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OTC Derivatives Reforms: Considerations and Challenges ESRC Conference on Diversity in Macroeconomics Mark Manning, Reserve Bank of Australia. Overview Policy motivation An initial contribution Methodology Exposures and collateral demands - PowerPoint PPT Presentation
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OTC Derivatives Reforms: Considerations and Challenges
ESRC Conference on Diversity in MacroeconomicsMark Manning, Reserve Bank of Australia
Overview
• Policy motivation• An initial contribution
− Methodology− Exposures and collateral demands− Financial stability under different clearing
structures• Policy messages and future work
Policy Motivation
• Fundamental changes to core financial markets− G20 financial reform agenda− Strengthen risk management; reduce
interconnectedness• Collateralisation and central clearing
− Trade-off between counterparty risk and liquidity risk
− Encumbrance; funding and liquidity• Assess implications for stability, market functioning
and real economic outcomes
Initial Contribution
• OTC Derivatives: Netting and Networks− Joint work with Alex Heath and Gerard Kelly
• Simulation approach− Static: Exposures and collateral demands− Dynamic: Financial stability
• Flexible, but stylised• Can examine a variety of clearing structures• Stylised ‘world’: network structure; balance
sheets
Links to Literature
• Duffie and Zhu (2011)− Model dealer exposures in alternative clearing
settings; consider fragmentation and un-netting• Macroeconomic Assessment Group on Derivatives
(2013)− Examine costs/benefits of G20 reforms: net long-
run impact on GDP • Duffie (2014)
− Model collateral demand in alternative clearing settings using bilateral CDS exposure data
Basic set-up
• Two agent types: banks (b) and investors (i)• Core/Periphery network structure• Draw derivative positions from a transaction matrix
Static Analysis: Exposure and Collateral
• Bilateral clearing:
• Central clearing, single CCP:
• Central clearing, separate CCPs:
• Mixed clearing:
• Split clearing: and
Banks Investors CCPs System
Bilateral 0.62 0.31 - 0.93
Split 0.42 0.31 0.10 0.83
Separate CCPs 0.15 0.13 0.28 0.56
Single CCP 0.12 0.10 0.22 0.44
Total Exposures
Changing the Size of the CoreExposure relative to notional outstanding
2 4 6 8 10 120
1
2
3
4
5
0
1
2
3
4
5
Number of banks
Single CCP
%
Separate CCPs
Bilateral
Split clearing
%
Changing the Directionality of the PeripheryExposure relative to notional outstanding
0 6 12 18 24 300
1
2
3
4
5
0
1
2
3
4
5
Number of directional investors
Single CCP
%
Separate CCPs
Bilateral
%
Dynamic Analysis: Networks (1)
Previous model extended by giving agents balance sheets• Banks and investors hold a composite ‘illiquid
asset’• Can be sold/transformed into a ‘liquid asset’ to
meet collateral needs• Liabilities comprise debt and equity for banks and
equity only for investors− Both banks and investors can default due to
illiquidity; banks can also default due to insolvency
Dynamic Analysis: Networks (2)
Models the dynamic interaction between derivative exposure and other balance sheet items under alternative clearing arrangements• Focus is on how price shocks are transmitted to
balance sheets and how they may trigger liquidity shortages or defaults
• Examines also the dynamics of collateral transformation
Simulation and Timeline
Monte Carlo simulation with 70 000 iterations. Seven steps:• Populate transaction matrix• Draw illiquid asset price change• Draw derivative price change• Calculate variation margin payment obligations• Sell/transform illiquid assets to obtain liquidity for
variation margin payments; could trigger default • Default could impose losses on others• Update balance sheets
Bank Default and Collateral CoverageExpected number of defaults
Bilateral
50.00 69.15 84.13 93.32 97.72 99.38 99.87 99.985
6
7
8
9
10
5
6
7
8
9
10
Coverage level (%)
Mixed clearing
Single CCP
No No
Policy Messages
• The appropriate scope of central clearing and collateralisation will depend on product and agent characteristics
• There is likely to be an ‘optimal’ level of collateralisation, which will vary with the structure of clearing arrangements
Future Work
• Economic significance of the results− Take the model to ‘real’ data
• Add richness to banks’ and investors’ balance sheets
• Endogenise pricing and agents’ trading choices