The impact of the credit crunch on the Polish economy · Motivation Model Calibration/estimation...

Motivation Model Calibration/estimation Results Conclusions

The impact of the credit crunch on the Polisheconomy

Michaª Brzoza-Brzezina and Krzysztof Makarski

Emerging Markets Workshop, Banco de España

21-22 September 2009

M. Brzoza-Brzezina and K. Makarski Credit crunch


Plan of the Presentation

1 Motivation

2 Model

3 Calibration/estimation

4 Results

5 Conclusions



Question

What was the role of the banking sector in the propagation ofthe �nancial crisis?

How much of GDP decline in Poland was generated by�nancial frictions?



Short history of the crisis in Poland

Credit crunch

Increase in lending restrictions (and spreads)Decline in loans to households and �rms

Decline in external demand

E�ect: Decrease of exports and lower growth of GDP



Short history of the crisis in PolandLending restrictions and credit creation in Poland. (Source: NBP)

Fig 2 Wykres 1

Page 1

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

I 2004 I 2005 I 2006 I 2007 I 2008 I 20090

5000

10000

15000

20000

25000

30000

35000

40000

45000

LTV_Enterprise LTV_Mortgage Enterprise loans Household loans




Credit crunch






Short history of the crisis in PolandDecline in external demand: GDP yoy in the EA

-5%

-4%

-3%

-2%

-1%

0%

1%

2%

3%

4%

1q2004 1q2005 1q2006 1q2007 1q2008 1q2009




Credit crunch






Short history of the crisis in PolandExport and GDP in Poland

Fig 1 Wykres 3

Page 1

-10

-5

0

5

10

15

2004 2005 2006 2007 2008 2009

GDP Exports

%




1 Motivation

2 Model


4 Results

5 Conclusions



Literature

The interest in models with �nancial frictions is growingrapidly.

Two main strands of literature:- Bernanke et al. (1999): �nancial accelerator through prices(interest rates). The cost of monitoring increases spreadsbetween the lending and the risk free rate.- Iacoviello (2005): �nancial accelerator through quantities.Collateral puts a constraint on the volume of lending.

Work in progress!!!



Key Features

Small open economy new Keynesian model of business cycle.

Non-neutrality of monetary policy due to: - Nominal rigidities -prices and wages are set according to a Calvo scheme (Calvo,1983)

Monetary Policy - Taylor rule (Taylor, 1993)

Financial frictions - following Iacoviello (2005) and Gerali et al.(2008):

collateral constraints on HH and enterprises,stochastic shocks to credit constraints and spreads.



Structure

Patient HH

Impatient HH

Entrepreneurs

Final good producers

Banks Central bank ROW

Housing/ Capital good producers

Deposits

Labour

OMO

Loans

Wholes. goods

Retailers

Interm. goods



Households

There are two types of households: patient, P , and impatient,I .

In equilibrium patient HHs save and impatient HHs borrow.

Patient HHs maximise utilityE0 ∑t(βP)tu(cPt , χP

t , nPt , εt),subject to the budget constraint.

Impatient households maximise utilityE0 ∑t(βI )tu(c It , χI

t , nIt , εt),

subject to the budget constraint and the credit constraintRH

L,tLHt ≤ mH

t Et

[Pχ,t+1 (1− δχ) χI

t

]where mH

t ∼ AR(1) denotes LTV ratio. Thus to get a loanthey need collateral (housing).



Entrepreneurs (wholesale good producers)

Do not work, run �rms. Sell their product in a competitivemarket.

Are impatient (βE = βI ).Own capital and use it as collateral for loans.

Maximise utilityE0 ∑t(βE )tu(cEt , εt),

subject to the �ow of funds and the credit constraintRF

L,tLFt ≤ mF

t Et [Pk,t+1 (1− δk) kt ]where mF

t ∼ AR(1) denotes LTV ratio.

Other (intermediate and �nal good) producers are standard.



Banking

Monopolistically competitive markets - nominal rigidities(Calvo) slow down the adjustment of the interest rates.

Banks have access to the domestic and international interbankmarket.

Deposit banks- Collect deposits from patient households and deposit them inthe interbank market. zD,t ∼ AR(1) a�ects the spreadbetween the interbank rate Rt and the HHs deposit rate RH

D,t .

Lending banks- Take loans in the interbank market and grant loans toimpatient HHs and �rms. zL,t ∼ AR(1) a�ects the spreadbetween the interbank rate Rt and the lending rate RL,t .- Borrowing constraints tied to housing (HHs) and capital(�rms)




1 Motivation

2 Model


4 Results

5 Conclusions



Calibration/estimation

Parameters that are widely discussed in the literature andsteady state parameters were calibrated.

Remaining parameters Bayesian Estimation,

Quarterly data 1q1997 - 1q2009,

14 time series:

PL: GDP, C, I, G, Inf, WIBOR3M, HH loans, Enterprise loans,Spread on HH loans, Spread on enterprise loans, Spread on HHdeposits.EUR: GDP, Inf, EURIBOR3M.




1 Motivation

2 Model


4 Results

5 Conclusions



HHs loans LTV shock

10 20 30 40−1

0

1

2

3x 10

−3 gdp

10 20 30 40−5

0

5

10

15x 10

−3 c

10 20 30 40−2

0

2

4x 10

−3 i

10 20 30 40−2

0

2

4

6x 10

−4 pi

10 20 30 400

0.5

1x 10

−3 R

10 20 30 40−5

0

5

10

15x 10

−4 l_f

10 20 30 40−0.05

0

0.05

0.1

0.15l_h

10 20 30 40−5

0

5x 10

−5 spread_f_l

10 20 30 40−5

0

5x 10

−5 spread_h_l



HHs loans spread shock

10 20 30 40−4

−2

0

2x 10

−4 gdp

10 20 30 40−2

−1

0

1x 10

−3 c

10 20 30 40−4

−2

0

2

4x 10

−4 i

10 20 30 40−10

−5

0

5x 10

−5 pi

10 20 30 40−15

−10

−5

0

5x 10

−5 R

10 20 30 40−2

0

2

4x 10

−4 l_f

10 20 30 40−15

−10

−5

0

5x 10

−3 l_h

10 20 30 40−2

−1

0

1x 10

−5 spread_f_l

10 20 30 40−1

0

1

2

3x 10

−3 spread_h_l



Historical realisations of selected shocks

10 20 30 40-0.04

-0.03

-0.02

-0.01

0

0.01v_y_star

10 20 30 40-0.01

-0.005

0

0.005

0.01v_pi_star

10 20 30 40-6

-4

-2

0

2x 10

-3 v_R_star

10 20 30 40-0.015

-0.01

-0.005

0

0.005

0.01v_z_h_d

10 20 30 40-0.015

-0.01

-0.005

0

0.005

0.01v_z_h_l

10 20 30 40-0.01

-0.005

0

0.005

0.01v_z_f_l

10 20 30 40-0.3

-0.2

-0.1

0

0.1

0.2v_m_f

10 20 30 40-0.2

-0.1

0

0.1

0.2v_m_h



Simulation

The goal of the simulation is to separate out the impact of thecredit crunch on GDP,

We run counterfactual simulation: hypothetical GDP without�nancial shocks,

Selected shocks are set to zero in the period 3q2008-1q2009,

We group shocks - shocks to spreads and to LTVs,

To obtain a better visualisation of the impact of the creditcrunch we make an unconditional forecast for 20 quarters (itshould be treated as a visualisation of the strength of theimpact rather than a real forecast),

Comparison with the impact of external shocks.



Scenario 1: No spread shocks

0 10 20 30 40 50 60 70-0.02

-0.015

-0.01

-0.005

0

0.005

0.01

0.015

0.02

0.025

Counterfactual scenario and forecastData and forecastImpact



Scenario 2: No LTV shocks

0 10 20 30 40 50 60 70-0.02

-0.015

-0.01

-0.005

0

0.005

0.01

0.015

0.02

0.025




Scenario 3: No �nancial shocks

0 10 20 30 40 50 60 70-0.02

-0.015

-0.01

-0.005

0

0.005

0.01

0.015

0.02

0.025




Scenario 4: No external shocks

0 10 20 30 40 50 60 70-0.02

-0.015

-0.01

-0.005

0

0.005

0.01

0.015

0.02

0.025





1 Motivation

2 Model


4 Results

5 Conclusions



Conclusions

Model allows to identify the impact of the �nancial turmoil onthe Polish economy,

The role of direct credit restrictions is greater than the role ofthe price of credit (spreads),

Financial shocks have a quantitatively important e�ect,nevertheless their impact is smaller than the impact of externalshocks,

Still work in progress: moment conditions, endogeneity of thebanking sector, entry-exit of �rms etc.


Thank you.

The impact of the credit crunch on the Polish economy · Motivation Model Calibration/estimation...

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