1. Systemic illiquidity in the Russian interbank market Alexei Karas Gleb Lanine Koen Schoors

2. Alexei Karas, Gleb Lanine, Koen Schoors Background  Russia faced 3 severe interbank market crises –August 1995 Careless risk management Structural reliance on interbank market for financing assets –August 1998 Collapse of the GKO market Unhedged positions in currency forwards –May/June 2004 Mini-crisis on the interbank market  These interbank market crises are costly –Systemic instability –Trust of depositors is affected –CBR intervenes to solve the problem

3. Alexei Karas, Gleb Lanine, Koen Schoors Motivation  Bank supervision neglects interbank linkages on the interbank market –Although in its enforcement the CBR seems to protect money centre banks (See Claeys, Schoors, 2007)  We want to understand –How vulnerable the Russian interbank market is to contagion –Whether this is linked to the structure of the banking system –Whether the CBR’s past interventions have helped to stabilize the interbank market –Whether the CBR could improve the effectiveness of its interventions

4. Alexei Karas, Gleb Lanine, Koen Schoors Contributions  We consider several types of shocks –A shock to an individual bank default –Correlated bank defaults  We define a new transmission channel –Next to the traditional capital channel –We define an innovative liquidity channel  We show this new channel is relevant in reality  We link this to the interbank market structure (through centrality measures)  And use this to assess CBR interventions

5. Alexei Karas, Gleb Lanine, Koen Schoors The data  We have the bank balances and income statements from two sources –INTERFAX –Mobile  We have the bilateral interbank exposures –A matrix of more than 1000 x 1000 –Monthly data –For the period 1998-2004 –Covering two crises on the interbank market

6. Alexei Karas, Gleb Lanine, Koen Schoors Market participation

7. Alexei Karas, Gleb Lanine, Koen Schoors Liquidity drains on the Russian interbank market

8. Alexei Karas, Gleb Lanine, Koen Schoors The dominance of large banks II Post 1998 crisis peak

9. Alexei Karas, Gleb Lanine, Koen Schoors Persistency of interbank relationships

10. Alexei Karas, Gleb Lanine, Koen Schoors Flight to quality in crisis time Top lenders shift to large debtors in times of crisis

11. Alexei Karas, Gleb Lanine, Koen Schoors Financial crises and bank health Capital versus liquidity 1998 2004

12. Alexei Karas, Gleb Lanine, Koen Schoors Traditional methodology where y ij = the gross exposure of bank i to bank j and c i = the capital of bank i.

13. Alexei Karas, Gleb Lanine, Koen Schoors The capital channel (passive banks)  A bank fails if the funds lost because the failure of debtor banks exceed her capital

14. Alexei Karas, Gleb Lanine, Koen Schoors The liquidity channel  Consider the following dataset where y ij = the gross exposure of bank i to bank j l i = the net liquidity position of bank i

15. Alexei Karas, Gleb Lanine, Koen Schoors The liquidity Channel  Define the net exposure on the interbank market NE i :  Then we can define the liquidity channel:  A bank fails if its net liquidity < net exposure Ne i –Only if it is linked to a bank that was affected: active banks scenario –If there one bank attacked: panic scenario

16. Alexei Karas, Gleb Lanine, Koen Schoors The empirical literature  Empirical work on the capital channel –Sheldon and Maurer (1998), Swiss banking system. –Upper and Worms (2002), German banking system –Furfine (1999) Federal funds market –Michael (1998) London interbank markets. –Degryse and Nguyen (2006), Belgian interbank market  But often no bilateral data –Construct bilateral data from gross exposures –No link to balance sheet data  The other transmission channels are neglected

17. Alexei Karas, Gleb Lanine, Koen Schoors The simulations  We assume a loss given default of 100% –Anecdotal evidence suggests very low recovery rates  We create a initial shock that kills banks –An idiosyncratic bank shock (Kill each bank once) –Random correlated defaults (10000 simulations/ month)  Then calculate the further round effects taking into account both channels of contagion –Capital channel –Liquidity channel

18. Alexei Karas, Gleb Lanine, Koen Schoors Correlated defaults  Calculate individual unconditional bank failure probabilities using a probit model  Generate correlated defaults using CR+ –Input probability of default from logit model –Using Bernouilli distribution to draw banks  In each month 10000 simulations of correlated initial defaults as a shock

19. Alexei Karas, Gleb Lanine, Koen Schoors How do we report the results?  What –The share of lost banking assets –The number of failed banks  We calculated –The average (but who wants to know the average expected damage of an earthquake) –The worst case scenario (could be a quirk) –The Value at Risk (95%) –The expected shortfall (95%), which is the average of the 5% worst cases  Here we report only the expected shortfall

20. Alexei Karas, Gleb Lanine, Koen Schoors Contagion under different scenarios 1998 crisis 2004 crisis Added value liquidity channel

21. Alexei Karas, Gleb Lanine, Koen Schoors Contagion with alternative shocks 1998 crisis 2004 crisis Added value liquidity channel

22. Alexei Karas, Gleb Lanine, Koen Schoors Intermediate conclusion  The capital channel does not suffice to understand systemic crises in the interbank market –The 1998 crisis is somewhat predicted by it –The 2004 crisis is off the screen  The liquidity channel is empirically relevant to Russia (both active banks and panic scenarios) –The 1998 crisis is predicted –The 2004 is also clearly predicted –Interbank market crises may be not a domino effect –But rather something like a liquidity run  It may be important in general

23. Alexei Karas, Gleb Lanine, Koen Schoors Next step I: does contagion risk help to predict bank failure?  Take the active bank scenario –Rerun the simulations exogenously imposing the survival of a banks that failed contagiously. –Do this for all simulations and all contagiously failing banks –Compare for each bank the new losses to the losses of the initial simulation –Average over simulations  Result: partial contribution to contagion of a given bank at a given point in time  “systemic importance” or ”contagion risk”

24. Alexei Karas, Gleb Lanine, Koen Schoors Next step I: does contagion risk help to predict bank failure? Benchmark model with active banks Panic scenario contagion risk

25. Alexei Karas, Gleb Lanine, Koen Schoors Next step II: Is this related to interbank market structure?  Theoretical work by Allen and Gale (2000) –They model the capital channel –They find that a complete market structure can be proven to be the most stable one  There is some work related to our liquidity channel –Boissay (2006) has a model of financial contagion through trade credit –Illiquid firm may render their suppliers illiquid though they were fundamentally solvent  Empirical work –Degryse and Nguyen look at interbank market structure –Müller (2003) uses network theory (centrality measures)

26. Alexei Karas, Gleb Lanine, Koen Schoors

30. Centrality as a measure of structure

31. Alexei Karas, Gleb Lanine, Koen Schoors Individual centrality measures

32. Alexei Karas, Gleb Lanine, Koen Schoors Market concentration and contagion Over time large banks have more positions But smaller ones

33. Alexei Karas, Gleb Lanine, Koen Schoors Is systemic importance related to centrality?

34. Alexei Karas, Gleb Lanine, Koen Schoors Next step III: Evaluating the CBR’s intervention  Method: use contagion risk simulations 1.Analyze what would have happened without CBR liquidity injections Sberbank and VTB are part of CBR 2.Look how the CBR behaved in reality 3.Analyze the effectiveness of the behavior Could the systemic risk have been better contained by targeting different banks? Try to allocate the same quantity of liquidity and attain lower contagion risk  Conclusion: the CBR did relatively well in saving the crisis, but could do more in prevention

35. Alexei Karas, Gleb Lanine, Koen Schoors Evaluating CBR interventions

36. Alexei Karas, Gleb Lanine, Koen Schoors Concluding remarks  The Liquidity channel –is relevant to interbank systemic stability in Russia –though its theoretical effects poorly understood  Interbank market structure –helps to explain the stability of the interbank market –Helps to explain bank-specific contagion risk  The CBR –did not bad in solving the two last crises, –But may do more in terms of prevention through influencing the interbank market structure