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Connecting the Dots:
Designing
i i
Macroprudential
d
i l
Tools that Work
L
Laura
E
E. Kodres
K d
Assistant Director
Monetary and Capital Markets Department
September 27, 2012
Outline



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What types of systemic risks are we
attempting
tt
ti
tto mitigate?
iti t ?
Connecting systemic risks to
e te nalities/ma ket failures
externalities/market
fail es
Connecting externalities to indicators of
systemic risks
Connecting indicators to practical policy
tools
Existing limitations and limiting
expectations
Connecting the Dots
Externalities
Systemic
Risk
Indicators
Policy Tools
?
Minimize effects of externalities
Types of Systemic Risks

Time-series dimension



Procyclicality of credit
Asset/collateral booms and busts
Cross-section/interconnectedness
dimension


Counterparty credit risks
Interbank freezes
Externalities and Systemic Risks

Coordination failures




Fire sales




Procyclicality of credit/leverage
Similar position taking/reaction to common incentives
Dependence on bailouts/benchmarking
Marked to market accounting/credit ratings
Margin/collateral calls
VaR systems
Cross-sectional/Interconnectedness


Interbank/wholesale funding freezes
H d i
Hedging
and
d portfolio
tf li rebalancing
b l
i
Externalities and Tools (I)

Require tools to address type of
externality



Excess credit/leverage is solvency issue—consider
tools to limit insolvency
Runs in wholesale funding markets is a liquidity
issue—consider tools to limit illiquidity
Decide who/what to apply the tool


Types of institutions/people
“Market”
Market functioning/structure
Decide price-based or quantity-based?
 Decide where systemic risks go? Still with
institution or with public sector?

Externalities and Tools (II)

How much does institution or market
“contribute”
contribute to systemic risk?



Need a specific measure of the systemic risk or
externality
How granular/specific does it need to be?
Consider these “contributions” as intermediate
t
target
t measures or iindicators
di t
off systemic
t i risk
i k
Indicators/Measures (I)

Coordination failures and fires sales






Various credit measures – credit to GDP, credit to
GDP gap, real credit growth
Leverage – assets/equity
House prices – real house price indices deflated by
CPI
Liquidity – non-core
non core funding (bank credit/deposits)
Capital flows – foreign liabilities/foreign assets for
banks
Margining/collateral practices
Indicators/Measures (II)

(Mostly) cross-sectional/interconnectedness dimension







Marginal Expected Shortfall (MES)
Conditional Value-at-Risk (CoVar)
Systemic Contingent Claims Analysis (SCCA)
Systemic liquidity risk index (SLRI)
Joint Probability of Default (JPoD)
Network-based metrics
Typically
T
i ll measures use market
k t data
d t on
institutions (with some balance sheet
information or some bilateral exposure data)
Connecting Indicators to Tools (I)

Time-series tools are applied to institutions but
since measure is relatively “fuzzy”
fuzzy both mitigates
“regular” risk and systemic risk






Reserve requirements
q
Capital requirements
Provisioning
Loan-to-value ratios
Debt-to-income ratios
Some tools can be applied to “markets”


Through-the-cycle margin/haircuts
R
Removal
l off investment
i
t
t grade
d credit
dit ratings
ti
Connecting Indicators to Tools (II)

Cross-section/interconnectedness tools young



G-SIFI
G
SIFI surcharge
h
Basel 2.5 risk-weights for counterparty credit risk
Other solvency surcharges




Liquidity charge/premium



Network modeling and CreditRisk+
CCA used to calculate solvency distress
Stress tests
CCA used to calculate liquidity distress: “market
market risk”
risk NSFR
Actuarially-fair insurance based on “contribution” to Systemic
Liquidity Index
Tools
T
l tend
t d to
t b
be more complex,
l
but
b t more
precisely aimed at externality
Connecting Indicators to Tools (III)

How to connect to solvency surcharge




Calculate
C
l l t aggregate
t loss
l
given
i
default
d f lt (LGD) using
i
credit VaR (monetary losses for the entire system
with and without Bank X’s interbank exposures)
based on individual bank’s probability of default,
assets, and LGDs
Multiply by probability of default of Bank X
Gives expected loss to system of default of Bank X –
g Bank X this amount (as
( percent
p
of capital).
p )
charge
Can average these through the cycle to add a
countercyclical component
(GFSR April
A il 2010)
Indicators and policies
A stitch in time
Theoretical: Effectiveness
of Tools
When to Act—Multiple Indicators
Low chance of
missing a crisis:
change in
Credit/GDP
>3-5 pp
(IMF GFSR,2011)
 Low chance of
overregulation
”gap”>1.5
g p
s.d. &
growth>10%
(Dell’Ariccia et al)
 Combination
b
(see figure)

Crisis
probability
20-25%
>5 pp
>15%
Policies costly if source of shocks
(ex: Squashing
healthy
gro.…
with time-varying capital)
When
Not To
Act
When to Act—Near-Coincident Indicators
Performance of marketmarket-based indicators
Source: Arsov, Canetti, Kodres, and Mitra (forthcoming)
Early Results on What Works
Policy Instruments
Reserve requirements
Capital requirement
Provisioning
LTV
DTI
1Ongoing
Reducing the Change (%) in1
Banks'
F i
Foreign
Real
Liabilities/
House
Foreign
Real
Price
Credit/GDP
Credit
/
Assets
***
*
***
**
***
*
***
*
***
*
work.
2See Lim and others (IMF Working Paper 11/238,
11/238 2011)
*** = significant at 1%; ** = significant at 5%; * = significant at 10%.
Reducing the
Correlation
between GDP
Growth and2
Real
Credit
Growth
***
**
**
***
Assets/
Equity
Growth
***
**
***
***
Existing
g Limitations ((I))

Cross-sectional assignment of systemic
risk incomplete




Institution needs to know direct and indirect
connections /knock-on effects to judge risk of a direct
counterparty exposure
exposure.
Institution A does not want to be “charged” for
exposing distant counterparties to its risks (not its
“f lt”)
“fault”).
Should market data be relied upon to assess these
risks?
Would removing asymmetric information by
publishing all exposure data fix the externality? Or
wreck the smooth functioning of markets?
Designing Tools with Networks
Bank 1
Bank X
Bank A
Bank 2
Bank 3
Bank Y
Bank 4
Bank 6
Bank 5
Existing
g Limitations ((II))

How much reliance on market discipline?



How can incentives be harnessed to control
systemic
y
risk?



Can systemic risk, by it very nature, be solved
by more market discipline?
How much confidentiality should institutions be
allowed?
The more systemic you are, the more you pay
to be part of the system? Can one reasonably
reduce one
one’s
s systemic-ness?
systemic ness?
What about the beneficiaries of safe systems?
Should they pay?
Can simplicity be encouraged? Mandated?
Limiting Expectations
How much systemic risk is optimal to
mitigate? Not all…
 How much can policymakers realistically
plan to offset with macroprudential tool?





Versus microprudential tools?
Versus monetary policy? Exchange rate policy?
Versus fiscal policy?
What about ex post tools to deal with
realization of systemic
y
risks?



Resolution
LOLR
B il
Bail-outs/Bail-ins
/B il i
Connecting the Dots:
Designing Macroprudential
Tools that Work
L
Laura
E
E. Kodres
K d
Assistant Director
Monetary and Capital Markets Department
September 27, 2012