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Chairholders:
Christian Gourieroux (CREST and University of Toronto)
Christophe Pérignon (HEC Paris)
A Network of Researchers on
Regulation and Systemic Risks
A Network in the Credit Default Swap Market:
Chair Network:
2
Research Themes
(non-exhaustive list)
(1) Definition and measure of systemic risk (SIFIs)
(2) Regulatory capital
(3) Sources of financial instability
(4) Reserves for solvency and liquidity risks
(5) Real effects of banking and financial regulation
(6) Clearing houses
(7) Estimation risk
(8) Collateral
(9) Contagion
(10) Interconnection of financial institutions
(11) Validation of risk models
(12) Mortality and longevity risk
3
List of Researchers
AURAY Stéphane (CREST and ENSAI)
CHALLE Edouard (CNRS, Ecole Polytechnique et CREST)
DE BANDT Olivier (ACPR)
FERMANIAN Jean-David (CREST)
FRAISSE Henri (ACPR)
GOURIEROUX Christian (CREST and University of Toronto) (chair)
HEAM Jean-Cyprien (ACPR and CREST)
HURLIN Christophe (University of Orleans)
LOISEL Olivier (CREST and ENSAE)
MONFORT Alain (University of Maastricht and CREST)
PERIGNON Christophe (HEC Paris) (co-chair)
RENNE Jean-Paul (Banque de France)
THESMAR David (HEC Paris)
ZAKOIAN Jean-Michel (CREST)
Associated Researchers
BILLIO Monica (University of Venice)
DUBECQ Simon (European Central Bank)
GAGLIARDINI Patrick (University of Lugano)
JASIAK Joan (York University, Canada)
SCAILLET Olivier (University of Geneva)
4
Research Activities
•
Monthly Seminar on Regulation and Systemic Risks
“Basels Accords versus Solvency II: Regulatory Adequacy and Consistency under the
Postcrisis Standards”, Caroline Siegel (Université de St Gallen), May 6, 2014
•
Conferences
“Systemic Risk and Financial Regulation” Conference, July 3-4, 2014, Banque de
France. Guest speakers: Darrel Duffie (Stanford) and Robert Engle (NYU)
•
Books [2 in 2013]
•
Working papers [30 in 2013] and published articles [34 in 2013]
•
Presentations at seminars and meetings [>50 in 2013]
5
[1] How to Make Clearing Houses
More Resilient
”Derivatives Clearing, Default Risk, and Insurance”
Jones and Pérignon (2013)
i
j
Equity
L
L
Interest rate
S
S
Commodity
L
L
margin
”CoMargin”, Cruz, Harris, Hurlin, and Pérignon (2014)
CoMargin
[2] Quantifying the Collateral Risk of ETFs
“The Collateral Risk of ETFs”, Hurlin, Iseli, Pérignon and Yeung (2014)
•
In a physical ETF, investors' money is directly invested in the index constituents.
•
In a synthetic ETF, the fund issuer enters into a total return swap with a financial
institution which promises to pay the performance of the underlying index.
69% of the ETFs and 35% of AUM in Europe, 100% of leveraged and inverse ETFs in the world
Swap, hence counterparty risk  collateral
•
Since 2011, allegations made by the Financial Stability Board and IMF about the
overall poor quality of ETF collateral.
 Massive outflows from synthetic ETFs in 2011-Q3.
 89% of investors in the UK state a specific preference for physical ETFs
over synthetic ETFs (Morningstar, April 2012).
 UCITS rules: 20% max per issuer in collateral portfolio; swap reset
7
Synthetic ETF Structure
8
Synthetics: From Allegations to Outflows
120
Factiva (left axis)
300
100
Google (rigth axis)
250
80
200
60
150
40
100
20
50
0
Google Searches for "Synthetic ETF"
Number of Press Articles on "Synthetic ETF"
350
0
2006
2007
2008
2009
2010
2011
2012
2013
2014*
2009
2010
2011
2012
2013
2014*
Annual Change in AUM in Europe ($ bio)
60
Physical ETFs
50
Synthetic ETFs
40
30
20
10
0
2006
2007
2008
-10
-20
-30
9
Low-Quality Collateral? Let’s Have a Look
•
Study the composition of the $40.9bn collateral portfolio of 164 ETFs managed by
the second largest ETF provider in Europe, db x-Trackers.
•
For each fund, we know the exact composition of the collateral portfolio every
week between July 2012 and November 2012.
•
What we do:
(1) Measure the level of collateralization (NAV vs. collateral)
(2) Assess the quality of the collateral (diversification, asset types, liquidity,
ratings, correlation with index tracked and swap counterparty)
(3) Estimate the likelihood and the magnitude of a collateral shortfall in a
given fund.
(4) Show how to build an optimal collateral portfolio for an ETF.
10
A Preview of the Results
Number of ETFs
AUM ($ Mio)
Total
Asset Exposure
Equities
Government Bonds
Money Markets
Commodities
Hedge Funds Strategies
Credits
Corporate Bonds
Currencies
Multi Assets
Geographic Exposure
Europe
World
Asia-Pacific
North America
Rest of the World
Total
164
Funded
112
Unfunded
52
37,927
20,122
17,805
74.5% (111)
11.0% (24)
6.6% (4)
3.8% (2)
2.2% (6)
0.7% (9)
0.6% (3)
0.3% (4)
0.3% (1)
85.5% (100)
2.2% (2)
7.2% (2)
3.9% (3)
0.6% (4)
0.6% (1)
61.9% (11)
20.8% (22)
14.0% (4)
0.3% (3)
1.6% (9)
1.4% (3)
-
58.9% (79)
22.3% (41)
9.4% (28)
7.2% (14)
2.2% (2)
29.4% (41)
37.0% (38)
17.6% (24)
11.9% (7)
4.1% (2)
92.4% (38)
5.5% (3)
0.2% (4)
1.9% (7)
11
Size of Collateral Portfolios
Collateral Value ($ Mio)
All
Number of Collateral Securities
All
Average Number of Collateral Securities
per Fund
All
Equities
Government Bonds
Money Markets
Commodities
Hedge Funds Strategies
Credits
Corporate Bonds
Currencies
Multi Assets
Collateralization
All
Equities
Government Bonds
Total
Funded Unfunded
40,940
23,080
17,860
3,299
3,014
1,141
81
109
14
20
93
37
10
12
50
70
110
117
13
93
66
50
70
18
35
16
20
9
10
12
-
108.4%
109.6%
102.8%
114.6%
115.7%
100.7%
101.3%
99.9%
103.1%
12
Types of Collateral Securities
Number of Collateral Securities
Asset Exposure
Geographic Exposure
Panel A: Type of Collateral Securities
All types
Equity
Gov. Bonds Corp. Bonds
3,299
2,591
490
218
All
Equity
Gov. Bonds
Corp. Bonds
Others
74.9%
92.5%
40.8%
19.7%
2.7%
96.5%
100.0%
48.8%
5.4%
4.8%
3.5%
10.4%
Panel B: Geographic Origin of the Collateral Securities
Europe Asia-Pacific N. America R. World
All
66.0%
17.5%
16.3%
0.2%
Europe
71.8%
13.9%
13.9%
0.4%
Asia-Pacific
56.0%
24.1%
19.8%
0.1%
N. America
58.3%
22.1%
19.5%
0.1%
Rest of the World
58.1%
25.5%
16.3%
0.1%
World
58.8%
21.7%
19.4%
0.1%
“home bias“
13
Bonds Used as Collateral
Bond issuer
North
America
Asia-Pacific
6.5%
11.2%
8.9%
21.3%
4.8%
3.5%
All
Gov. Bonds
Corporate Bonds
Europe
82.2%
69.8%
91.6%
Bond Type
All
Gov. Bonds
Corporate Bonds
AAA
46.1%
62.9%
34.8%
AA
22.1%
22.0%
21.9%
A
15.3%
14.4%
16.2%
BBB
4.6%
0.6%
7.3%
BB
2.9%
0.1%
4.9%
B
0.3%
0.5%
n/a
8.7%
14.4%
Bond Issuer
Europe
North America
Asia Pacific
50.5%
60.3%
12.6%
17.3%
0.2%
65.4%
17.9%
0.2%
5.3%
5.4%
1.6%
0.3%
1.8%
13.5%
3.8%
0.1%
3.3%
-
7.0%
20.9%
12.6%
Bond Type
Rest of the World
0.1%
0.1%
Rating
14
Turnover
Turnover
All
Equities
Government Bonds
Money Markets
Commodities
Hedge Funds Strategies
Credits
Corporate Bonds
Currencies
Multi Assets
Total
34.0%
43.9%
1.3%
2.2%
72.7%
29.7%
1.4%
2.4%
60.8%
76.7%
Funded
47.4%
46.5%
0.5%
72.7%
60.7%
60.8%
76.7%
Unfunded
5.0%
18.8%
1.4%
2.2%
0.3%
1.4%
2.4%
-
15
Collateral Shortfall
Probability of Collateral Shortfall (average)
1
0
0
Expected Collateral Shortfall (average)
16
More information at:
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