ESRB Annual Conference - van den Burg Prize - D'Errico and ... · ESRB Annual Conference - van den...
Transcript of ESRB Annual Conference - van den Burg Prize - D'Errico and ... · ESRB Annual Conference - van den...
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Compressing over-the-counter markets
Marco D’Errico1 Tarik Roukny2
1University of Zurichmarco.derrico @ uzh.ch
2Massachusetts Institute of Technologyroukny @ mit.edu
Second ESRB Annual ConferenceSeptember 21-22, 2017
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Outline
1. What is compression? The main intuition
2. Historical background3. Theory:
▸ Key concepts: excess, conservative vs non-conservativecompression, tolerances
▸ Mechanics: conditions, efficiency, characterisation of networkstructure
4. Empirics:▸ Impact of a EU-wide adoption of compression(via EMIR data, CDS)
▸ How much notional can be eliminated?
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What is compression?
▸ OTC derivatives markets: complex, opaque, large notionalamounts
▸ Compression in a nutshell: post-trade operation thatreduces gross positions while preserving net positions
A
B
C D5
10
20
10
Gross Net
vgA = 25 vn
A = −15vgB = 15 vn
B = +5vgC = 40 vn
C = +20vgD = 10 vn
D = −10
V g = 45 V n = 0
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What is compression?
Compression in a nutshell: post-trade operation that reducesgross positions while preserving net positions
A
B
C D
5
15
10
Gross Net
vgA = 15 vn
A = −15vgB = 5 vn
B = +5vgC = 30 vn
C = +20vgD = 10 vn
D = −10
V g = 30 V n = 0
Gross notional reduction: 15
System-wide deleveragingwhich does not entail asset sales or additional capital
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“Size” of OTC derivatives markets
Credit Default Swaps (source: BIS OTC derivatives statistics)“significant decline” due to compression (BIS QR) - 85 T eliminated through2012 (ISDA)
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● total singlename index
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How did we get there? Part 1
OTC derivatives marketsCustomers
SellingCustomers
BuyingDealers
Complex Opaque Large↓
Global Reform of Financial Markets(G-20 Post-Crisis Summit, 2009)
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How did we get there? Part 2
Compression introduced in early 2000s▸ Good housekeeping
▸ Counterparty risk ↓▸ Operational management ↓
2008 GFC aftermath (1)
“Only now is the industry discovering the joys of compression”The Economist, November 2008
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How did we get there? Part 2
Compression introduced in early 2000s▸ Good housekeeping
▸ Counterparty risk ↓▸ Operational management ↓
Crisis aftermath (2)
New regulatory constraints↙ ↓ ↘
Capital requirements Leverage ratio Margins / collateral
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How did we get there? Part 2
Compression introduced in early 2000s▸ Good housekeeping
▸ Counterparty risk ↓▸ Operational management ↓
Crisis aftermath (3)
Regulatory assessment↓
Compression is“greatest source of improvement in OTC derivatives exposure”
(Duffie, 2017)
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Compression today
How?▸ Bilateral level → Mutual agreement▸ Multilateral level → External service provider
(TriOptima, LCH SwapClear, LMRKTS, Catalyst, Markit)
What?▸ IRS (cleared and non-cleared), CDS (single-name and index)
▸ More recently: FX, Commodity, Inflation, Currency, etc.Numbers▸ TriOptima: $1000 trillion eliminated (cumulative,2003-2017)
▸ LCH SwapClear: $380 billions eliminated in 2016▸ ISDA: 67% reduction of IRD markets (2010-2016)
Regulations▸ Defined in MiFIR▸ EMIR art. 14 requires “valid explanation” for not compressing
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Why care?
Global Regulatory SupportMiFIR, EMIR, Dodd-Frank
▸ Reduction of Systemic Risk + Increase of Transparency
However...
Systemic Risk(partial) reconfiguration
▸ Local vs. global▸ Risk concentration▸ Legal framework
Monitoring/Regulationlack of tractability
▸ Opaque methods▸ Limitations in currentreporting framework
▸ Aggregate measurementsmay be distorted
... and
Limited literature and analytical research on the topic(O’Kane, 2014 WP), (Benos et al., 2013, BoE WP), (Schrimpf, 2015 BIS QR), (Duffie 2017, MS)
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Mapping OTC Markets
Dealers and CustomersEMIR CDS on large sovereign (April 2016)
Total gross notional: 15.95Bn euros
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Mapping OTC Markets
Dealers and CustomersTotal gross notional: 15.95Bn euros
1.6 % 96.6 % 1.9 %
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Mapping OTC Markets
Dealers and Customers
1.6 % 96.6 % 1.9 %
1 0.21 (0.18) 1
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Excess
i j keij ejk
excess notional = ∑i ,jeij
²gross notional
− (∑i ∣∑j eij −∑j eji ∣)2´¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¸¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¶
minimum notional
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Excess
excess notional = ∑i ,jeij
²gross notional
− (∑i ∣∑j eij −∑j eji ∣)2´¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¸¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¶
minimum notional
▸ In a market (trades are fungible and outstanding), there isexcess if and only if there is intermediation
i j keij ejk
i
j
k
eij ejk
eki
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Compression
Definition: operation over the market that reconfigures the webof outstanding trades s.t. the resulting market:
o Preserves net positions → unchanged market risko Reduces excess → reduction of counterparty risko Satisfies pre-determined tolerance levels (bilateralcounterparty constraints set by participants)
↓Efficiency criteriaExcess Reduction
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Classification
Two benchmark classes of compression based on tolerances
ConservativeWith counterparty relationship
constraints
Non-conservativeNo counterparty relationship
constraints
Necessary and sufficient condition (Feasibility)
Efficiency
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Results: trade-off in netting efficiency
Conservation (counterparty relationships)Conservative
Link-constrainedNon-conservativeNo link constraints
Feasibility
Closed intermediation chains Presence of intermediaries
Efficiency
Never fully efficientExcess > 0
Fully efficientExcess = 0
Network structure
No closed intermediation chains(preserves intermediation)
No more intermediation(buyers matched with sellers)
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Illustration: conservative vs non-conservative
A
B
C
D
5
10
20
10
3
Excess = 26
Conservative
A
B
C
D2
15
7
Excess = 2
Non-conservative
A
B
C
D
15
5
2
Excess = 0
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A third approach: hybrid compression
Hybrid Compression
Assumptions1. Dealers want to keep their intermediation role with customers
2. Intra-dealer trades can be switched at negligible cost
Implementation▸ EC is the set of dealer-customer trades → conservative▸ ED is the set of intra-dealer trades → non-conservative▸ EC + ED = E
Efficiency ranking
bilateral ≤ conservative ≤ hybrid ≤ non-conservative
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Application
DataTrade state report under EMIR: EU-wide Credit Default Swaps(see ESRB OP 11)▸ Oct 2014 - Apr 2016▸ 100 most traded instruments (ref. entity + maturity) ≈ 70Bn euros
Implementation▸ Design benchmark solution for each approach
o Non-conservativeo Conservativeo Hybrido Bilateral
Analysis
▸ Excess▸ Compression efficiency
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ResultsTop 100 markets
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ResultsTop 100 markets
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Conclusion
▸ Networked markets with fungible trades generate excessnotional obligations when there is intermediation
▸ Excess can be removed by compression▸ Compression is widely used in OTC derivatives markets▸ Theoretical understanding of the mechanics
o Tolerances, feasibility, efficiency trade-off, design▸ Empirical application
o Large levels of excess, concentration in the intra-dealersegment, efficiency of multilateral approaches despitetrade-off
↓
Towards an understanding of the systemic implications ofcompression
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Ongoing research
1. Macro-prudential & crisis management tool▸ e.g.: “compressing” in a Lehman-type event▸ impact on margins and procyclicality
2. Epistemology of derivatives market size and impact onunderlying
3. Liquidity improvement/distortion
4. CCP and netting efficiency
5. Impact on capital, collateral and prices
6. Legal framework
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Thank you!
marco.derrico @ uzh.chroukny @ mit.edu