2009 09 02 M Sc Presentatie

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

Contents
Introduction
Method
Results
Conclusions
Recommendations
introduction | method | results | conclusions | recommendations

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

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

Including economic dynamics
Three examples (no random factor)
First order AutoRegressive (AR) model
Frequency Analysis (Steehouwer)
Thesis: AR including crisis
When right amount of noise applied, volatility can be equal:
Same risk-return characteristics
Different ‘funding profile’

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 profileof VAR 1 and VAR 2

Results: assets and liabilities

Results: funding ratio

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

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

Pension fund and economic crisis
A scenario generating approach to incorporate economic crisis in the asset liability management methodology

appendix

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

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

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%

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 badperiods 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.

Funding ratio characteristics