Discussion

1. Discussion Tomohiro Ota Bank of England * The views expressed in this presentation are mine and not necessarily those of the Bank of England.

2. Mapping the network studies Marginal contagion model: shocks propagates through solvent banks. E.g. Battiston’s et.al. (2012) DebtRank and Ota’s (2013) Chained Merton model

3. 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)

4. 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

5. 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

6. A question from a central bank economist: Advantages and disadvantages of the methods?

7. 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) • … but where is the collateral coming from? UK interbank bilateral exposures by instrument (net of collateral) Langfield, Liu and Ota (2014)

8. 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

9. 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

10. 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

11. 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?

13. Discussions ofDenbee, Julliard, Li and Yuan “Network risk and key players: A structural analysis of interbank liquidity” Tomohiro Ota Bank of England

14. 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

15. 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

16. 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

17. Discussions ofGiansante and Markose “Multi-Agent Financial Network models for systemic risk monitoring and design of Pigou tax for SIFIs” Tomohiro Ota Bank of England

19. 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): variance decomposition • Billio et. al. (2013): Granger causality • Perasan et.al. (2006): GVAR

20. Questions and comments • Does this measure the unobservable inter-dependence between Fis (and sovereigns)? • Is it intuitive that network became dense after the crisis? • Is MCS-GVAR better than the other ways?