Discussion
Tomohiro Ota
Bank of England
* The views expressed in this presentation are mine and not necessarily those of the Bank of England.
Mapping the network studies
Network Data
Contagion mechanism
Metrics
Gross and Kok
Implied exposure of
banks and sovs
(estimated from
CDS premia)
Estimated from dynamic Spill-over potential
interaction of banks’ CDS (average impulse
premia using MCS-GVAR response)
Denbee,
Julliard, Li and
Yuan
O/N interbank loans Strategic interaction of
(estimated from
banks’ liquidity holding
payment data)
(between linked banks)
Indegree/outdegree
Katz-Bonacich
centrality
Giansante and
Markose
RBI’s bilateral
Loss of capital
exposure data of FIs propagates through
network (marginal
contagion)
Eigen-pair method
(right/left
Eigenvector)
Marginal contagion model: shocks propagates through solvent banks. E.g. Battiston’s
et.al. (2012) DebtRank and Ota’s (2013) Chained Merton model
Gross and Kok: Main findings
• Banks’ and sovereigns’ interconnectedness is estimated
by dynamic interaction of CDS premia (with MCS-GVAR)
• The impulse response matrix provides the size of
amplification (and key players, I guess?)
• Implied networks became more dense during the crisis
• The spill-over effect from banks to sovereigns was strong
in 2008, but the direction went opposite in 2012
• The spill-over effect is regionally heterogeneous (South
EA countries are linked tighter)
Yuan et.al.: Main findings
• Studying the strategic behaviour of liquidity holding (as
NE, based on Ballester et.al. 2006)
• Katz-Bonacich centrality, characterising the NE, identifies
key players in the market
• Testing the theory model to estimate indeterminate
parameters and an ‘amplification’ factor of agg. liquidity
• Before LB crisis, the banks’ liq holding showed strategic
complementarity
• After the introduction of QE, a bank’ liquidity holding
reduces the counterparties’ liquidity holdings
Giansante and Markose: Main findings
• Modelling higher-order ‘Infections’ of loss of capital
through a given network structure
• The maximum Eigenvalue of ‘relative exposure’ matrix
gives the size of amplification potential
• The column-sum and row-sum of corresponding
Eigenvector identifies key ‘offenders’ and ‘victims’
• The Pigouvian tax using Eigenvector centrality reduces
Maximum Eigenvalue (i.e. amplification) by internalising
the negative externality of the infection
A question from a central bank economist:
Advantages and disadvantages of the methods?
Network Data
Contagion mechanism
Metrics
Gross and Kok
Implied exposure of
banks and sovs
(estimated from
CDS premia)
Estimated from dynamic Spill-over potential
interaction of banks’ CDS (average impulse
premia using MCS-GVAR response)
Denbee,
Julliard, Li and
Yuan
O/N interbank loans Strategic interaction of
(estimated from
banks’ liquidity holding
payment data)
(between linked banks)
Indegree/outdegree
Katz-Bonacich
centrality
Giansante and
Markose
RBI’s bilateral
Loss of capital
exposure data of FIs propagates through
network (marginal
contagion)
Eigen-pair method
(right/left
Eigenvector)
A question from a central bank economist:
how should we model systemic liquidity risk?
• Credit exposures have been (will be) collateralised
significantly (e.g. collateralisation of OTC derivatives)
UK interbank bilateral
exposures by instrument
(net of collateral)
Langfield, Liu and Ota (2014)
• … but where is the collateral coming from?
A question from a central bank economist:
how should we model systemic liquidity risk?
• Modelling liquidity contagion would be more
complicated than credit risk contagion
– Credit risk is defined on balance sheets
– Liquidity risk is more behavioural
– Higher degree of freedom in liquidity management models
A question from a central bank economist:
how should we model systemic liquidity risk?
• Theoretical approaches
– Considering actions on a network
– Endogenous network formation is an essential
part of systemic liquidity risk
– No workhorse model (yet)
– Could be too complicated to solve
A question from a central bank economist:
how should we model systemic liquidity risk?
• Price-based approaches
– Can skip the complicated propagation channels
and focus on the results
– We don’t have decent network time series data
– Do we have market-traded prices indicating
liquidity stress of a bank / firm?
– We need to be aware of what we are estimating
A question from a central bank economist:
how should we model systemic liquidity risk?
• Agent-based modelling approach
– Powerful tool to model the complex nature of
liquidity risk
– How many assumptions do we need?
– Potential risk of being a ‘black box’
• ABM for simulating real life
• ABM to understand structure
– Do we need consensus of assumptions?
– Can we define social optimum and/or 1st best?
Discussions of
Denbee, Julliard, Li and Yuan “Network
risk and key players: A structural analysis
of interbank liquidity”
Tomohiro Ota
Bank of England
Summary
• Studying the strategic behaviour of liquidity
holding (as NE, based on Ballester et.al. 2006)
• Seeing the impact of an idiosyncratic shock to
aggregate liquidity shock
• Social optimum can be obtained
• Proposing systemic risk metrics consistent
with the theory
• Testing the model empirically
Findings
• A bank holds more liquidity when the counterparties
increase their liq holding if:
– Holding liquid assets promises borrowing from connected
counterparties
• Katz-Bonacich centrality, characterising the NE,
identifies key players in the market
• Before LB crisis, the banks’ liq holding showed strategic
complementarity
• During the crisis, the network density decreased and
banks are less dependant
• After the introduction of QE, a bank’ liquidity holding
reduces the counterparties’ liquidity holdings
Questions and comments
• Does a larger liquid asset holding increase
borrowing capability?
– Is leverage stack applicable to consider the issue
• How to define liquidity for empirical tests
– Reserve plus ‘collateral holding’
• Reserves are ‘on average’ affected by monetary policy
– The collateral holding may not be a strategic choice
– Hidden links between the banks and BOE
– Tiering: some trades are not for the banks
Discussions of
Giansante and Markose “Multi-Agent
Financial Network models for systemic risk
monitoring and design of Pigou tax for SIFIs”
Tomohiro Ota
Bank of England
Two main approaches to financial contagion
• Assume specific transmission channels of
financial stress to test the size of systemic risk
(for a given network structure)
• Estimating transmission channels from market
data
– Diebold and Yilmaz (2011):
– Billio et. al. (2013):
– Perasan et.al. (2006):
variance decomposition
Granger causality
GVAR
Questions and comments
• Does this measure the unobservable interdependence between Fis (and sovereigns)?
• Is it intuitive that network became dense after
the crisis?
• Is MCS-GVAR better than the other ways?