On Systemic Risk Contribution and
Macro-financial linkages
By
Marco A. Espinosa-Vega
IMF
Prepared for the Conference on
Financial Stability and Macroprudencial Policy
Central Bank of Portugal
Lisbon, Portugal
February 2015
The views expressed in this presentation are those of the author and do not necessarily represent those of the IMF or IMF policy.
1
Identifying SIFIs has become a priority
Calibration of financial institutions’ systemic
contribution and (macroprudential) policies to
mitigate systemic risk … a policy reality
•The Turner Report
•The Dodd-Frank bill
•The work of the BIS …
An implosion of technical analysis
An Implosion of Technical Work
• IMF work includes
– Systemic risk monitoring toolkit: a user guide
(SysMo, 2013)
– Addressing interconnectedness (2013)
– Options to deal with credit booms (2011)
– Options to deal with real estate booms (2011)
3
An Implosion of Technical Work
• A Survey of Systemic Risk Analytics (Bisias,
Flood, Lo, Valavanis (2012)
4
A Taxonomy
– Time dimension
– Structural dimension
•
•
•
•
•
Estimates of co-dependence of financial institutions
in the tails of equity returns or CDs: Tobias and
Brunnermeier (2008)
Marginal expected shortfall and systemic risk:
Acharya et al. (2010)
Contingent claims analysis: Gray and Jobst (2011)
Distress dependence: Segoviano (2011)
Network Analysis
5
Network Analysis: from Espinosa-Vega & Solé
We start from the following stylized balance sheet identity:
ki
∑ x ji
j
di
ai
bi
∑ xij
j
Where
x stands for interbank lending,
a : bank i’s other assets,
k : bank i’s capital,
b : other borrowing, and
d : deposits
Interbank Exposures
7
Network Analysis: Tracking Shocks
from Espinosa-Vega & Solé
We consider two shocks: (i) a credit shock
Pre-Shock Balance Sheet
ki
∑ x ji
j
di
ai
bi
∑ xij
j
Post-Shock Balance Sheet
Network Analysis: Tracking Shocks
from Espinosa-Vega & Solé
(ii) and a funding shock
δρxih
ki
∑ x ji
∑ x ji
j
ki
j
di
ai
di
ai ai
bi
bi
∑ xij
∑ j =h xij
ρxih
j
(1 + δ ) ρxih
Network Analysis: Algorithms
Parameters:
λ is loss-given default,
δ is asset price haircut,
ρ fraction of s/t funding not rolled-over
10
Network Analysis: Tracking shocks
from Espinosa-Vega & Solé
Institution
i fails
Assess the
impact of
parameter
assumption
including on
LGD & haircut
for each
institution: is
capital ≤0 ?
Institution j fails
Institution k
Institution m
Institution n fails
Compile list of
all failed
institutions up
to this point and
re-start
algorithm for
remaining (nonfailed)
institutions
Network Analysis: Main Findings
1. Provides clean metric on the domino effect of capital losses and failures induced by
alternative credit events
2. Compounding shocks reveals additional systemic countries/institutions
Number of Induced Failures
16
14
12
10
8
6
4
2
0
France
Germany
Credit Channel
Netherlands
United Kingdom
Credit and Funding Channel
United States
Network Analysis: Main Findings
3. It is possible to quantify amount of potential capital losses at institutional level
(stress-tests’ second round effects).
Post Simulation Capital Impairment
Australia Austria Belgium Canada France Germany Ireland Italy Japan Nether. Portugal Spain Sweden Switzer. U.K.
Trigger
Country:
Australia
Austria
Belgium
Canada
France
Germany
Ireland
Italy
Japan
Nether.
Portugal
Spain
Sweden
Switzer.
U.K.
U.S.
U.S.
(capital impairment in percent of pre-shock capital)
-1.9
-1.8
-36.2
0.0
-61.7
-95.9
-2.6
-0.5
-18.7
-36.2
-0.2
-1.3
-1.4
-10.0
-------------
-18.1
-1.6
-32.3
-------5.3
-49.3
-2.0
-18.1
-1.9
-6.6
-1.7
-10.9
-------------
-7.3
-8.1
-6.7
-------------90.3
-48.7
-12.1
-------8.7
-46.3
-4.2
-19.3
-------------
0.0
-0.2
-3.3
-4.4
-8.5
-0.5
-0.1
-2.5
-3.3
0.0
-0.1
-0.2
-1.1
-20.4
-79.1
-7.0
-3.8
-46.8
-3.8
-------13.1
-70.1
-36.9
-46.8
-4.6
-29.6
-2.7
-10.8
-------------
-7.2
-17.5
-41.7
-5.9
-82.6
-30.9
-46.1
-16.5
-41.7
-6.6
-38.2
-8.9
-13.0
-------------
-4.6
-4.8
-46.0
-7.1
-79.0
-------43.5
-21.9
-46.0
-5.8
-23.0
-4.7
-13.1
-------------
-0.2
-25.7
-13.4
-0.4
-49.3
-------12.6
-2.9
-13.4
-1.2
-8.0
-0.5
-2.8
-------------
-2.9
-0.4
-4.8
-2.5
-14.5
-33.0
-2.2
-2.6
-4.8
-0.2
-1.4
-0.7
-3.5
-49.5
-97.5
-39.9
-7.9
-------22.8
-------------24.1
-42.0
-24.7
-8.2
-73.5
-6.0
-18.9
-------------
-0.7
-1.9
-22.7
-0.3
-44.0
-99.3
-15.5
-6.6
-1.3
-22.7
-68.8
-1.3
-3.8
-------------
-0.7
-1.1
-18.0
-0.5
-38.6
-83.7
-5.0
-11.0
-1.8
-18.0
-16.6
-0.9
-1.9
-------------
-4.4
-3.5
-26.0
-3.5
-47.7
-------11.7
-6.7
-7.6
-26.0
-1.3
-11.6
-12.6
-------------
-9.8
-6.9
-33.3
-11.6
-76.3
-------11.7
-9.2
-59.5
-33.3
-1.5
-7.7
-5.1
-------------
-8.3
-1.2
-16.1
-5.6
-37.5
-86.0
-15.3
-5.4
-9.9
-16.1
-1.3
-14.1
-1.6
-7.0
-------
-1.7
-0.4
-7.3
-6.4
-17.2
-40.0
-2.4
-1.2
-12.2
-7.3
-0.1
-2.4
-0.7
-9.0
-64.8
Network Analysis: Main Findings
6. Allows to track potential contagion paths.
Japan: Contagion Paths
December 2008
Trigger
Country
Affected
Countries
Contribution to Final
Capital Impairment
Japan's Total Capital
Impairment
(percent of initial capital)
(percent of initial capital)
Time Series
Contagion/Vulnerability Index
Contagion/Vulnerability Index
from Espinosa-Vega & Solé
Post Simulation Capital Impairment
Australia Austria Belgium Canada France Germany Ireland Italy Japan Nether. Portugal Spain Sweden Switzer. U.K.
Trigger
Country:
Australia
Austria
Belgium
Canada
France
Germany
Ireland
Italy
Japan
Nether.
Portugal
Spain
Sweden
Switzer.
U.K.
U.S.
U.S.
(capital impairment in percent of pre-shock capital)
-1.9
-1.8
-36.2
0.0
-61.7
-95.9
-2.6
-0.5
-18.7
-36.2
-0.2
-1.3
-1.4
-10.0
-------------
-18.1
-1.6
-32.3
-------5.3
-49.3
-2.0
-18.1
-1.9
-6.6
-1.7
-10.9
-------------
-7.3
-8.1
-6.7
-------------90.3
-48.7
-12.1
-------8.7
-46.3
-4.2
-19.3
-------------
0.0
-0.2
-3.3
-4.4
-8.5
-0.5
-0.1
-2.5
-3.3
0.0
-0.1
-0.2
-1.1
-20.4
-79.1
-7.0
-3.8
-46.8
-3.8
-------13.1
-70.1
-36.9
-46.8
-4.6
-29.6
-2.7
-10.8
-------------
-7.2
-17.5
-41.7
-5.9
-82.6
-30.9
-46.1
-16.5
-41.7
-6.6
-38.2
-8.9
-13.0
-------------
-4.6
-4.8
-46.0
-7.1
-79.0
-------43.5
-21.9
-46.0
-5.8
-23.0
-4.7
-13.1
-------------
-0.2
-25.7
-13.4
-0.4
-49.3
-------12.6
-2.9
-13.4
-1.2
-8.0
-0.5
-2.8
-------------
-2.9
-0.4
-4.8
-2.5
-14.5
-33.0
-2.2
-2.6
-4.8
-0.2
-1.4
-0.7
-3.5
-49.5
-97.5
-39.9
-7.9
-------22.8
-------------24.1
-42.0
-24.7
-8.2
-73.5
-6.0
-18.9
-------------
-0.7
-1.9
-22.7
-0.3
-44.0
-99.3
-15.5
-6.6
-1.3
-22.7
-68.8
-1.3
-3.8
-------------
-0.7
-1.1
-18.0
-0.5
-38.6
-83.7
-5.0
-11.0
-1.8
-18.0
-16.6
-0.9
-1.9
-------------
-4.4
-3.5
-26.0
-3.5
-47.7
-------11.7
-6.7
-7.6
-26.0
-1.3
-11.6
-12.6
-------------
-9.8
-6.9
-33.3
-11.6
-76.3
-------11.7
-9.2
-59.5
-33.3
-1.5
-7.7
-5.1
-------------
-8.3
-1.2
-16.1
-5.6
-37.5
-86.0
-15.3
-5.4
-9.9
-16.1
-1.3
-14.1
-1.6
-7.0
-------
-1.7
-0.4
-7.3
-6.4
-17.2
-40.0
-2.4
-1.2
-12.2
-7.3
-0.1
-2.4
-0.7
-9.0
-64.8
Contagion/Vulnerability Index
• Bank i’s contagion index: the sum of all the capital
losses in the system (except for the trigger bank)
divided by the sum of the capital of all banks.
• Bank i’s vulnerability index: the simple average
of percentage of capital losses suffered by a
country in all the simulations.
19
1-Abr-14
1-Jan-14
1-Out-13
1-Jul-13
1-Abr-13
1-Jan-13
1-Out-12
1-Jul-12
1-Abr-12
1-Jan-12
1-Out-11
1-Jul-11
1-Abr-11
1-Jan-11
1-Out-10
1-Jul-10
1-Abr-10
1-Jan-10
1-Out-09
1-Jul-09
1-Abr-09
1-Jan-09
1-Out-08
1-Jul-08
1-Abr-08
1-Jan-08
Contagion and Vulnerability Indexes
Bank 16
120
100
80
60
Index of Contagion
Index of Vulnerability
40
20
0
20
Recent Approaches
• A simulated network approach, Hałaj and Kok (2013)
Next step:
Design Pigouvian Capital Surcharges
Based on the systemic contribution of an
institution
From the Work of Espinosa-Solé (2010)
Capital surcharges should be commensurate with
the large negative effects that a financial firm’s
distress may have on other financial firms—their
systemic interconnectedness.
A Methodology to Compute Systemic-Risk Based
Surcharges comprises:
1. Tracking financial institutions’ portfolios through
the credit cycle.
2. Estimating each institution’s spillover effects
following a stress event, at each point in the cycle—
based on network analysis.
3
Computing Systemic-Risk Based Surcharges
4
Computing Systemic-Risk Based Surcharges
5
Computing Systemic-Risk Based Surcharges
from Espinosa-Vega & Solé
Two Approaches:
• Standardized Approach (SA): regulators assign systemic risk
ratings based on the amount of system-wide capital impairment
that the default of each institution would induce under a tail-risk
scenario.
• Risk-Budgeting Approach (RBA): capital surcharges are a
function of an institution’s marginal contribution to systemic risk
and its own probability of distress.
In addition,
A smoothing technique is applied to the RBA to lessen
its procyclical profile.
6
The Standardized Approach
• Consider three ratings based on the system-wide capital impairment
caused by an institution’s default:
 Tier 1 (T1) if capital of distressed institutions is above 35% of system’s
capital,
 Tier 2 (T2) if capital of distressed institutions is 20-35% of system’s capital,
 NS for the rest of institutions
• Each institution is rated at the peak, trend and through of the
business cycle.
• Regulator assigns a “systemic-risk rating” based on each institution ‘s
worst rating through the cycle, to reflect tail-risk.
• The regulator then assigns a capital surcharge based on this systemicrisk rating
7
The Standardized Approach
8
The Standardized Approach
from Espinosa-Vega & Solé
Capital Surcharges Based on SA
11
12
The Risk-Budgeting Approach
• Under this approach, an institution’s capital surcharge is
determined as a function of its probability of default and its
incremental credit Value at Risk (VaR)
• Incremental VaR is defined as the increase in the joint VaR of
the rest of institutions, caused by the institution’s default on its
interbank exposures.
14
The Risk-Budgeting Approach (RBA)
15
Figure
5. Simulation of Systemic Risk Capital Surcharges
(Capital shortfall in percent of risk weighted assets)
Cyclical capital shortfall
Smoothed capital shortfall
0.6
4.5
Bank 1
4.0
Detrended credit growth
0.6
4.5
Bank 2
4.0
0.4
3.5
0.4
3.5
3.0
3.0
0.2
2.5
- 1E - 15
2.0
0.2
2.5
0
2.0
1.5
1.5
- 0.2
1.0
- 0.2
1.0
0.5
0.5
- 0.4
0.0
- 0.5
- 0.6
Trend
Trough
4.5
Trend
- 0.5
- 0.6
Trend
Peak
0.6
Bank 3
- 0.4
0.0
4.0
Trough
4.5
Trend
Peak
0.6
Bank 4
4.0
0.4
3.5
3.0
0.2
2.5
0.4
3.5
3.0
0.2
2.5
0
2.0
1.5
0
2.0
1.5
- 0.2
1.0
0.5
- 0.4
0.0
- 0.2
1.0
0.5
- 0.4
0.0
- 0.5
- 0.6
Trend
Trough
4.5
Trend
- 0.5
Peak
0.6
Bank 5
- 0.6
Trend
4.0
Trough
4.5
Trend
Peak
0.6
Bank 6
4.0
0.4
3.5
3.0
0.2
2.5
0.4
3.5
3.0
0.2
2.5
0
2.0
1.5
0
2.0
1.5
- 0.2
1.0
0.5
- 0.4
0.0
- 0.2
1.0
0.5
- 0.4
0.0
- 0.5
- 0.6
Trend
Trough
Trend
Peak
- 0.5
- 0.6
Trend
Trough
Trend
Peak
Source: IMF staff calculations.
Note: The capital shortfall is defined as the difference between the minimal Basel capital requirement
the systemic
assets.
- risk
surcharge
minus
the actual total capital of each institution, in percent of risk
plus
- weighted
19
The Risk-Budgeting Approach (RBA)
• An alternative is to focus on marginal expected
short fall as in Acharya et.al. (2010)
17
The BIS approach
Country Experience
Macroprudential Toolkit for Cross Sectional Dimension
(number of countries)
120
100
80
60
40
20
0
Yes
No
SIFI surcharge
Yes
No
Interbank
exposure limit
Yes
No
Concentration
Limit
Source: IMF survey on Macrorudential Policy
Instruments, 2013.
•
Concrete plans to introduce SIFI surcharges within 2 years? 37 countries
•
Early adopters: Australia, Canada, Singapore, Austria, Denmark, Sweden and Switzerland.
Macro-Financial Linkages
Interconnectedness, Systemic Crises, and
Recessions. Espinosa-Vega and Russell (2015)
37
In the Aftermath of the Crisis
Claims
• financial institutions’ linkages may increase an economy’s vulnerability
to systemic financial crises
• recessions that follow financial crises tend to be exceptionally severe
(e.g., Reinhart and Rogoff (2009), Kannan et.al. (2009))
• the recent financial crisis was caused, at least in part, by a lack of
complete and accurate information about the state of the portfolios of
financial institutions.
Corollary
• proposals for government regulation to limit interconnectedness
A paper consistent with these claims
In Espinosa-Vega-Russell (2015) we lay out a simple theoretical model that
helps explain the relationship between interconnectedness of financial
institutions, systemic financial crises, and long, severe recessions. In our
model:
 diversification retains its traditional advantage from portfolio theory:
depositors are risk averse and diversification tends to increase their expected
utility (by reducing the variance of their returns, across portfolios and states
of the world)
 interconnectedness means banks connect with each other by loan portfolio
diversification: their portfolios include loans originated by other banks. This
interconnectedness is attractive to bank depositors but potentially hazardous
for the banking system.
•
Related literature. Bimpikis and Tabaz-Salehi (2012), Ibragimov, et al. (2011),
and Wagner (2010, 2011).
• In our paper, interconnectedness obscures information about the sources
of banks’ portfolio returns.
A paper consistent with these claims
• Loan portfolio diversification makes it difficult for a bank’s
depositors to determine the returns on particular components
(by originator) of the bank loan portfolio. As a result, depositors
may make inefficient decisions, of two types:
– They may liquidate loans (and projects) whose prospects are good,
along with loans whose prospects are bad. (They may also fail to
liquidate loans whose prospects are bad.) This can create a
recession.
– because it may be difficult for banks to determine which group of
managers originated bad loans, many banks may fire their
managers, even though a few banks’ managers were
incompetent. This will impair the effectiveness of bank risk
assessment, going forward, increasing the probability of prolonged,
severe recessions.
Interconnectedness and Externalities
• The link between the decisions of current depositors
and the returns received by future depositors produces
an intertemporal externality,
– bank managers act to maximize the expected utility of the
current depositors.
Macroprudential Policy
 The government cares about current and future
depositors…creating a rationale for restricting
interconnectedness: Macropru regulation.
– Complete restriction
– Partial restriction
Macroprudential Policy
 Macropru regulation: curtailing interconnectedness
– reduces inefficient liquidations and boosts future depositors’
expected returns
– make it easier to differentiate the returns on loans with different
originators, which produces both more efficient liquidation
decisions and more efficient ‘firing’ decisions and shorter
recessions.
… but regulation has costs: curtailing interconnectedness increases
variability in depositors returns, thus reducing depositors expected
utility.
 Optimal regulation will be a function of the depositors’ degree of risk
aversion
 Optimal regulation may not eliminate crisis
Summing Up
• Keen interest in furthering our understanding of
financial interconnectedness
…with an eye at identifying SIFIs and their
potential contribution to systemic risk.
…to help in the design of macroprudential
policies (e.g. Pigouvian capital surcharges)
• Ongoing analysis on when to trigger, tighten or relax
macropru policies…and their interaction with the
macroeconomy.
44
Summing Up
• More needs to be done to improve our
understanding of macro-financial linkages.
• When financial interconnectedness goes ‘wrong’
what is the mechanism through which the
macroeconomy gets affected?
• What should the policy response be?
45
THANK YOU
46