systemic illiquidity in the russian interbank market alexei karas gleb lanine koen schoors
TRANSCRIPT
Systemic illiquidity in the Russian interbank market
Alexei KarasGleb Lanine
Koen Schoors
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
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
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
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
Alexei Karas, Gleb Lanine, Koen Schoors
Market participation
Alexei Karas, Gleb Lanine, Koen Schoors
Liquidity drains on the Russian interbank market
02
000
400
06
000
Tra
nsa
ctio
ns b
etw
een
Do
mes
tic B
anks
Jul 98 Jan 00 Jul 01 Jan 03 Jul 04
Number of Open PositionsRouble Volume of Open Positions (Aug-98 prices, tens of millions)
Alexei Karas, Gleb Lanine, Koen Schoors
The dominance of large banks II
Post 1998 crisis peak
Alexei Karas, Gleb Lanine, Koen Schoors
Persistency of interbank relationships
Alexei Karas, Gleb Lanine, Koen Schoors
Flight to quality in crisis time
02
04
06
08
01
00S
har
e in
Sys
tem
-wid
e C
laim
s, %
Jul 98 Jan 00 Jul 01 Jan 03 Jul 04
Top Lenders, Top Debtors Other Lenders, Other Debtors
Top Lenders, Other Debtors Other Lenders, Top Debtors
Note: total gross claims of the group stated first on the group stated second
Top lenders shift to large debtors in times of crisis
Alexei Karas, Gleb Lanine, Koen Schoors
Financial crises and bank healthCapital versus liquidity
51
01
52
02
53
0A
vera
ge
Ra
tio a
cro
ss B
ank
s, %
Jul 98 Jan 00 Jul 01 Jan 03 Jul 04
Capital to Assets Liquid Assets to Assets
1998 2004
Alexei Karas, Gleb Lanine, Koen Schoors
Traditional methodology
where yij = the gross exposure of bank i to bank j
and
ci = the capital of bank i.
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
Alexei Karas, Gleb Lanine, Koen Schoors
The liquidity channel
Consider the following dataset
where yij = the gross exposure of bank i to bank j
li = the net liquidity position of bank i
Alexei Karas, Gleb Lanine, Koen Schoors
The liquidity Channel
Define the net exposure on the interbank market NEi:
Then we can define the liquidity channel:
A bank fails if its net liquidity < net exposure Nei
– Only if it is linked to a bank that was affected: active banks scenario
– If there one bank attacked: panic scenario
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
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
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
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
Alexei Karas, Gleb Lanine, Koen Schoors
Contagion under different scenarios
02
04
06
08
0C
onta
giou
sly
Fa
iled
Ass
ets
, %
Jul 98 Jan 00 Jul 01 Jan 03 Jul 04
Passive banks Active banks Panic
Initial Shock: Idiosyncratic
1998 crisis
2004 crisis
Add
ed v
alue
liqui
dity
cha
nnel
Alexei Karas, Gleb Lanine, Koen Schoors
Contagion with alternative shocks
02
04
06
0C
onta
giou
sly
Fa
iled
Ass
ets
, %
Jul 98 Jan 00 Jul 01 Jan 03 Jul 04
Idiosyncratic shock Systemic shock
Scenario: Active Banks
1998 crisis2004 crisis
Add
ed v
alue
liqui
dity
cha
nnel
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
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”
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
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)
Alexei Karas, Gleb Lanine, Koen Schoors
Alexei Karas, Gleb Lanine, Koen Schoors
Alexei Karas, Gleb Lanine, Koen Schoors
Alexei Karas, Gleb Lanine, Koen Schoors
Alexei Karas, Gleb Lanine, Koen Schoors
Centrality as a measure of structure
Alexei Karas, Gleb Lanine, Koen Schoors
Individual centrality measures
Alexei Karas, Gleb Lanine, Koen Schoors
Market concentration and contagion1
23
45
Top
-40
Ba
nks
Ave
rage
, %
Jul 98 Jan 00 Jul 01 Jan 03 Jul 04
Valued Outdegree Valued Indegree
Non-valued Outdegree Non-valued Indegree
Over time large banks have more positions
But smaller ones
Alexei Karas, Gleb Lanine, Koen Schoors
Is systemic importance related to centrality?
Alexei Karas, Gleb Lanine, Koen Schoors
Next step III:Evaluating the CBR’s intervention
Method: use contagion risk simulations1. Analyze what would have happened without CBR
liquidity injections• Sberbank and VTB are part of CBR
2. Look how the CBR behaved in reality3. 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
Alexei Karas, Gleb Lanine, Koen Schoors
Evaluating CBR interventions
05
10
15
20
25
Con
tagi
ousl
y F
aile
d A
sse
ts, %
Jul 98 Jan 00 Jul 01 Jan 03 Jul 04
Absent CBR Real CBR Hypothetical CBR
Initial Shock: Idiosyncratic; Scenario: Active banks
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