[PPT] - Macroprudential policies for a small open economy Aino Silvo Fabio PowerPoint Presentation

SLIDE 1

Macroprudential policies for a small open economy

Aino Silvo Fabio Verona

Bank of Finland, Monetary Policy and Research Department aino.silvo@bof.fi fabio.verona@bof.fi

3rd Research Conference of the CEPR Network on Macroeconomic Modelling and Model Comparison Frankfurt am Main, 13-14 June 2019

The views expressed are ours and do not necessarily reflect the views of the Bank of Finland

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The need of macro models ...

... for macroprudential policy simulations

The global financial crisis added financial frictions to the agenda of macroeconomic research At the same time, new tasks such as macroprudential supervision, required central banks to employ structural models that facilitate the evaluation of macroprudential policies These requirements set in motion the development of the Aino 3.0 model, which builds on its predecessor – the Aino 2.0 model1 From the point of view of Finland, monetary policy is exogenous and macroprudential is an active domestic policy

1 Kilponen, Ripatti, Orjasniemi and Verona (2016), “The Aino 2.0 model”, Bank of

Finland Research Discussion Paper 16/2016

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Macroprudential policy simulations

Do macroprudential policies help making the economy more stable (i.e., less volatile)? Are borrower-based instruments (LTV ratio) more effective in stabilizing the economy than capital-based tools (risk weights)? They stabilize the “financial” cycle at the expenses of larger real business cycle fluctuations

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Features of the Finnish housing market

The housing sector

◮ Residential construction: an important driver of the Finnish business

cycle

◮ 2/3 of aggregate household wealth is in residential real estate

Household indebtedness rate: 128%

◮ Considered as one of the key macroeconomic vulnerabilities in the

Finnish economy

Transmission of monetary policy

◮ Direct interest rate channel through variable lending rates ◮ Indirect balance sheet channel through collateral values and lending

spreads

Homeownership rate: 67%

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The Aino 3.0 model

SLIDE 6

The Aino 3.0 model

Households

Iacoviello (AER 2005) Patient / savers, share ωh = 0.66, discount at rate β P

◮ Consume, work, hold houses, save in bonds and bank deposit

Impatient / borrowers, share 1−ωh, discount at rate β I < β P

◮ Consume, work, hold houses, borrow from banks to finance the housing

purchase

◮ (Always binding) collateral constrained: the loan amount (BLH,new

t+1

) is a fraction (θ H

t ) of the market value of the new housing being purchased

BLH,new

t+1

≤ θ H

t PH t

Ht+1 −
1−δ H

Ht

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The Aino 3.0 model

Multiperiod mortgage loans

Kydland, Rupert and Sustek (IER 2016) and Garriga, Kydland and Sustek (RFS 2017) Stock of outstanding debt: BLH

t+1 = (1−γt)BLH t +BLH,new t+1

where γt ≤ 1 is the effective amortisation rate Period mortgage payment: MPt =

γt +(1−τr

t )r H t−1

BLH

t where r H t

is the (variable) interest rate on home loans and τr

t is the tax

deduction on the interest rate Effective amortisation rate: γt+1 =

1−

BLH,new

t+1

BLH

t+1

(γt)α +

BLH,new

t+1

BLH

t+1 κ

where 0 < κ ≤ 1 is the initial amortisation rate of new loans and 0 ≤ α ≤ 1 governs the evolution of the effective amortisation rate

◮ α = 0 and κ = 1 yields the one-period loan framework ◮ α = 1 gives the constant amortisation (decaying coupon) framework

with an amortisation rate γt = κ

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The Aino 3.0 model

Banks

Gerali, Neri, Sessa and Signoretti (JMCB 2010) (Exogenous) bank capital requirement: the bank has to pay a cost whenever the capital-to-risk-weighted-assets ratio K b/

BLNFC +ΦHBLH

is different from the target value vb

t

◮ Mandatory capital requirement: vb

t = vb = 8%(+2.5%)

◮ Countercyclical capital buffer: vb

t = vb + χv

Bt

Yt − B Y

◮ Risk-weight requirement associated with mortgage loans: ΦH

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The Aino 3.0 model

Banks

Monopolistic competition and sticky loan rates: different interest rate pass-through for NFC and housing mortgage loans ˜ r NFC

t

= c11˜ r NFC

t−1 +c21Et˜

r NFC

t+1 +c31 ˜

Rb

t −c41˜

εNFC

t

˜ r H

t = c12˜

r H

t−1 +c22Et˜

r H

t+1 +c32 ˜

Rb

t −c42˜

εH

t

˜ Rb

t = ˜

Rt −c5

˜

kb

t+1 − ˜

bl

reg t+1 − ˜

vb

t

˜

bl

reg t+1 = c6 ˜

bl

NFC t+1 +c7

φ H

t + ˜

bl

H t+1

Note: ˜

Rt is the “ECB” interest rate, which is exogenous

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Steady state

Variable Data† Model C / Y 0.76 0.77 I / Y 0.27 0.27 X / Y 0.54 0.54 MC / Y 0.17 0.18 MI / Y 0.40 0.40 MX / Y 0.51 0.51 housing investment / Y 0.09 0.10 non-financial corporations loans-to-gdp ratio 1.47 1.47 mortgage loans-to-gdp ratio 1.77 1.40 spread (corporate loan rate - risk-free rate), annual (%) 1.55 1.55 spread (mortgage loan rate - risk-free rate), annual (%) 1.62 1.62

† Sample period: 1996Q1 – 2018Q3

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Macroprudential policy simulations

Do macroprudential policies help making the economy more stable (i.e., less volatile)? Are borrower-based instruments (LTV ratio) more effective in stabilizing the economy than capital-based tools (risk weights)? Two simulation scenarios

◮ House demand shock which generates a boom in house prices ◮ Anticipated and permanent low for long Euro Area monetary policy

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Macroprudential policy simulations

House demand shock (ar = 0.75)

LTV=0.9 & RW=0.15; LTV=0.9, RW=0.5; LTV=0.8, RW=0.15

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Macroprudential policy simulations

House demand shock (ar = 0.75)

LTV=0.9 & RW=0.15; LTV=0.9, RW=0.5; LTV=0.8, RW=0.15

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Macroprudential policy simulations

House demand shock (ar = 0.75)

LTV=0.9 & RW=0.15; LTV=0.9, RW=0.5; LTV=0.8, RW=0.15

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Macroprudential policy simulations

House demand shock (ar = 0.75)

Percentage difference with respect to the baseline calibration (LTV=0.90 & RW=0.15) σgdp σh.inv. σh.prices σml/gdp LTV=0.90 & RW=0.50 0.5 0.4 0.3 5.7 LTV=0.80 & RW=0.15 51.7 5.8 1.9

14.9

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Macroprudential policy simulations

Anticipated and permanent low for long Euro Area monetary policy

LTV=0.9 & RW=0.15; LTV=0.9, RW=0.5; LTV=0.8, RW=0.15

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Macroprudential policy simulations

Anticipated and permanent low for long Euro Area monetary policy

LTV=0.9 & RW=0.15; LTV=0.9, RW=0.5; LTV=0.8, RW=0.15

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Macroprudential policy simulations

Anticipated and permanent low for long Euro Area monetary policy

LTV=0.9 & RW=0.15; LTV=0.9, RW=0.5; LTV=0.8, RW=0.15

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What’s next

Better calibration of the elasticities / parameters that drive the dynamics How to quantify the gains / losses Which variables does/should the macroprudential authority care about? Which shocks are important (to respond to)? SVAR / DSGE model for foreign variables to improve e.g. the monetary policy transmission mechanism Effects of other macroprudential policy tools Estimate the model . . .

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Extra slides

SLIDE 21

Aino model vintages @ the Bank of Finland

1st version – Aino – August 2004

◮ Dynamic general equilibrium model à la Gertler (1999) ◮ Calibrated, focus on fiscal and demographic issues

2nd version – eAino – 2010-2012

◮ Open economy DSGE model similar to e.g. the Riksbank Ramses

model or the ECB New Area-Wide model

◮ Estimated, focus on business cycle analysis in a small open economy ◮ Used for forecasting the Finnish economy

3rd version – Aino 2.0 – 2014-2016

◮ eAino augmented with a banking sector and corporate loans ◮ Estimated, used for forecasting the Finnish economy

IRFs to a government spending shock (ar = 0.82) black lines: LTV=0.9 & RW=0.15; red lines: LTV=0.9, RW=0.5; blue lines: LTV=0.8, RW=0.15

SLIDE 31

Model dynamics

IRFs to a LTV ratio shock (ar = 0.90) black lines: LTV=0.9 & RW=0.15; red lines: LTV=0.9, RW=0.5; blue lines: LTV=0.8, RW=0.15

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