Macroprudential Policy and
Procyclicality
Connel Fullenkamp
Duke University
IMF – CAPTAC-DR Course on
Macroprudential Policies
March 2017
This training material is the property of the International Monetary Fund (IMF) and is intended for use in
IMF Institute for Capacity Development (ICD) courses. Any reuse requires the permission of ICD.
What Is Procyclicality in the
Macroprudential Context?
• An activity is procyclical if it tends to enhance
or intensify some type of cyclical fluctuation
• In finance, there are many types of cycles that
may be enhanced by financial activity:
– Price cycles
– Leverage cycles
– Risk-taking cycles (“risk-on, risk-off”)
• These cycles in turn affect solvency of banks
and other financial institutions
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Procyclicality: Another Unintended
Consequence of Regulation
• Most familiar examples of unintended
consequences are associated with attempts
to avoid or evade regulation
• Regulatory Capital Arbitrage: the attempt
to make an institution’s capital adequacy
appear to be greater than it actually is
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The Regulatory Cycle
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A Macroprudential Paradox
• Procyclicality leading to systemic risk can
also result from attempting to comply with
regulation
• Why?
– Regulations often rely on imperfect
measurements of reality, especially forecasts
– Regulation generally coordinates activity on a
large scale
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Example:
Private Risk Management Spillover
• Regulators require institutions to adopt a
risk-management plan
• Each private firm plans and implements a
risk-management strategy
• Each firm believes risks are static and
unaffected by their activity
• True risk is underestimated, leading to
“catastrophic” losses when the market prices
change, in response to aggregate activity
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Another Cause of the
Procyclicality Problem
• The menu of risk-management actions is not
very long, especially after a bad event occurs
– Risk transfer
– Post-loss financing
– Absorption
• Example: “Living Wills”
• How will a large institution wind itself down?
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Measurement and Procyclicality
• Measuring the sources of risk is an essential
step in risk management (and compliance
with risk-management regulations)
• Measurement may pass along or amplify
existing procyclicality
• Measurement may be a separate source of
procyclicality
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Cyclicality of Indicators
• Some financial indicators are highly correlated with the
economic cycle.
Procyclicality and Regulation
Borio (2001)
9
Business Cycle Component in Recovery
Rates
Source: Chen, Journal of Finance 2010
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Defaults by Credit Rating
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Moody’s Rating Changes (1983-2011)
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Sovereign Rating Changes, Moody’s
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Interaction of Cyclical Data
and Regulation
• Prudential regulations are based on indicators of
risk.
• Many measures of risk depend on observed asset
prices:
– Housing
– Stock prices
• Estimated risk tends to decrease in periods of high
growth and to increase thereafter.
• Implication: Prudential requirements calibrated
on contemporaneous measures of risk tend to
accentuate pro-cyclical behavior in financial
intermediaries.
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Example: Credit Risk Models
• Measured credit risk is cyclical; defaults fall
as the economy grows and then rise quickly
after the economy begins to contract
• Example: default rates had never been lower
right before the 2008 crisis
• Danger: can’t forecast the turning points of
this cycle
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Credit Risk Model Example continued
• Traditional credit risk models (rating
systems, credit scoring)
– Like equity analysis with some event risk added
• Models based on equity prices (contingent
claims analysis)
– Equity prices also tend to be pro-cyclical
• Models based on credit transition matrixes
(CreditMetrics)
– Credit spreads tend to narrow in booms
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Basel II Internal Ratings-Based
(IRB) Credit Risk Models
• IRB banks use a “point-in-time” methodology to
measure risk
– To calculate probabilities of default (PDs), all the
information available at a certain point in time is used
to estimate PD over a certain period:
• Data observed during one year
• Includes expected long run credit risk for the company
and sectoral trends
• Point-in-Time PDs react immediately to all
information on the default risk of a company,
• Credit risk signal is improved but at the cost of
higher volatility.
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Example 2: Accounting and
Asset Valuation
• Accounting methodologies may affect the
cyclicality of balance sheet measures:
– Fair value or historical value may reduce
cyclicality
• “investment book”
– Mark-to-Market may increase cyclicality
• “trading book”
– Mark-to-Model effects depend on model
assumptions and how parameters were estimated
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Data Problem 2: Imperfect
Measurement and Cyclicality
• Much of our microprudential risk
management regulation depends on the
Value at Risk (VaR) framework
• VaR critical values represent lower
boundaries of large-loss regions that we
expect to enter infrequently
• These boundaries must be
estimated…imperfectly, which can lead to
cyclicality problems
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Procyclicality of VaR Values and
Market Risk
• During periods of generally rising asset values,
positive realizations of price changes
outnumber and outweigh negative realizations
• Thus, VaR critical values naturally move up
toward 0 during these periods
• When prices begin to decline, VaR will
underestimate the lower tail risk—sometimes
dramatically
• Overestimation of tail risk also possible!
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Effect of Data on VaR Critical Values
35
30
25
20
1% VaR Critical
15
10
5
0
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Data Limitations of VaR
• All VaR implementations depend ultimately
on using historical data
– Directly, to estimate empirical distribution
– Indirectly, to estimate parameters of an
assumed underlying distribution
• Basel requirement for market risk: 1 year of
daily data
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Changes in Distributions
• Parameter values may change out of
sample—procyclicality is one illustration
• Volatility of financial data not only varies
over time but also appears to include
discrete jumps in value
• Underlying distribution of the data could
also change
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The Accuracy-Relevance Tradeoff
• Using fewer observations results in lessaccurate parameter estimates
– With 500 data points, the highest empirical
VaR critical value is 99.8%
– Parameter estimates are less precise
• Including more data runs the risk of biases
resulting from changes in the underlying
structure of the markets or economy
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Special concern: Extreme VaRs
• The extreme (99.9% and higher) VaR values
now typically used are specifically subject to
the accuracy-relevance tradeoff
• Rebonato: “…our risk metrics cannot be
anywhere as precise as they are made out to
be. This is not because we must ‘try harder’—
say, collect more data, or use cleverer
statistical techniques. It is because, given the
problem at hand, this degree of precision is
intrinsically unattainable.”
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Expected Shortfall Especially
Vulnerable
• Expected Shortfall is the expected loss or
return given that the loss or return is lower
than the VaR critical value: E( X | X ≤ VaR)
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Most VaR-based Measures
Vulnerable to Cyclicality
• Using a limited amount of backward-looking
data to estimate VaR critical values can
induce cyclicality in any VaR-based standard,
in the sense of intensifying a given change in
the market
• Market risk, liquidity risk, credit risk are
clearly cyclical
• Even operational risk may be vulnerable to
induced cyclicality
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Through-the-Cycle Estimates:
Answer to Data-Driven Cyclicality?
• Seeks to capture the long run risk of a firm.
– Transitory short run changes in risk that tend to
revert with time are filtered out.
– The risk estimates are more stable through the cycle
and are not subject to abrupt changes.
– As a consequence this measure displays less volatility
than Point-in-Time estimates during the cycle.
• Through-the-cycle estimates are initially more
stable, But they may suffer from inferior
performance in predicting future defaults
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Users of Through-the-Cycle
Estimates
• Rating agencies claim to use through-thecycle estimates of PDs
• Basel’s IRB approach (both Foundation and
Advanced) permits through-the-cycle
estimates of PDs
• Through-the-cycle estimates of Loss Given
Default (LGD) also possible but Basel prefers
“downturn” LGDs
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Through-the-Cycle Estimates Not a
Perfect Solution
• They rely on trend-cycle separation, which is
imperfect
• We don’t necessarily want to remove the
cyclicality
• The probability of being downgraded increases at the
bottom of the cycle
• LGD volatility also important to systemic risk (may be
more important than PD volatility)
• The information also needs to be used wisely—
rating agencies delay ratings changes
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Interaction of Regulation and
Common Financial Cycles
• Leverage Cycle, including Collateral and
Margin
• Credit Cycle
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Collateral
• Collateral policies have a large role in
determining the size of financial institutions
• Collateral can interact with regulation and
institution size through two channels
– Valuation Method
– Leverage: Loan-to-Value Ratios, Margins,
Haircuts
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Collateral Spirals
• Classic example from Shin
• Suppose an institution desires to maintain a
constant leverage ratio: 10 units of assets to
every unit of capital
• Initial capital = 10
• Initial assets = 100
• Initial debt = 90 (value of promised
payments)
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Rising Value of Assets Increases
Borrowing
• Suppose value of assets rises to 101 because
market price rises
• Assets = 101; Debt = 90; Capital = 11
• To maintain capital-asset ratio of 10, the firm
can borrow 9 and expand assets:
• Assets = 110; Debt = 99; Capital = 11
• A one-percent increase in value leads to tenpercent increase in borrowing
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Now Let Asset Value Fall
• Suppose market prices fall so that the assets
are now only worth 109:
• Assets = 109; Debt = 99; Capital = 10
• The firm is overleveraged and must reduce
assets or raise capital
• Suppose firm tries to sell assets—it must sell
9 units of assets to repay debt
• The large sale of assets may depress prices…
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The Death Spiral
• Suppose that asset values fall so that after
selling enough to repay 9 units of debt, the
assets are now only worth 99:
• Assets: 99, Debt: 90, Capital: 9
• The firm is even more overleveraged than
before, and must sell more assets
• The fire sales get larger and downward
pressure on asset value increases.
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Dependence of Collateral Spiral on
Valuation and LTV
• Using market values for collateral enhances the
procyclicality
• The higher the loan to value ratios (LTVs), the
greater their impact on the cycle
• The higher the LTV ratio the higher the amount of
new lending that can be granted for a given change
in the value of collateral.
• LTVs often increase because of competitive
pressures during asset price booms
• This also applies to Haircuts and Margin (which
fall during asset price booms)
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Leverage Inflates Asset Values
• High LTV, low margin and low haircuts all
allow high levels of borrowing
• High rates of borrowing can sustain demand
for the assets—at least while the supply of
borrowers increases
• Note the relationship between leverage and
asset value in the following slides
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Housing Leverage Cycle
Margins Offered (Down Payments Required) and Housing Prices
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Securities Leverage Cycle
Margins Offered and AAA Securities Prices
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Source BIS 2010
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Regulations: Collateral Policies
• Prudential regulations aimed at credit risk
mitigation often use loan-to-value and debt-toincome ratios.
– Loan-to-value ratios are a measure of the leverage
that you allow to have, and thus of the multiplier
effect of a one additional unit of credit.
– Debt–to-income ratios (or payment-toincome ratios) cap leverage regardless of collateral
values.
• LTV limits can limit systemic damage to banks
but may not address the root cause of problem
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Capital Regulation and
Procyclicality
• Bank capital regulations may aggravate the problem
of pro-cyclicality and thus increase the threat of
systemic risk.
– Reliance on credit ratings in the standardized approach to
credit risk
– Role of Probability of Default and Loss Given Default in the
IRB approach to credit risk, both of which are highly
cyclical
– Emphasis of marking-to-market arrangements by
accounting practices and trading book evaluation.
• RWAs are an increasing function of the probability of
default, loss given default, and the exposure at
default.
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How Procyclicality Works:
Standardized Approach
• In the Standardized Approach, capital asset ratio
(CAR) is given by
Equity
CAR 
0.2 A1  ...  0.75 Ai  ...  1.25 Aj
(ratings) AAA
AA
A
• If asset Aj is upgraded in the upswing from A to AA
rating, CAR would automatically improve: riskweighted assets decrease, increasing the ratio
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A Through-the-Cycle Fix?
• To avoid the problem with cyclicality of upgrades
and downgrades, take average of rating of asset
Aj over the cycle for that asset or class of assets
(say, commodities)
• But do we really want banks to ignore the
downgrade part of the cycle?
• Recall that risk is “one-sided volatility”
• Is it feasible or desirable to have an asymmetric
treatment of ratings changes in the standardized
approach?
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How Procyclicality Works: IRB
• Similar to collateral spirals described above
• Now the cyclicality of asset values,
probabilities of default, losses given default,
and other measures that feed into the RiskWeighted Assets measure drive the spiral
Procyclicality and Regulation
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Example of IRB Procyclicality
• Initial Balance Sheet at t=0:
• Assume that average risk weight is 100% or TA=RWA.
• Minimum regulatory capital requirement is (K / RWA) >
8%.
• RWA = 100; Liabilities = 92; K = 8 so CAR = 8/100 = .08
• Leverage ratio = TA / E = 100 / 8 = 12.5.
TA=RWA Liab 92
100
Equity 8
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IRB Example, continued
• Initally, bank cannot increase its size beyond 100 as the capital
constraint is binding.
• Now, suppose at time t = 1, there has been a healthy economic
expansion
• Suppose asset values remain the same
• But because of the effects on PDs and LGDs of the expansion,
Risk-Weighted Assets fall to 80 from 100
• Now the CAR = K / RWA = 8 / 80 = .10
• Thus the bank has 200 bp of “excess” capital that it can return
to shareholders or use to expand its assets
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IRB and Leverage
• Assets can increase by up to 25 (100/0.8 - 100) to reach 125,
in which case they will be financed by an equal increase in
liabilities
• There is an expansion…so why not borrow more and lend?
• Suppose leverage increases by 25, so that Liabilities = 92 + 25
= 117.
• (Simple) Leverage ratio increases from 12.5 (or 100 / 8) to
15.6 (or 125 / 8); CAR goes back to 8% = 8/(125*.8).
TA 125
Liab 117
(RWA=80 ) Equity 8
Procyclicality and Regulation
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Implications
• During the growth phase of the cycle, RWA tend
to decrease
• The bank may acquire more assets to increase
income or repay surplus capital to its shareholders.
• In the contraction phase, the credit grade
deteriorates and RWA tend to increase.
• The bank must increase capital–how?
• The result is increased leverage within the financial
system in the upturn which in turn leads to lower
capital and liquidity buffers during downturns.
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Basel II CAR and GDP growth, Spain
1986-2007 (Repullo et. al 2010)
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Basel 4 to the Rescue?
• One proposed feature of Basel 4 is a lower
bound for risk-weighted assets
• This will mitigate the problem of migration,
but how much will depend on the lower
bound chosen…and whether this bound can
vary with economic conditions
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Provisioning and Procyclicality
• Provisions set aside by the banks strongly
negatively associated with the economic cycle
– Provisions only increase after the cycle has turned
– Provisions tend to generate pro-cyclical behavior
in banking profits, which increase bank’s lending
capacity during upswings and strengthen procyclical behavior of the banking sector
– Releasing provisions adds to income (and capital)
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Borio 2001
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Underprovisioning
• This is a more “classic” case of unintended
consequences—banks try to avoid regulation
• Banks have incentives to under-provision during
good times
• Differential regulatory and tax treatment of
provisioning (charge-off method vs. reserve method)
• Compensation schemes often directly related to
lending volumes, profits, and earnings
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Underprovisioning and
Procyclicality
• Underprovisioning frees resources, which
leads to procyclicality
– On the upward swing of the cycle, banks can
expand lending more if they underprovision
– Banks also delay provisioning for bad loans until
the downturn has begun, but raising reserves in
such conditions prolongs bad times
– When the cycle turns, a credit crunch ensues, as
large capital losses due to nonperforming loans
lead to a drastic reduction of credit
Procyclicality and Regulation
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What about Dynamic Provisioning?
• Tried in Spain during the 2000s
• Complicated to implement, and did not seem
to prevent systemic problems (remember the
Spanish banking crisis?)
• Other solutions are being tried instead…one
of them is a countercyclical capital buffer
instead of provisioning (and implemented as
part of Basel 3 Plus)
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A Simpler Change in Provisions
• Moving from backward-looking “incurred
loss” standards for provisioning to forwardlooking “expected loss” standards for
provisioning
• Incurred Loss Standard: provisions are some
function of past-due or outstanding balances
• Expected Loss Standard: IFRS 9,
implemented by January 2018, requires
expected losses to be recognized
Procyclicality and Regulation
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Summary
• Procyclicality of regulatory capital often
reflects the cyclicality of underlying data
• Regulation can intensify procyclicality
because of measurement issues and the
coordination effect
• Some regulations can avoid or dampen
procyclicality (LTV ratios, standardized
approaches)
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Applying the Lessons
• Measuring procyclicality of leverage, asset
prices and other financial characteristics is an
essential step in macroprudential policy
• Macroprudential regulations can aim to limit
the influence of procyclical indicators on
lending and trading practices (for example,
LTV ceilings)
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Applying Lessons continued
• Macroprudential regulations can also require
institutions to prepare for later downturns
during any cyclical upswings (for example,
countercyclical capital buffers and
provisioning)
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Questions for the Future
• How much can the cyclicality from regulation
be reduced through methods like throughthe-cycle ratings?
• What is the optimal level of cyclicality in
regulations?
• Will greater emphasis in IRB-type
approaches to regulation spread unintended
procyclicality to new areas, such as liquidity?
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