This is my final graduation presentation @ Erasmus University Rotterdam

1. 02-09-2009
Pension fund and economic crisis
A scenario generating approach to incorporate economic
crisis in the asset liability management methodology
ir. B. Masselink
dr. O.W. Steenbeek
dr. L.A.P. Swinkels

2. 2Pension funds and economic crisis | 10
Contents
• Introduction
• Method
• Results
• Conclusions
• Recommendations
introduction | method | results | conclusions | recommendations

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Introduction
“The trade-off between long term gains and short term losses should be
made carefully, while anticipating future adjustments of the policy”
(Kouwenberg, 2001)
• Dutch pension assets about 132% of GDP
• Private pension funds lost around 20% between January and
October 2008
• Dutch MSCI all share index declined by more than 50% during
dotcom crisis and credit crunch
• FTK requires 105% funding ratio of Dutch pension funds
Purpose:
Prove that it is technically possible to incorporate crisis in the
scenario generating process using the ALM methodology
introduction | method | results | conclusions | recommendations

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Business cycles and frequency
domain analysis
• Vector AutoRegressive (VAR(p)) models
• Disadvantage: long term dynamics require a lot of variables
• Spectral analysis / Fourier transformation
• Disadvantage: uncertainty of the accuracy, especially low frequency
• Repetitive measurements
• Frequency smoothing
• Parameterizing the model (Steehouwer, 2005)
• Disadvantage of parametric model:
• Number and influence of parameters made ex ante
• One dataset used to fit and one needed for checking
introduction | method | results | conclusions | recommendations

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Including economic dynamics
Three examples (no random factor)
1. First order AutoRegressive (AR) model
2. Frequency Analysis (Steehouwer)
3. Thesis: AR including crisis
When right amount of noise applied, volatility can be equal:
• Same risk-return characteristics
• Different ‘funding profile’
introduction | method | results | conclusions | recommendations

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Generating economic scenarios
• VAR 1:
• traditional first order Vector AutoRegressive model using the
complete range of available data
• VAR 2:
• VAR 2A: first order VAR using only data of ‘non crisis’ periods
• VAR 2B: first order VAR using data of ‘crisis’ periods
• One crisis in each scenario
• Timing is random
• Length is constant
• Same risk-return profile
of VAR 1 and VAR 2
introduction | method | results | conclusions | recommendations

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Results: assets and liabilities
introduction | method | results | conclusions | recommendations

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Results: funding ratio
introduction | method | results | conclusions | recommendations

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Conclusions
1. It is possible to include crises in the ALM approach using the
VAR methodology of generating economic scenarios.
2. By including crisis using the proposed method, long term
dynamics can be incorporated using a rather straight forward
technique
3. Using traditional ALM methodology the pension fund risk profile
is underestimated.
introduction | method | results | conclusions | recommendations

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Recommendations
Depth
• Include demographic and actuary models
• Extend the investment mix
• Evaluate hedging strategies
• Multiple crisis and random length
• Extend the different kind of periods (now: good and bad)
• Include (transaction) costs
Width
• Other institutions can apply same methodology
• Optimize the algorithms and/or combine stochastic programming
techniques
• Investigate intergenerational transfers during crisis.
• Perform sensitivity analysis
introduction | method | results | conclusions | recommendations

11. 02-09-2009
Pension fund and economic crisis
A scenario generating approach to incorporate economic
crisis in the asset liability management methodology

12. 12Pension funds and economic crisis | 10
APPENDIX

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Stochastic programming vs. scenario
analysis
• Stochastic programming
• Step by step evolution
• Dimensions increase exponentially
• Scenario analysis
• Linear independent scenarios

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Historical economic data
• Monthly data between 1977M02 till 2009M03 (410 months)
• Dutch Consumer Price Index (CPI)
• Dutch MSCI All Share Index,
• Interest rates:
• spot
• 1, 3 and 6 months
• 1 and 10 years
• 10 years
• Crisis:
• 1987M08 – 1988M01: Oil crisis
• 2000M11 – 2003M04: Dotcom crisis
• 2007M06 – 2009M03: Credit Crunch

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Economic scenarios
• Nelson Siegel parameters
• β0, β1, β2
• τ
• rstocks
• ΔCPI
• 600 months (50 years)
• Crisis: 58 months (14.17%)
• Initial funding ratio is 125%

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Assumptions
• The state of the economy is represented by a six dimensional state, generated using a first
order VAR model.
• Scenarios consists of good and bad periods generated using two independent VAR algorithms
• The length of these good and bad periods is in the same proportion as the historical
observation period.
• The VAR 2 model includes one crisis of 85 months in each scenario. The start of the crisis is
random.
• The calculation of future liabilities and assets is done without incorporating a demographic and
actuary model.
• The investment mix only consists of the market index and bonds with several maturities.
• Recovery plans, contribution schemes and changes in the investment mix are beyond the
scope of this thesis.
• Transaction and other costs are not included in this analysis.

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Funding ratio characteristics