a Financial Architecture with TooInterconnectedtoFail Institutions MICHAEL GOFMAN UWMADISON August 28 2014 Study efficiencystability tradeoff for different financial architectures Implication for the desired structure of the financial system ID: 596106
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Slide1
Efficiency and Stability of a Financial Architecture withToo-Interconnected-to-Fail Institutions
MICHAEL GOFMAN, UW-MADISON
August 28, 2014Slide2
Study efficiency-stability trade-off for different financial architectures.
Implication for the desired structure of the financial system
Implications for the costs and benefits of too-interconnected-to-fail banks and whether they are systemically importantImplications for understanding the relationship between contagion and diversification of banksComparative statics on a calibrated network by holding the density constant and decreasing heterogeneity across banks in the number of counterpartiesUse a model with endogenous exposures between banks to compute market efficiency before and after contagion
ObjectivesSlide3
Trading Model:
Mapping from endowments to equilibrium allocations for any possible network of trading relationships
The Proposed Framework
Financial Architecture
Unobservable:
Network of trades:
Density
Max in-degree
Max out-degree
Diameter
Size
Prices, profits, volume
Efficiency
Unobservable:
Observable:
Financial Architecture
Price-setting mechanism: bargaining, auctions.
Financial Architecture – Network of Trading Relationships
Distribution of endowment and valuations shocks
StabilitySlide4
Illustration of the Model
1
Initial allocation:E(1)=1
V(1)=0.3
V(2)=0
Private
value: V(5
)=
0.6
V(4)=1
Feasible
first-best
allocation
V(3)=0
2
3
4
5
Valuation: P(5
)=
0.6
P(1)=
0.525
P(2)=
0.5625
P(3)=
0.75
P(4)=1
Welfare loss =
1-0.6=0.4
Surplus loss =welfare loss/first-best surplus = 0.4/(1-0.3)=0.57
Slide5
Model Fit: Visualization
Equilibrium daily network of trades in the model. Only one third of all trading relationships are equilibrium trades.
Network of trades in the Fed funds market on September 29, 2006 Source: Bech
and
Atalay
(2010)
Model
DataSlide6
Equilibrium Network of Trades: Model vs. Data
* Data Source: “The
Topology of the Federal Funds Market” Bech and Atalay , Physica A, 20103 parameters to match 5 moments using SMM, 5 std. dev. (not targeted) also match well. Slide7
Efficiency Before and After Contagion
Failure of the most interconnected bank triggers failure of counterparties with exposure above 15%.
Exposure of bank A to bank B = loans from A to B / all loans by A.Slide8
Average Cascade Size from Failure of the Most Interconnected Banks
Between 30% to 55% of banks fail due to endogenous contagion.
The number of bank failures is non-monotonic.Slide9
Comparative Statics with Six BanksSlide10
Contagion Scenario with Cumulative Losses (Preliminary)
Cascade
is triggered by failure of the most interconnected bankA bank fails if exposure to all banks failed in the past is above 15%. Maximum Number of Counterparties
Number of failed banksSlide11
Efficiency is as important as stability but it is frequently omitted in policy discussions and is rarely quantified.
Bridging the gap between theory and empirics is important for financial regulation. To compute efficiency we need to use some trading model, the calculation is more reliable if the model can also match the data.
Using a trading model to compute endogenous exposures between banks is important for studying contagion risk.To understand the costs and benefits if too-interconnected-to-fail banks the comparative statics should be with respect to the variance of the degree distribution, holding the mean of the distribution constant.
Final RemarksSlide12
Cumulative contagion: a bank fails if exposure to all banks failed in the past is above a threshold.
Add counterparty risk to the trading model.
In addition to the dynamical allocation in the network of trading relationships, allow for non-iid shocks and study trading when traders anticipate they will receive position/negative shocks in the future. Might improve the fit of the model even further.Strategic network formation to narrow down what counterfactual network would form under regulation that puts constrains on banks.
Model Limitations and Future Work