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

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


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


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


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


Market participation


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)


The dominance of large banks II

Post 1998 crisis peak


Persistency of interbank relationships


Flight to quality in crisis time

02

04

06

08

01

00S

har

e in

Sys

tem

-wid

e C

laim

s, %


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


Financial crises and bank healthCapital versus liquidity

51

01

52

02

53

0A

vera

ge

Ra

tio a

cro

ss B

ank

s, %


Capital to Assets Liquid Assets to Assets

1998 2004


Traditional methodology

where yij = the gross exposure of bank i to bank j

and

ci = the capital of bank i.


The capital channel (passive banks)

A bank fails if the funds lost because the failure of debtor banks exceed her capital


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


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


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


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


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


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


Contagion under different scenarios

02

04

06

08

0C

onta

giou

sly

Fa

iled

Ass

ets

, %


Passive banks Active banks Panic

Initial Shock: Idiosyncratic

1998 crisis

2004 crisis

Add

ed v

alue

liqui

dity

cha

nnel


Contagion with alternative shocks

02

04

06

0C

onta

giou

sly

Fa

iled

Ass

ets

, %


Idiosyncratic shock Systemic shock

Scenario: Active Banks

1998 crisis2004 crisis

Add

ed v

alue

liqui

dity

cha

nnel


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


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”


Next step I: does contagion risk help to predict bank

failure?Benchmark model with active banks

Panic scenario contagion risk


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)


Centrality as a measure of structure


Individual centrality measures


Market concentration and contagion1

23

45

Top

-40

Ba

nks

Ave

rage

, %


Valued Outdegree Valued Indegree

Non-valued Outdegree Non-valued Indegree

Over time large banks have more positions

But smaller ones


Is systemic importance related to centrality?


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


Evaluating CBR interventions

05

10

15

20

25

Con

tagi

ousl

y F

aile

d A

sse

ts, %


Absent CBR Real CBR Hypothetical CBR

Initial Shock: Idiosyncratic; Scenario: Active banks


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

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

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