network models for systemic risk monitoringnetwork models have been used to study financial...
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Network Models for Systemic Risk
Monitoring
May 2010.
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I. Motivation
a) Relevant concepts
b) Related Literature
II. The network model for systemic risk
a) Conceptual model
b) Simulation model
III. Some results
IV. Conclusions
Content
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I. Motivation
a) Relevant concepts
b) Related Literature
II. The network model for systemic risk
a) Conceptual model
b) Simulation model
III. Some results
IV.Conclusions
Content
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Definition for Systemic Risk
1. Systemic Risk is the risk of experiencing an event
that threatens the well functioning of the system of
interest (payments, banking, financial)
2. Systemic risk consists of two main components
(Rochet 2009, Marquez & Martinez-Jaramillo 2009):
a) An initial (macroeconomic) shock, and
b) A contagion mechanism.
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1. Financial contagion has been used interchangeably with
systemic risk, something that is not fully accurate.
2. However, as it was shown in the definition, contagion is
just one of the components of a systemic event (a very
important one though).
3. Moreover, the relevance of the (macro)economic
environment is crucial.
Financial Contagion and Systemic Risk
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Since the influential paper by Allen and Gale (1998),
network models have been used to study financial
contagion.
Network models are very appealing to study financial
contagion and systemic risk for the following reasons:
• They are very intuitive,
• There is a vast amount of knowledge and analytical
tools in this area, and
• There are many practical tools, software and
interfaces available.
Network models, financial contagion and
systemic risk
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Capital Adequacy Indexes arising from a
worst chain of Contagion occurring daily (Number of banks)
Assets of banks whose capital would be
affected in the event of a worst chain of
contagion occurring daily(Percentage of total banking assets)
0
2
4
6
8
10
12
J2007
A J O F2008
M A D M2009
J S J2010
A
41
Number of Banks with CAR > 8%
Number of Banks with 4% < CAR < 8%
Number of Banks with CAR < 4%
0
2
4
6
8
10
12
14
J2007
A J O F2008
M A D M2009
J S J2010
A
Lower than 4% Lower than 8%
Source: Banco de México. Source: Banco de México.
I. Interconnectedness: Contagion Risk
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I. Motivation
a) Relevant concepts
b) Related Literature
II. The network model for systemic risk
a) Conceptual model
b) Simulation model
III. Some results
IV. Conclusions
Content
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Financial Contagion.
1. Direct contagion in banking systems through the
interbank market has been widely studied by central
banks in several countries, Upper(2007).
• Maximum entropy assumption.
• Individual idiosyncratic failures.
2. More recently contagion and systemic risk have
been studied recurring to Network Theory, Muller
(2006), Nier et al. (2006), Babus (2007), Mistrulli
(2007), Markose et al. (2009).
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Systemic Risk
1. Goodhart et al. (2006) propose a general equilibrium
model which includes heterogeneous agents,
endogenous defaults and credit and deposit markets.
2. Segoviano and Goodhart (2009) infer the multivariate
density, which they use to derive relevant measures of
distress for individual banks, groups of banks and the
distress on the system due to an individual bank.
3. Boss et al. (2006) use a simulation model which they use
to estimate the distribution of losses for the system as a
whole.
4. Aikman et al. (2009) put in place a complex simulation
model to study financial stability.
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I. Motivation
a) Relevant concepts
b) Related Literature
II. The network model for systemic risk
a) Conceptual model
b) Simulation model
III. Some results
IV.Conclusions
Content
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The conceptual model
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I. Motivation
a) Relevant concepts
b) Related Literature
II. The network model for systemic risk
a) Conceptual model
b) Simulation model
III. Some results
IV. Conclusions
Content
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The simulation model
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The data used to obtain the systemic distribution of losses
for the Mexican banking system consists of:
1. The daily interbank exposures,
2. The macro economic information used to build the macro
models (GDP, interest rates, stock indexes, etc),
3. The market portfolio,
4. Credit delinquency ratio as a proxy for the evolution of credit
losses, and
5. The Tier 1 capital.
Data
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Completeness index Daily Average Degree
United
Kingdom
15%
United States
53%
Source: Banco de México. Source: Banco de México
The Interbank Market Network
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The interbank market
Source: Banco de México
Interbank market
January 27th 2008
Source: Banco de México
Interbank market
Largest exposures
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Over-exposed banks
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Interbank market
January 27th 2008Interbank market
After an initial shock
Over-exposed banks: 17 Over-exposed banks: 19
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Preference index
19
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International exposures
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Previous versions of this work, Marquez Martinez-
Jaramillo (2009), computed the joint distribution of losses
from market and credit operations, and this distribution
was used to generate ``losses draws'' and to determine
whether those losses trigger a contagion process.
Despite the advantages of this method, behind each
shock was the idea that ``something happened'' but there
was few to say about what that ``something'' was.
Hence, to gain in the interpretation and to ease the stress
testing procedure one of the aims is having scenarios with
an economic interpretation.
Link to the economic variables I.
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A measure of financial fragility: An example
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To generate these scenarios linked to real economic
variables within a consistent framework, a simple
structural VAR was estimated:
Link to the economic variables II.
National Variables External
VariablesCredit
IGAE (GDP proxy)
Treasury Bills rate
Commercial credit
delinquency ratio
Cetes rate
Libor rate
Consumption credit
delinquency ratio
INPC (Consumer Price
Index)Dow Jones Index
Mortgate delinquency
ratio
FX (peso-dollar) Bovespa stock index
IPC (stock index)
No. Insured workers at
IMSS (unemployment
proxy)
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I. Motivation
a) Relevant concepts
b) Related Literature
II. The network model for systemic risk
a) Conceptual model
b) Simulation model
III. Some results
IV. Conclusions
Content
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Normal Scenarios
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Distribution of losses
Source: Banco de México
Market distribution of losses
Source: Banco de México
Credit distribution of losses
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Definition of CoVaR
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CoVaR
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Distribution of losses for the system
and conditional distribution given that
the big banks’ losses are at their VaR
level.
Distribution of losses for the system
and conditional distribution given that
the medium size banks’ losses are at
their VaR level.
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Systemic events
• Contagion did not happen under the previous 20k
simulations.
• Contagion did happen under Montecarlo simulation
(5m).
• Systemic events are located on the tail.
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Stress scenarios
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Joint tail distribution
Source: Banco de México
Joint distribution of losses
Normal scenarios
Source: Banco de México
Joint distribution of losses
Including stress scenarios
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Contagion under stress
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I. Motivation
a) Relevant concepts
b) Related Literature
II. The network model for systemic risk
a) Conceptual model
b) Simulation model
III. Some results
IV. Conclusions
Content
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The literature adhered to the belief that the topology of the
network was enough to characterize the systemic riskiness
of a particular financial system.
The relevance of the initial macroeconomic shock should not
be disregarded.
Finally, to concentrate on size and interconnectedness
(alone) to determine the systemic importance of institutions
could be misleading.
There are another aspects which are very important as well.
For example: the size of the losses, the relationship between
the capacity of a bank to absorb losses and its exposure on
the interbank market.
Conclusions
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Network Models for Systemic Risk
Monitoring
May 2010.