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This paper
◮ Assess the impact of the credit cycle affect on the propagation
- f monetary and credit shocks
Amplifying slumps, taming booms: the bitter consequences of high - - PowerPoint PPT Presentation
Amplifying slumps, taming booms: the bitter consequences of high - - PowerPoint PPT Presentation
Amplifying slumps, taming booms: the bitter consequences of high indebtedness Cyril Couaillier, Valerio Scalone Sciences Po Banque de France June 11, 2019 1 / 25 This paper Assess the impact of the credit cycle affect on the propagation
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Motivation
◮ In the aftermath of the GFC, indebtedness has been pointed
as an important amplification factor of the downturn (Jord` a et al. [2013], Mian et al. [2017]);
◮ When agents are on their debt limit, shocks affecting debt
limits can force borrowers to deleverage and further reduce spending:
◮ State effect: shocks are amplified when vulnerability is high
(financial accelerator: Bernanke et al. [1996], Kiyotaki and Moore [1997], Occasionally Binding Constraint: Guerrieri and Iacoviello [2016], Maffezzoli and Monacelli [2015])
◮ Sign effect: shocks with negative effect on debt limit hit more
the economy in absolute terms than shocks of the same size but with opposite sign
◮ Several credit to output ratio transformation display good
properties as early warning indicators of financial crises
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Literature
1 Asymmetry matters to study amplification of shocks and financial vulnerability: Aikman et al. [2016, 2017], Aladangady [2014], Alpanda and Zubairy [2017], Barnichon and Matthes [2016], Bauer and Granziera [2016], Cloyne et al. [2016], Harding and Klein [2018], Hofmann and Peersman [2017], Jord` a et al. [2016], Juselius et al. [2016] 2 Non-linear dynamics related to indebtedness: Barnichon et al. [2016], Carriero et al. [2018], Jord` a et al. [2013, 2015, 2016], Mian et al. [2017] 3 Credit growth is a good early warning indicator of financial crises and their costs Bridges et al. [2017], Jord` a et al. [2011]
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The strategy
◮ Time series analysis with non-linear terms detecting state and
sign effects;
◮ Use of an interaction variable (3y difference in Credit to GDP
Ratio) to capture continuous build-up of vulnerability;
Credit/GDP
◮ Smooth Local Projections (SLP) method by Barnichon and
Brownlees [2018]) to allow for non-linearities and limit noise in the estimation
◮ Two identification strategies
◮ Instrument variable (SVAR-LP) with High Frequency
Identification of monetary shocks from Miranda-Agrippino and Ricco [2018])
◮ Sign restrictions to jointly recover monetary policy and credit
supply shocks
◮ Estimation on quarterly US data (1983-2018)
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The empirical model
For each horizon h = 0...H, the setting is: Yt+h = αh + Σp
ℓ=1Lh,ℓ Xt−ℓ
+ Λh X ✶, ¯
X t−1
+ Φh kt−1Xt−1 + Ψh kt−1X ✶, ¯
X t−1
+ uh,t (1) with Yt vector of endogenous variables, Zt = (Yt, Zt)′ vector of regressors variables, kt scalar interaction variable, X ✶, ¯
X t−1 =
X1,t−1✶X1,t−1< ¯
X1
... Xn,t−1✶Xn,t−1< ¯
Xn
- , ¯
X vector of cutoff values, uh,t vector
- f errors at horizon h. Lh,ℓ Λh Φh Ψh the coefficient matrices at
horizon h.
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Data
◮ Sample for US: 1983Q1-2018Q2 ◮ Endogenous variables
◮ Real GDP growth; ◮ Inflation; ◮ Shadow short term rate (Wu and Xia [2016]), robustness with
1-y gov. bond rate;
◮ Lending rates for HH (30-y mortgage rate) and NFC (Moody’s
BAA Corporate bond yield);
◮ Total credit (loans and debt securities) to HH and NFC, net
flow over stock;
◮ Ratio of credit to households over credit to Non-Financial
Corporations
◮ Exogenous interaction variable: Credit to GDP ratio 3-y
variation, in pp;
Credit/GDP
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Benchmark specification and identification strategy
◮ Process with one lag; ◮ 20 quarters horizon; ◮ Cut-off values for asymmetry: historical mean; ◮ First identification: identification with High Frequency
(Miranda-Agrippino and Ricco [2018])
◮ Robustness and credit shock identification with sign
restrictions
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SVAR-LP for monetary shock
So-called SVAR-LP strategy first proposed in Gertler and Karadi [2015]
- 1. Regress estimated errors up
t of policy rate on a time series of
structural policy shocks to obtain the part of error due to those shocks (ˆ up
t ). We use the series developed by
Miranda-Agrippino and Ricco [2018] built by using the market surprises and taking into account central banks’ private information and informational rigidity
MA&R (2018)
- 2. Regress other reduced form errors uq
t on the fitted policy
errors ˆ up
t to recover the ratios of the impacts on the monetary
policy shock on all the values
- 3. Determine impact to the monetary policy shocks through a
variance covariance decomposition
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Monetary shock - HFI
Figure: Impulse responses to a monetary shock ` a la MA&R
response: Credit flow NFC response: Ratio of HH to NFC credit response: Lending rate NFC response: Credit flow HH response: Shadow policy rate response: Lending rate HH response: GDP response: Inflation 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 −0.5 0.0 0.5 −0.4 −0.2 0.0 0.2 0.4 −20 −10 −0.02 0.00 0.02 −1.0 −0.5 0.0 0.5 1.0 −1.0 −0.5 0.0 0.5 −0.50 −0.25 0.00 0.25 −10 −5 5 quantile 0.1 0.9
Figure: Contractionary shock
response: Credit flow NFC response: Ratio of HH to NFC credit response: Lending rate NFC response: Credit flow HH response: Shadow policy rate response: Lending rate HH response: GDP response: Inflation 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 −0.5 0.0 0.5 −0.4 −0.2 0.0 0.2 −5 5 10 −0.06 −0.04 −0.02 0.00 0.02 0.04 −1.0 −0.5 0.0 0.5 −0.5 0.0 0.5 −0.25 0.00 0.25 0.50 −5 5 10 15 quantile 0.1 0.9
Figure: Expansionary shock
The red (green) lines are the impulses at the 90th (10th) percentile of the credit cycle. Shaded areas represent the bootstrapped 90% confidence intervals. Cumulated IRF for GDP, inflation and credit flows
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Sign restriction identification
- 1. Compute the eigendecomposition of the covariance matrix Γ
so that ΓDΓ′ = ˆ Ω
- 2. draw a set of independent normal vectors and take the
resulting R of their their QR decomposition
- 3. Check whether ΓQ verifies some sign conditions
- 4. If not repeat 2-3
Sign restriction matrix inspired from Gambetti and Musso [2017] and Furlanetto et al. [2017]:
GDP Inflation ST rate HH Rate NFC Rate HH Cr NFC Cr HH/NFC Cr AD + + + + + AS +
- MP
+ +
- HH CS
+ + +
- +
+ NFC CS + + +
- +
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Non-linear effects for monetary policy shock
Strong asymmetric and state-dependent effects
◮ Only recessionary shocks have statistically significant effects
- n income and credit growth;
◮ Recessionary shock are amplified by the credit cycle:
◮ Strong negative and persistent effect on GDP when
vulnerability is high
◮ Spread increases more when vulnerability is high ◮ Effect on credit statistically significant only when vulnerability
is high
◮ Credit channel stronger on households than on firms (HH/NFC
credit ratio decreases when vulnerability is high)
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Monetary shock
Figure: Impulse responses to a monetary shock
response: Credit flow NFC response: Ratio of HH to NFC credit response: Lending rate NFC response: Credit flow HH response: Shadow policy rate response: Lending rate HH response: GDP response: Inflation 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 −0.50 −0.25 0.00 0.25 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 −10 10 −0.03 −0.02 −0.01 0.00 0.01 −1.0 −0.5 0.0 0.5 1.0 −0.5 0.0 0.5 −0.4 −0.2 0.0 0.2 −5 5 10 quantile 0.1 0.9
Figure: Contractionary shock
response: Credit flow NFC response: Ratio of HH to NFC credit response: Lending rate NFC response: Credit flow HH response: Shadow policy rate response: Lending rate HH response: GDP response: Inflation 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 −0.4 0.0 0.4 −0.2 0.0 0.2 0.4 −5 5 10 −0.06 −0.04 −0.02 0.00 0.02 0.04 −1.0 −0.5 0.0 0.5 1.0 −1.0 −0.5 0.0 0.5 −0.2 0.0 0.2 0.4 −5 5 10 15 quantile 0.1 0.9
Figure: Expansionary shock
The red (green) lines are the impulses at the 90th (10th) percentile of the credit cycle. Shaded areas represent the bootstrapped 90% confidence intervals. Cumulated IRF for GDP, inflation and credit flows
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Non-linear effects for credit supply shocks
◮ Larger income effect of contractionary HH credit supply shock
◮ When the credit cycle is high recessionary HH credit shocks
have significant effects on income and HH credit growth for two years;
◮ The effect of recessionary corporate credit shock quickly
vanishes whatever the credit cycle
◮ High credit cycle makes negative shocks on credit flows much
stronger and more persistent
◮ Negative effect of expansionary shocks down the road:
◮ Effect on income growth positive on impact but turns negative
after a few years when vulnerability is high: indebtedness drives down growth, suggesting debt overhang
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Households credit supply shock
Figure: Impulse responses to a household credit shock.
response: Credit flow NFC response: Ratio of HH to NFC credit response: Lending rate NFC response: Credit flow HH response: Shadow policy rate response: Lending rate HH response: GDP response: Inflation 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 −0.75 −0.50 −0.25 0.00 0.25 −0.2 0.0 0.2 −15 −10 −5 5 10 15 −0.02 0.00 0.02 −0.4 0.0 0.4 0.8 −0.50 −0.25 0.00 0.25 0.50 −0.4 −0.2 0.0 0.2 −5 5 10 quantile 0.1 0.9
Figure: Contractionary shock
response: Credit flow NFC response: Ratio of HH to NFC credit response: Lending rate NFC response: Credit flow HH response: Shadow policy rate response: Lending rate HH response: GDP response: Inflation 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 −0.5 0.0 0.5 −0.25 0.00 0.25 −5 5 10 −0.06 −0.04 −0.02 0.00 0.02 0.04 −1.0 −0.5 0.0 0.5 1.0 −1.0 −0.5 0.0 0.5 1.0 −0.4 −0.2 0.0 0.2 0.4 −10 10 quantile 0.1 0.9
Figure: Expansionary shock
The red (green) lines are the impulses at the 90th (10th) percentile of the credit cycle. Shaded areas represent the bootstrapped 90% confidence intervals. Cumulated IRF for GDP, inflation and credit flows
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Corporate credit supply shock
Figure: Impulse responses to a corporate credit shock.
response: Credit flow NFC response: Ratio of HH to NFC credit response: Lending rate NFC response: Credit flow HH response: Shadow policy rate response: Lending rate HH response: GDP response: Inflation 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 −0.4 −0.2 0.0 0.2 −0.2 −0.1 0.0 0.1 0.2 0.3 −5 5 10 15 −0.03 −0.02 −0.01 0.00 0.01 0.02 −0.25 0.00 0.25 0.50 0.75 −0.5 0.0 0.5 −0.2 −0.1 0.0 0.1 0.2 0.3 −5 5 10 quantile 0.1 0.9
Figure: Contractionary shock
response: Credit flow NFC response: Ratio of HH to NFC credit response: Lending rate NFC response: Credit flow HH response: Shadow policy rate response: Lending rate HH response: GDP response: Inflation 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 −0.4 0.0 0.4 0.8 −0.50 −0.25 0.00 0.25 −15 −10 −5 5 −0.025 0.000 0.025 0.050 −1.0 −0.5 0.0 0.5 −1.5 −1.0 −0.5 0.0 0.5 −0.3 0.0 0.3 −10 −5 5 10 quantile 0.1 0.9
Figure: Expansionary shock
The red (green) lines are the impulses at the 90th (10th) percentile of the credit cycle. Shaded areas represent the bootstrapped 90% confidence intervals. Cumulated IRF for GDP, inflation and credit flows
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Robustness
Results are qualitatively robust trying different specifications:
◮ 1-y gov bond rate versus shadow rate ◮ GDP deflator instead of inflation ◮ Credit growth instead of credit flows ◮ Alternative interaction variables
◮ Credit to GDP gap ◮ 3-y change in Debt Service Ratio
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Conclusion
◮ Indebtedness strongly affects the propagation of
economic shocks
◮ High indebtedness is associated with higher sensitivity when
shocks are recessionary
◮ This may explain the good properties of credit to GDP growth
as early-warning indicator of financial crises
◮ Household debt seems more critical than corporate debt,
coherent with financial crises often being real estate-driven
◮ Tentative evidence of debt-overhang when the credit cycle is
at its peak
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High Frequency (Miranda-Agrippino and Ricco [2018])
- 1. An instrument for monetary policy
¯ MPI is built regressing high frequency market surprises in the fourth Federal funds rate future (FF4m) round FOMC announcements on Greenbook forecasts F cb
m xq+j for a vector of macro variables
x and on revisions [F cb
m xq+j − F cb m−1xq+j] (in order to control central bank’s
private information) FF4m = α0 +
3
- j=−1
θjF cb
m xq+j + 2
- j=−1
ϕ[F cb
m xq+j − F cb m−1xq+j] +
¯ MPI m (2)
- 2. The residual of this regression (Monetary Policy instrument) is used to construct
a monthly instrument
- 3. The monthly instrument is regressed on its lagged values to get rid of the
autoregressive component: ¯ MPI t = φ0 +
12
- j=1
φj ¯ MPI t−j + MPIt (3)
Go Back
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Interactions: the Credit to GDP ratio in difference
Go Back to Strategy Go Back to Data
−1.0 −0.5 0.0 0.5 1.0 1960 1980 2000 2020
Date value variable
F3F4_NFPS_GDP_ratio_2_y F3F4_NFPS_GDP_ratio_3_y F3F4_NFPS_GDP_ratio_5_y
Credit to GDP ratio in 2 years difference (blue), 3 years difference (yellow) and 5 years difference (red).
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Literature I
David Aikman, Andreas Lehnert, J Liang, and Michele Modugno. Financial vulnerabilities, macroeconomic dynamics, and monetary policy. Technical report, Finance and Economics Discussion Series, 2016. David Aikman, A Lenhert, Nellie Liang, and Michele Modugno. Credit, financial conditions, and monetary policy transmission. Technical report, Hutchins Center Working Paper, 2017. Aditya Aladangady. Homeowner balance sheets and monetary policy. Technical report, Finance and Economics Discussion Series, 2014. Sami Alpanda and Sarah Zubairy. Household debt overhang and transmission
- f monetary policy. Texas A&M University, mimeo, 2017.
Regis Barnichon and Christian Brownlees. Impulse response estimation by smooth local projections. The Review of Economics and Statistics, 0(0):1–9,
- 2018. doi: 10.1162/rest\ a\ 00778. URL
https://doi.org/10.1162/rest_a_00778. Regis Barnichon and Christian Matthes. Gaussian mixture approximations of impulse responses and the non-linear effects of monetary shocks. Technical report, CEPR Discussion Papers, 2016.
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Literature II
Regis Barnichon, Christian Matthes, and Alexander Ziegenbein. Assessing the non-linear effects of credit market shocks. Technical report, CPER, 2016. Gregory H Bauer and Eleonora Granziera. Monetary policy, private debt and financial stability risks. Technical report, Bank of Canada, 2016. Ben Bernanke, Mark Gertler, and Simon Gilchrist. The financial accelerator and the flight to quality. The Reviews of Economics and Statistics, 1996. Jonathan Bridges, Chris Jackson, and Daisy McGregor. Down in the slumps: the role of credit in five decades of of recessions. Technical report, Bank of England, 2017. Andrea Carriero, Ana Beatriz Galvao, Massimiliano Marcellino, et al. Credit conditions and the effects of economic shocks: Amplification and
- asymmetries. Technical report, Economic Modelling and Forecasting Group,
2018. James Cloyne, Clodomiro Ferreira, and Paolo Surico. Monetary policy when households have debt: new evidence on the transmission mechanism. Technical report, Bank of England, 2016.
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Literature III
Francesco Furlanetto, Francesco Ravazzolo, and Samad Sarferaz. Identification
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(617):311–337, 2017. Luca Gambetti and Alberto Musso. Loan supply shocks and the business cycle. Journal of Applied Econometrics, 32(4):764–782, 2017. Mark Gertler and Peter Karadi. Monetary policy surprises, credit costs, and economic activity. American Economic Journal: Macroeconomics, 7(1): 44–76, 2015. Luca Guerrieri and Matteo Iacoviello. Collateral constraints and macroeconomic asymmetries. Technical report, International Finance Discussion Papers, 2016. Martin Harding and Mathias Klein. Monetary policy and household (de-)
- leveraging. Technical report, DIW Berlin, 2018.
Boris Hofmann and Gert Peersman. Is there a debt service channel of monetary transmission? Technical report, BIS, 2017. ` Oscar Jord` a, Moritz Schularick, and Alan M Taylor. When credit bites back. Journal of Money, Credit and Banking, 45(s2):3–28, 2013.
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Literature IV
` Oscar Jord` a, Moritz Schularick, and Alan M Taylor. Leveraged bubbles. Journal of Monetary Economics, 76:S1–S20, 2015. ` Oscar Jord` a, Moritz Schularick, and Alan M Taylor. The great mortgaging: housing finance, crises and business cycles. Economic Policy, 31(85): 107–152, 2016. scar Jord` a, Moritz Schularick, and Alan M Taylor. Financial crises, credit booms, and external imbalances: 140 years of lessons. Working Paper 16567, National Bureau of Economic Research, December 2011. URL http://www.nber.org/papers/w16567. Mikael Juselius, Claudio Borio, Piti Disyatat, and Mathias Drehmann. Monetary policy, the financial cycle and ultra-low interest rates. Technical report, BIS Working Paper, 2016. Nobuhiro Kiyotaki and John Moore. Credit cycle. The Journal of Political Economy, 1997. Marco Maffezzoli and Tommaso Monacelli. Deleverage and Financial Fragility. CEPR Discussion Papers 10531, C.E.P.R. Discussion Papers, April 2015. URL https://ideas.repec.org/p/cpr/ceprdp/10531.html.
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Literature V
Atif Mian, Amir Sufi, and Emil Verner. Household debt and business cycles
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