This presentation by Marie Briere, Amundi, was made at the OECD-Risklab-APG Workshop on pension fund regulation and long-term investment held in Amsterdam on 7 April 2014. Discussions focused on: long-term pension investment strategies under risk-based regulation; riskiness and procyclicality in pension asset allocation; and, regulatory challenges for long-term illiquid assets. For more information, please visit: http://www.oecd.org/daf/fin/private-pensions/OECD-APG-workshop-pension-fund-regulation-LTI.htm

1. Does Regulation Matter?
Riskiness and Procyclicality of Pension Asset Allocation
L.N. Boon Amundi, Netspar, Paris Dauphine and Tilburg
University
M. Brière Amundi, Paris Dauphine University and
Université Libre de Bruxelles
S. Rigot Université Paris Nord, CEPN
OECD–APG Workshop – April 2014
Pension Funds Regulation and Long Term Investment
Preliminary – Do not quote

2. 2
Ongoing debate on pension regulation in Europe: application of a
Solvency Framework (EIOPA, 2012) ?
Our question: does the type of regulation have an influence on the
asset allocation of DB pension funds?
– % of risky assets
– Procyclicality
We attempt to quantify the importance of regulatory factors on top of
individual/ structural characteristics
US, Canada and the Netherlands are particularly interesting cases :
– They underwent notable regulatory changes: Pension Protection Act in
2006 in the US, Financial Assessment Framework in 2007 in the
Netherlands
– Fund regulation varies across countries and types of funds
Motivation

3. 3
Related Literature: Drivers of pension fund’s allocation
Individual characteristics of the funds are a major determinant of the
riskiness of pension plan’s asset allocation
– Size (Dyck and Pomorski, 2011)
– Maturity (Rauh, 2009 ; Bikker, 2011)
– Inflation indexation (Sundaresan and Zapatero, 1997; Lucas and Zeldes,
2006)
Institutional characteristics of the plan: presence of a guaranteeing
mechanism (PBGC in the US, PBGF in Ontario)
– This insurance is in effect a put option that reduces the negative impact of
pension liabilities on the firm’s value (Sharpe, 1976 ; Treynor, 1977; Nielson
and Chan, 2007; Crossley and Jametti, 2013)
Regulatory environment
– US public funds increased their risky asset allocation to maintain high
discount rates and present lower liabilites (Pennachi and Rastad, 2011;
Andonov et al. 2013)

4. 4
Related Literature: Debate on the efficiency of regulation
Use of risk models to calibrate solvency buffers
– Limit financial insitutions’ ability to take risk (Severinson and Yermo, 2012)
– Generate procyclical investment (Bec and Gollier, 2009)
– Generate substantial economic costs when repeated short term VaR
constraints are imposed on long term investors (Shi and Werker, 2012)
Mark-to-market accounting methods
– Constitute an additional source of price volatility, especially for long maturity
or illiquid assets (Plantin et al., 2008)
– Limit investors’ ability to take risk (Severinson and Yermo, 2012)
– Generate procyclical investment (Novoa, Scarlata and Solé, 2009)
– Generate contagion (Allen and Carletti, 2008)

5. 5
Empirical investigation of the drivers of pension fund’s asset
allocations
– Expanding the literature over all regulatory dimensions
– Quantifying / comparing the impact of regulation with other explanatory
factors
Main Findings
– Regulatory factors play a strong role in explaining pension fund’s asset
allocation choices
– They have a much larger economic impact than individual characteristics
– Similar in amplitude to institutional factors
Our Paper

6. 6
Regulatory changes induced a significant reduction in risky asset
allocation
Risk-based capital requirements have the strongest impact
– They induce a strong reduction in risky asset weights, especially equities
– They have positive impact on alternatives (especially private equity, real
estate) and risky fixed income (mainly high yield)
The choice of the liabilities discount rate comes as the second
largest factor
Our Results: Riskiness of asset allocation

7. 7
We build two original procyclicality measures
– Funds are procyclical in the sense that they do not fully rebalance
– Strong evidence of additional procyclicality during financial crises
(asset weight decrease stronger than implied by asset drift)
Little evidence of the impact of regulation on procyclicality
– Quantitative investment restrictions reduce procyclicality on the restricted
asset classes
– Counterintuitively, risk-based regulation induced a slighly lower
procyclical behavior
– Result driven by the temporary regulatory slackening during the last crisis in
the Netherlands
Our Results: Procyclicality

8. 8
Differences in Pension Funds’ Regulatory Environment
US public US private Canada public and private
Dutch public
and private
INVESTMENT RESTRICTIONS
Quantitative
investment
restrictions
Yes
No unified
regulation
None
Prior to 2005:
Max 30% on foreign assets
2005-2010:
Max 15% on resource
property, 25% real estate and
Canadian natural resource
property.
After 2010:
None
None

9. 9
Differences in Pension Funds’ Regulatory Environment
US public US private
Canada public and
private
Dutch public
and private
VALUATION REQUIREMENTS : ASSETS
Asset
valuation
GASB 27:
Actuarial
value
Before 2006:
ERISA+FAS87
Fair value, discounted
cash flow, book value,
smoothed value
After 2006:
PPA +FAS157
Fair value with 24M
smoothing (smoothed
value bounded
between 90%-110% of
asset’s current market
value)
Before 2000: CICA3460
Market value or market
related value.
After 2000: CICA3461
Fair value or market
related value adjusted to
moderate its volatility,
discounted cash flows,
constant yield to
maturity for illiquid
assets.
After 2011: IAS19
Market value
Before 2007: PSW
Market value
After 2005: IAS19
Market value to
calculate the
unfunded pension
liabilities for listed
corporate sponsors
After 2007: FTK
Market value
In red: funding regulation
In blue: accounting regulation

10. 10
Differences in Pension Funds’ Regulatory Environment
US public US private
Canada public
and private
Dutch public
and private
VALUATION REQUIREMENTS: LIABILITIES
Liability
discount
rate
GASP:
Expected
return of
assets
Before 2004: ERISA
Corridor around 4Y
average of 30Y T-Bond.
2004-06: PFEA
Corporate bond market
rate, 4Y average.
Since 2006: PPA+FAS158
Corporate bond market
rate (smoothing for PPA)
Before 2000 : CICA3460
Long term expected
return of assets (“best
estimate”)
After 2000: CICA3461
Government bond
rate
Before2007: PSW
Fixed actuarial
interest rate with
a maximum.
Since 2007: FTK
Swap rate.
Liabilities
recognized
in
sponsor/
gvt
balance
sheet
No Before 2006: FAS87
In footnotes.
Since 2006 : FAS158
Yes
Private plans:
Yes since 2000
(CICA3461)
Public plans:
Yes with exceptions
No

11. 11
Differences in Pension Funds’ Regulatory Environment
US public US private
Canada public and
private
Dutch public and
private
FUNDING REQUIREMENTS
Quantitative
risk
requirements
None None None Since 2007: FTK
Yes
Fixed
minimum
funding
requirements
No min
(0%)
1994-2006: Retirement
Protection Act
90%
Since 2006 : PPA
92% in 2008
94% in 2009
96% in 2010
100% in 2011
100% Before 2007: PSW
100%
Since 2007: FTK
105% at confidence
level of 97.5% with 1Y
horizon.
Recovery
period
None Before 2006: ERISA
30Y
Since 2006: PPA
7Y
Usually 5Y
Federal plans +
Alberta and Ontario
plans have a max
amortization period
of 10Y since 2009
Before 2007: PSW
10Y
Since 2007: FTK
3 – 15 years depending
on continuity analysis

12. 12
Methodology : Regulatory variables definition
Variable Definition Riskiness Procyclicality
Investment requirements
Quantitative
investment
restrictions (QIR)
Dummy: 1 on the existence of limits
on any asset class
- -
Valuation requirements
Asset valuation
(Mkt Val)
Dummy: 1 if market valuation without
discretion, 0 otherwise
- +
Excess liability
discount rate
(eLDR)
Discount rate level disclosed by the
fund minus the fund’s home country
government 10Y interest rate
+ ≈
Liabilities’
recognition in
sponsor/ gvt balance
sheet (LiabRecog)
Dummy: 1 if liabilities are recognized
on the sponsor’s (i.e., enterprise or
government) balance sheet
- +
Funding requirements
Min funding
requirement (Fund
Req)
Level of funding requirement ≈ +
Quantitative risk-
based capital
requirements
(QRR)
Dummy: 1 on the existence of
mandatory quantitative risk-based
capital requirements
- +
Recovery period
(Recov)
Average recovery period in years + -

13. 13
Methodology : Individual and institutional variables definition
Variable Definition Riskiness Procyclicality
Individual characteristics
Maturity
(Maturity)
Percentage of retired members - ≈
Inflation indexation
(InfIndx)
Percentage of member’s benefits
contractually indexed to inflation
+ ≈
Size
(Size)
Market value of Assets Under
Management in billions of USD
+
(for alternatives)
≈
Institutional characteristics
Guarantee
(Guarantee)
Dummy: 1 if pension benefits are
collectively insured by a guarantee
fund
+ ≈

14. 14
Panel regression analysis with the following explanatory variables
Methodology
InstitutionalFactor
FundCharacteristics
RegulatoryFactors
Quantitative Investment
Restriction
Excess liability discount
rate
Mark-to-market asset
valuation, min funding
requirements and recovery
period
Liabilities in sponsor’s
balance sheet
Quantitative risk-based
capital requirements
Maturity
Indexation
Size of AUM
External
guarantee

15. 15
Allocation to risky assets
We estimate the following pooled panel regression model
Methodology
• is the allocation to risky assets, or its sub-categories: equities, risky
fixed income, and alternatives for fund i at time t.
• Standard errors are clustered by Year
=∝ + 1 + 2 + 3 + 4 + 5 + 6
+ 7 "# "$% + 8' ( + 9* " + +

16. 16
Procyclicality of Equity Investment
Definition of the procyclicality measure
Methodology
,-
(/)
= 1
1
0
if sign( - /
)=sign( 9
)
otherwise
• is the fund i’s allocation to risky assets at time t
• @
is a reference weight the fund would have if it were not procyclical
• A fund is considered procyclical if it increases its asset allocation to risky
assets in response to high performances that year (and the reverse)

17. 17
Procyclicality of Equity Investment
We define two alternative definitions of the reference weight
– A full rebalancing strategy: reference weights are considered constant and
equal to the fund’s average reported allocation over time
– A no-rebalancing strategy: reference weights are defined as the weights
that the fund would have if it lets assets drift along with market performance
Methodology
AB
= CD
DEFGH
IGJKL
DEFGH
M
is the asset drift weight.
F NOP is the risky asset return
Q
is the total return of the fund.
RS
=
1
T
U
Q
VD

18. 18
Procyclicality of Equity Investment
We estimate the following logit regression model
Methodology
• ,-
@
is the procyclicality indicator for fund i at time t
ln X
,Y,-
(/)
= 1Z
1 − ,Y,-
(/)
= 1Z
=] + 1 + 2 + 3 + 4
+ 5 + 6 + 7 "# "$% + 8' (
+ 9* " + +

19. 19
CEM Benchmarking Database
– More than 800 DB pension funds
– Around 500 in the US, 250 in Canada and 80 in the Netherlands,
representing respectively 40%, 90% and 30% of US, Canadian and Dutch
DB funds
– Yearly asset allocation and performance over 1990-2011
Data

20. 20
Pension Funds’ Measure of Procyclicality
Procyclicality measure
based on equity market
Average over all funds
More procyclicality during
periods of expansion
US public funds much
more procyclical since
2008
Procyclicality Measure (comparison to full rebalancing strategy)

21. 21
Pension Funds’ Measure of Procyclicality
Procyclicality measure
based on equity market
Average over all funds
Evidence of procyclicality
during the 2 crises (2001
– 2007)
Procyclicality Measure (comparison to no-rebalancing strategy)

22. 22
Results: risky asset allocation
Dependent variable:
Percentage Allocation to
Risky Assets Equities Risky Fixed
Income
Alternatives
Quantitative Investment Restrictions -0.573
(1.3)
9.490***
(1.470)
-2.740***
(1.020)
-7.320***
(1.360)
Excess Liability Discount Rate 4.670***
(0.673)
3.660***
(0.876)
-0.204
(0.253)
1.210*
(0.687)
Funding, Recovery, Market Valuation -2.420
(1.810)
-8.760***
(2.180)
2.400**
(1.220)
3.940**
(1.880)
Liabilities Recognized in Sponsor's Balance Sheet -1.610*
(0.832)
-2.690**
(1.190)
-0.153
(0.166)
1.230**
(0.499)
Quantitative Risk-based Capital Requirements -8.200***
(1.710)
-12.700***
(2.750)
0.691
(1.130)
3.810***
(1.290)
Maturity -2.480***
(0.414)
-3.180***
(0.377)
-0.082
(0.062)
0.779***
(0.201)
Inflation Indexation 0.648**
(0.283)
-2.030***
(0.646)
-0.068
(0.099)
2.750***
(0.467)
Size 2.540***
(0.200)
-1.910***
(0.595)
0.305***
(0.094)
4.140***
(0.470)
Guarantee 7.330***
(1.240)
11.800***
(2.260)
-2.670**
(1.200)
-1.760
(1.920)
Intercept 64.700***
(1.360)
57.900***
(1.260)
1.560***
(0.318)
5.230***
(0.545)
2 0.213 0.182 0.084 0.174
Adjusted- 2 0.211 0.180 0.081 0.172
Nobs. 3932 3932 3932 3932
Significance: *0.1, **0.05,***0.01

23. 23
Results: risky asset allocation (alternatives)
Dependent variable:
Percentage Allocation to
Commo Infra RE PE, VC, LBO HF
Quantitative Investment Restrictions -0.278***
(0.076)
-0.263
(0.302)
-3.120***
(0.618)
-2.210***
(0.319)
-2.400***
(0.845)
Excess Liability Discount Rate -0.081
(0.051)
0.179**
(0.072)
-0.351
(0.289)
1.220***
(0.290)
0.591***
(0.203)
Funding, Recovery, Market Valuation 0.230**
(0.095)
0.684*
(0.390)
-0.576***
(0.194)
0.777***
(0.282)
1.260***
(0.276)
Liabilities Recognized in Sponsor's Balance Sheet 0.045
(0.030)
-0.112
(0.081)
1.210*
(0.635)
2.140***
(0.539)
2.630**
(1.210)
Quantitative Risk-based Capital Requirements 1.410***
(0.180)
0.0305
(0.290)
2.850***
(0.537)
7.250***
(0.392)
-1.070
(0.869)
Maturity 0.0003
(0.024)
-0.005
(0.010)
0.235*
(0.137)
0.238***
(0.059)
0.088
(0.151)
Inflation Indexation 0.090***
(0.023)
0.131***
(0.031)
1.200***
(0.134)
0.778***
(0.233)
0.925***
(0.159)
Size 0.098***
(0.038)
0.185***
(0.046)
1.550***
(0.120)
2.420***
(0.268)
0.316***
(0.075)
Guarantee -0.193*
(0.106)
-0.525
(0.364)
-2.060***
(0.703)
-0.693
(0.490)
-1.410
(1.160)
Intercept 0.118*
(0.063)
-0.234***
(0.083)
4.150***
(0.294)
-0.309
(0.295)
-0.330**
(0.147)
2 0.112 0.052 0.168 0.214 0.079
Adjusted- 2 0.110 0.050 0.166 0.212 0.077
Nobs. 3932 3932 3932 3932 3932
Significance: *0.1, **0.05,***0.01

24. 24
Results: risky asset allocation (risky fixed income)
s Dependent variable:
Percentage Allocation to
High Yield Mortgages
Quantitative Investment Restrictions -1.560***
(0.161)
-1.180***
(0.134)
Excess Liability Discount Rate 0.281***
(0.079)
-0.485***
(0.066)
Funding, Recovery, Market Valuation 1.140***
(0.206)
1.260***
(0.172)
Liabilities Recognized in Sponsor's Balance Sheet 0.154*
(0.086)
-0.307***
(0.072)
Quantitative Risk-based Capital Requirements 0.672**
(0.277)
0.019
(0.231)
Maturity -0.042
(0.065)
-0.0406
(0.054)
Inflation Indexation 0.117*
(0.070)
-0.185***
(0.058)
Size 0.174***
(0.065)
0.131**
(0.054)
Guarantee -1.040***
(0.194)
-1.630***
(0.162)
Intercept 0.614***
(0.121)
0.950***
(0.101)
2 0.070 0.070
Adjusted- 2 0.068 0.068
Nobs. 3932 3932
Significance: *0.1, **0.05,***0.01

25. 25
Regulatory factors have much more economic impact than individual
characteristics, similar to institutional factors
– Significant reduction in risky asset allocation
Risk-based capital requirements have the strongest impact
– Strong reduction in risky asset weights, especially equities
– Positive impact on alternatives (especially private equity, real estate) and
risky fixed income (mainly high yield)
The choice of the liabilities discount rate comes as the second
largest impact
Results: risky asset allocation

26. 26
Results: procyclicality (comparison to full rebalancing strategy)
Dependent variable:
,-
Risky Assets Equities Risky Fixed
Income
Alternatives
Quantitative Investment Restrictions 0.259
(0.185)
0.990***
(0.309)
-2.030**
(0.905)
-0.857***
(0.277)
Excess Liability Discount Rate 0.679***
(0.156)
0.315
(0.208)
0.581
(0.269)
0.256
(0.234)
Funding, Recovery, Market Valuation 0.221
(0.293)
-0.940**
(0.419)
2.310
(1.170)
0.831***
(0.25)
Liabilities Recognized in Sponsor's Balance
Sheet
-0.510**
(0.228)
-0.533**
(0.248)
-0.223
(0.215)
0.306*
(0.185)
Quantitative Risk-based Capital Requirements -0.375
(0.419)
0.167
(0.603)
-0.718
(0.976)
-0.258
(0.420)
Maturity 0.104
(0.086)
0.008
(0.089)
0.072
(0.091)
0.014
(0.066)
Inflation Indexation 0.049
(0.109)
-0.093
(0.125)
0.211***
(0.085)
0.216
(0.135)
Size 0.203
(0.123)
-0.001
(0.151)
0.695***
(0.110)
0.238*
(0.132)
Guarantee 0.021
(0.236)
0.908**
(0.393)
-1.900
(1.150)
-0.731***
(0.278)
Intercept -0.486**
(0.197)
0.176
(0.254)
-2.200
(0.404)
-0.819***
(0.192)
Pseudo- 2 0.024 0.027 0.074 0.019
Nobs. 3932 3932 3932 3932
Significance: *0.1, **0.05,***0.01
VIII
This is 1 − 1
0
, 1 is the log likelihood of the estimated model. 0 is the log likelihood of the null model with only the
constant term.

27. 27
Results: procyclicality (comparison to no-rebalancing strategy)
Dependent variable:
,-^
Risky Assets Equities Risky Fixed
Income
Alternatives
Quantitative Investment Restrictions -0.534**
(0.269)
1.140**
(0.558)
-1.640**
(0.721)
-0.691
(0.468)
Excess Liability Discount Rate -0.322
(0.218)
-0.310
(0.230)
0.087
(0.290)
-0.305**
(0.127)
Funding, Recovery, Market Valuation 0.484
(0.327)
-1.350**
(0.534)
1.040
(1.180)
0.164
(0.477)
Liabilities Recognized in Sponsor's Balance
Sheet
0.190
(0.263)
-0.374
(0.348)
0.074
(0.595)
0.375
(0.343)
Quantitative Risk-based Capital Requirements -1.030*
(0.573)
0.803
(0.597)
-0.477
(1.160)
-0.575
(0.607)
Maturity -0.066
(0.071)
-0.195*
(0.116)
0.117
(0.121)
-0.171
(0.111)
Inflation Indexation 0.050
(0.131)
-0.042
(0.084)
0.260
(0.173)
0.179
(0.133)
Size -0.0217
(0.213)
0.082
(0.089)
0.484***
(0.108)
0.0264
(0.194)
Guarantee -0.468*
(0.279)
1.500***
(0.541)
-0.980
(1.100)
-0.493
(0.398)
Intercept -0.744*
(0.400)
1.450***
(0.373)
-2.440***
(0.419)
-0.473***
(0.163)
Pseudo- 2 0.007 0.025 0.049 0.012
Nobs. 3932 3932 3932 3932
Significance: *0.1, **0.05,***0.01

28. 28
We find evidence of procyclicality
– Funds do not set their weights equal to full-rebalancing
– Additional procyclicality during financial crises (asset weight decrease
stronger than implied by asset drift)
Little evidence of regulatory impact on procyclicality
– Except for quantitative investment restrictions
Counterintuitively, risk-based regulation induced lower procyclical
behavior
– Result driven by the temporary regulatory slackening during the last crisis in
the Netherlands (extension of recovery period, suspension of pension
indexation or reduction in nominal pensions, higher contribution rates
allowed, etc.)
Results: procyclicality

29. 29
Our objective: quantify the importance of regulatory factors on top of
individual / structural characteristics of the funds
Regulation plays a strong role in pension funds’ asset allocation
choices, compared to institutional / individual funds’ variables
All regulatory measures (asset valuation, funding requirements) and
in particular risk-based capital requirements decreased the overall
risky asset allocation
Strong reduction of equities, but paradoxically risk-based
regulations led to an increase in alternatives, especially real estate
and private equity
Conclusion

30. 30
We find evidence of some form of procyclicality, more pronounced
during financial crises
Counterintuitively, we do not find that risk-based regulation induced a
more procyclical behavior
– Unique to the Netherlands: the DNB authorized numerous waivers to the
standing regulation during the Subprime crisis (especially extension of the
recovery period) to assist pension funds
– This argues for a « dynamic » setting of regulatory rules ?
Conclusion

31. A joint stock company (Société anonyme) with registered capital of 546,162,915 euros
An investment management company approved by the French Securities Authority
(Autorité des marchés financiers) under no. GP 04000036
Registered office: 90 boulevard Pasteur 75015, RCS Paris no. 437 574 452