
20%
30%
40%
40 45 50 55 60 65 70 75 80
37.20%
24.10%
16.10%
13.10%
9.50%
0%
10%
20%
30%
40%
5 10 15 20 30

Pricing results
► The conclusion of our analysis may vary depending on the ESG used.
► Priced the product using a single deterministic scenario
► Target ROI of 7.5%
► Generated scenario ROI using an ALM pricing model
Phigh(t=0) = 10% Phigh(t=0) = 25% Phigh(t=0) = 50%
VaR25 8.4% 8.9% 9.7% 10.8%
Average 7.1% 6.6% 7.4% 8.3%
VaR75 5.7% 4.6% 5.1% 5.5%
VaR95 3.7% -0.2% 0.8% 2.2%
VaR99 1.4% -5.3% -4.2% -2.8%
AIRG
Alternative ESG
Metric

Closing remarks
► Pricing actuaries need to understand the underlying assumptions and
strengths/weaknesses of the ESG used in order to properly perform their
analysis.
► Using an ESG without the proper understanding when pricing a life insurance
or annuity product can result in issuing a mispriced product or in issuing a
product where the financial risks are misunderstood.
David Moreno Jr, FSA, CERA, FRM
david.moreno1@ey.com
212 773 9496
Jean-Philippe Larochelle, FSA, CERA
jeanphilippe.larochelle@ey.com
212 773 6035

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