Download Impact of Macroprudential Policy Measures on Economic Dynamics: Koji Nakamura

Impact of Macroprudential Policy
Measures on Economic Dynamics:
Simulation Using a Financial Macro-econometric Model
Koji Nakamura
E-mail: [email protected]
Bank of Japan
“The Interaction of Monetary and Macroprudential Policy”
Reserve Bank of New Zealand Workshop
22 October, 2014
The views expressed here are those of the authors and should not be ascribed to the Bank of Japan.
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Presentation Outline
1. Introduction
2. Financial Macro-econometric Model (FMM)
3. Simulations and Results
4. Discussions and Extensions
2
1. Introduction
• Motivation
• Brief Literature Review
3
Motivation
• After the global financial crisis, “financial
cycles” attract more attention of policy makers.
• There is a hope that “macro-prudential
measures” could counter “financial cycles.”
• However, we do not have sufficient
experiences of macro-prudential measures so
far.
• We need some “experiments” of macroprudential measures to check pros and cons.
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Brief Literature Review
• Single macro-prudential measure with a small
theoretical model: Cristensen et al. (2011)
• Single macro-prudential measure with a small
empirical model: Aiyar et al. (2012).
• Multiple macro-prudential measures with a
small theoretical model: Angelini et al. (2011)
and Goodhart et al (2012).
• Multiple macro-prudential measures with a
large empirical model: This paper.
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2. Financial Macro-econometric Model
(FMM)
•
•
•
•
•
•
•
Overview of the FMM
Structure
Feedback loop
Banks’ activities
“Expectation channel”
Spending activities
Monetary policy
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Overview of the FMM
• The FMM is used for BOJ’s macro stress testing
exercise and is a medium-sized structural model
with the detailed financial sector and the macro
economic sector.
• About 120 banks* are explicitly modeled with
actual banks’ data such as capital, loan amounts,
and transition probabilities.
• The macro economic sector is simpler than other
large scale macro models such as FRB/US, but
incorporates a feedback loop between the macro
economy and the financial sector.
* The latest version of FMM includes 373 banks and has been improved for the financial
sector. See Kitamura et al. (2014) for the details of the latest version of FMM.
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Structure
<Financial Sector>
<Macroeconomic Sector>
Credit cost, Lending interest rate,
Capital adequacy ratio,
Bank earnings, Lending volume
GDP (Corporate capital spending,
Household expenditure, etc)
Corporate earnings,
Employee compensation
<Expected Growth and Asset Price Factors>
Expected growth rate, Stock prices,
Land prices
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Model Performance Evaluation
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Feedback Loop
10
Second-round Effect
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Banks’ Activities (1)
• Banks’ activities affect the real economy through loan amount
and loan interest rate.
 Bank i’s loan amount to corporate sector
= Bank i’s fixed effect + 1.5*expected growth rate - 1.9*(Bank i’s loan
interest rate – CPI ) + 0.4*Bank i’s capital ratio gap + 0.3*land price
 Bank i’s loan interest rate
= Bank i’s fixed effect + 0.95*Bank i’s funding rate + 0.01*loan amount gap
 Bank i’s funding rate
= Bank i’s fixed effect + 0.7*policy rate – 0.1*Bank i’s capital ratio gap
 Land price
= -4.00 + 0.16*nominal GDP growth + 1.03*loan amount (-1) + 1.83*CPI
Capital ratio gap is the difference between actual capital ratio and its regulatory level.
Loan amount gap is the difference between actual loan amount and its level consistent with potential GDP.
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Banks’ Activities (2)
• Individual banks’ credit costs are influenced by the developments of
macro economy and borrowers’ financial condition.
 Bank i’s credit cost
Credit cost
= ΣmΣn(transition probability of Bank i’s self-assessment from m to n)*
(loss ratio at time of downgrading of Bank i’s self-assessment from m to n)*
(exposure of Bank i’s self-assessment of m)
Transition probability
= Bank i’s fixed effect + α*nominal GDP growth + β*borrowers’ liquid asset-liability
ratio*nominal GDP growth + γ*borrowers’ interest coverage ratio*nominal GDP
growth
The specification of transition probability is revised recently. For more details, see Kitamura et al. (2014).
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“Expectation Channel”
• Okina, Shirakawa, and Shiratsuka (2000) : “the intensified
bullish expectations which played an important role behind
the large fluctuations in asset prices and the economy.”
• In FMM, real expenditures and asset prices are affected by
expected growth rate.
 Expected growth rate
= 0.77*potential GDP growth rate + 0.10*actual GDP growth rate
 Stock price
= 9.63*corporate profit + 1.39*expected growth rate + 0.32*U.S. stock price
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Spending Activities
• Household expenditure is affected by expected growth rate
through stock prices and bank loan amount.
• Capital spending by firms is affected by expected growth rate
directly and bank loan amount.
 Household expenditure
= 0.54*labor income + 0.02*stock price + 0.15*loan amount to household
– 0.36*loan interest rate
 Capital spending by firms
= 9.0*firm profit + 0.65*expected growth rate – 1.52*(loan interest rate - CPI)
+ 0.77*loan amount to firms
15
Monetary Policy
• Monetary policy is a simplified Taylor rule.
R(t) = 0.957*R(t-1) + 0.042*output gap(t)
• Since we focus on the average nominal GDP
growth and standard deviation in nominal GDP,
price/inflation development is exogenously
determined.
• We could extend the model to treat
price/inflation as an endogenous variable.
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3. Simulations and Results
•
•
•
•
•
•
•
•
Simulation procedure
“Bubble economy shock”
Macro-prudential measures
Simulation 1: Fixed duration
Simulation 2: Various policy strength
Simulation 3: Recognition lag
Simulation 4: Use of reference indicators
Assessment of resilience of financial system
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Simulation Procedure
• First, we create “the bubble economy” by
producing “shocks” in expected growth rate in
order to mimic the actual bubble economy.
• Then, we implement counterfactual
simulations by using the identified “shocks”
and macro-prudential policy measures.
• We will look at the average nominal GDP
levels and the standard deviations for both
baseline and counterfactual cases.
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“Bubble Economy”
• As a “baseline” scenario, we create “bubble economy.”
• The expected growth rate is affected by the potential
and actual GDP. If the actual GDP is better than the
potential, the optimistic expectations are built.
 Expected growth rate
= 0.77*potential GDP growth rate + 0.1*actual GDP growth rate + shock(t)
 Shock(t)
= iid shock(t) + cumulative growth shock(t)
 Cumulative growth shock(t)
= 0.2*(cumulative growth shock(t-1) + (actual GDP growth rate - potential GDP
growth rate) )*I(t), where I(t) = { 1 if the economy is in expansion phase, -1
otherwise}.
The parameter of cumulative growth shock (0.2) is set so that the simulation recreates the economic
fluctuations during the bubble period in Japan.
The duration of I(t) is fixed (4 years) to mimic the actual bubble economy.
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Macro-prudential measures
• We use five macro-prudential measures as
follows.
 Time varying capital requirement (countercyclical Capital Buffer, CCB):
Regulatory capital adequacy ratio is raised when credit-to-GDP ratio is
above a certain threshold.
 Credit growth restriction: Restrictions on YoY growth in both household
and corporate lending when credit growth is above a certain threshold.
 Corporate Loan-To-Value (LTV) regulation: Restrictions on YoY growth in
corporate lending when corporate LTV ratio is above a certain threshold.
 Retail LTV regulation: Restrictions on YoY growth in household lending
when retail LTV ratio is above a certain threshold.
 Debt-To-Income (DTI) regulation: Restrictions on YoY growth in household
lending when DTI ratio is above a certain threshold.
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Simulation 1: Fixed duration (1)
• First, we run a simulation in which we use the macroprudential measures with fixed duration (1Y, 2Y, 3Y and 4Y).
• For 1Y case, we activate CCB in the first year of the
“bubble” economy, then stop it in the second year. For 2Y,
we activate CCB for the first 2 years and stop it in the third
year (= the policy duration is 2 years). and so forth…For 4 Y
case, we activate CCB for an entire “bubble” period.
• How much should we increase the level of CCB? We
increase the ratio by ¼ σ (=historical standard deviation) as
a benchmark case.
• The same “strengths” are used for other macro-prudential
measures: reductions in bank loan amount growth by ¼ σ
for the total loan, the household loan, and the corporate
loan.
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Simulation 1: Fixed duration (2)
•
•
•
The baseline and policy simulations are “visualized” as follows.
As shown, the baseline scenario shows “big swings.”
With macro-prudential measures, such “big swings” are repressed, but the
degrees of the policy effects are different.
tril. yen
490
480
470
460
Baseline
Retail LTV and DTI regulations
450
Corporate LTV regulation
440
Credit growth restrictions
Time-varying capital requirement
430
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15 years
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Simulation 1: Fixed duration (3)
•
•
•
•
The vertical line is the difference in the
standard deviations of nominal GDP -7
b/w the baseline and the policy
implementation.
The horizontal line is the difference in
the average nominal GDP b/w the
baseline and the policy implementation.
Trade-off: Both the average and the
standard deviation decline. This is
because macro-prudential measures do
not push up the economy during the
recession period while they do push
down the economy during the bubble
period.
Different effects: the effects of various
measures are different. The credit
growth restriction is powerful but has a
large trade-off. The time-varying capital
requirement is less powerful but has a
small trade-off.
nominal GDP, avg., tril. yen
-6
-5
-4
-3
-2
-1
1 year →
2 years →
0
-1
-2
3 years →
4 years →
Retail LTV and DTI regulations
-3
-4
Corporate LTV regulation
-5
Credit growth restrictions
-6
Time-varying capital requirement
-7
st. dev., tril. yen
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Simulation 2: Various policy strength
nominal GDP, avg., tril. yen
• Stronger policy measures have
-12
larger impacts. The standard
deviations are more reduced
when the stronger measures are
implemented.
• Trade-offs are more distinct for
stronger policy measures. The
stronger policy measures reduce
the standard deviations, but at
the same time reduce the
average nominal GDP.
-10
-8
-6
-4
-2
weaker (1/8σ)→
0
-2
benchmark (1/4σ)→
-4
stronger (1/2σ)→
Retail LTV and DTI regulations
Corporate LTV regulation
Credit growth restrictions
-6
-8
-10
Time-varying capital requirement
-12
st. dev., tril. yen
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Simulation 3: Recognition lag
•
•
•
•
st. dev., tril. yen
1
The impacts of policy measures
depend on the timing of the policy
implementation.
← 5th year
However, it is difficult to recognize the
nominal GDP, avg., tril. yen
state of the economy accurately.
Without accurate recognition, the -3
-2
-1
0
effective policy cannot be
implemented. This is a typical “real
time issue” for policy implementation.
-1
The most effective timing of policy
Retail LTV and DTI regulations
implementation is the third year of
the expansion for most policy
1st year ↑
measures i.e. lower variation and
Corporate LTV regulation
higher average GDP.
-2
It is harmful to implement measures
Credit growth restrictions
after the “bubble economy”. Such
actions exacerbate the recession
(upper left).
Time-varying capital requirement
1
← 4th year
← 3rd year
← 2nd year
-3
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Simulation 4: Use of reference indicators
•
•
•
•
In a realistic situation, we do not
know when the “bubble economy ”
begins and how long the “bubble
nominal GDP, avg., tril. yen
economy” continues.
Policy makers need to monitor
-5
-4
-3
-2
“reference indicators” to recognize
90%tile→
Retail LTV regulation
the state of the economy and decide
Corporate LTV regulation
when they activate the policy
measures.
Credit growth restrictions
In the simulations, we assume that
Time-varying capital requirement
measures are implemented when the
reference indicators deviate from
DTI regulation
their historical trends by 70 or 90
percent confidence intervals.
The levels of the reference indicators
matter for the impacts of the policy
70%tile→
measures.
st. dev., tril. yen
1
-1
0
1
-1
-2
-3
-4
-5
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Resilience of Financial System
•
•
•
Another objective of macro-prudential
policy measures is to build greater
resilience of the financial system.
Average Tier 1 capital ratios under any
policy measures are higher over the
financial cycle regardless of which
policy measure is implemented.
Due to higher capital level, loan
amounts during the recession period
are higher than those during the
baseline case (red circle in RHS chart).
(1) Tier 1 capital ratio
deviation from the baseline, % pts
0.4
0.3
0.2
0.1
0.0
Retail LTV and
DTI regulations
Corporate LTV
regulation
Credit growth
restrictions
Time-varying
capital requirement
(2) Loan amount
deviation from the baseline, tril. yen
10
0
-10
Retail LTV and DTI regulations
-20
Corporate LTV regulation
-30
Credit growth restriction
Time-varying capital requirement
-40
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 years
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4. Discussions and Extensions
• Summary of results
• Discussions of the current analysis
• Possible future extensions
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Summary of Results
• We analyzed the impacts of five macro-prudential
policy measures by using a stress test model
(FMM).
• We found the following results.
(1) There are trade-offs between the average economic growth and reduction in
the fluctuation of business cycles, depending on macro-prudential measures.
(2) Each macro-prudential measures have different impacts on the average
economic growth and the fluctuation of business cycles according to the
Japanese economic structure.
(3) “Real time” issue, the lags between recognition of the state of the economy
and implementation of policy measures, is crucial for effectiveness of macroprudential policy measures.
(4) Macro-prudential policy measures can help contribute to more stable financial
intermediation by raising the resilience of the financial system against risks.
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Discussions
• Model
 The model is an econometric model with simple economic structure and not
DSGE. It is subject to the Lucas critique. Coherent DSGE model would be
preferable, but might be too complicated to be used for policy simulations.
• Policy measures
 We do not know the appropriate size of the policy measures. In monetary
policy analysis, the Taylor rule is a benchmark. We do not have such a
benchmark among macro prudential policy measures.
 We do not know the appropriate combinations of macro prudential policu
measures.
 The policy impacts are asymmetric. The macro prudential policy measures are
used to check the overheating of the economy, but not stimulate the economy
in the recession period.
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Possible Future Extensions
• The combined impacts of monetary policy and
various macro-prudential measures.
• The impacts of macro-prudential measures to
different “shocks”.
• The impacts of liquidity policy measures.
• The appropriate reference indicators.
• Policy simulations with DSGE models.
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References
Aiyar, S., C. W. Calomiris, and T. Wieladek, 2012, “Does macropru leak? Evidence from a UK policy
experiment,” Bank of England Working Paper, No. 445.
Angelini, P., S. Neri, and F. Panetta, 2011, “Monetary and macroprudential policies,” Banca d’Italia
Working Papers, No. 801.
Bank of Japan, Financial System Report.
Christensen, I., C. Meh, and K. Moran, 2011, “Bank Leverage Regulation and Macroeconomic
Dynamics,” Bank of Canada Working Paper, 2011-32.
Goodhart, C. A. E., A. K. Kashyap, D. P. Tsomocos, and A. P. Vardoulakis, 2012, “Financial
Regulation in General Equilibrium,” NBER Working Paper Series, 17909.
Ishikawa, A., K. Kamada, Y. Kurachi, K. Nasu, and Y. Teranishi, 2012, “Introduction to the Financial
Macro-econometric Model,” Bank of Japan Working Paper Series, No. 12-E-1.
Kawata, H., Y. Kurachi, K. Nakamura, and Y. Teranishi, 2013, “Impact of Macroprudential Policy
Measures on Economic Dynamics: Simulation Using a Financial Macro-econometric Model,”
Bank of Japan Working Paper Series, No. 13-E-3.
Kitamura, T., S. Kojima, K. Nakamura, K. Takahashi, and I. Takei, 2014, “Macro Stress Testing at the
Bank of Japan,” BOJ Reports and Research Papers.
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Thank you !
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