Bubbles, Crashes and the Financial Cycle: The Limits to Credit Growth, Slides presented at WEHIA 2015 Nice

The Big Questions Eurace@Unibi Model Simulation Results Conclusions

Bubbles, Crashes & the Financial Cycle:

The Limits to Credit Growth

Sander van der Hoogand Herbert Dawid Chair for Economic Theory and Computational Economics

Bielefeld University

WEHIA

Sophia Antipolis, 21-23 May 2015

The Big Questions Eurace@Unibi Model Simulation Results Conclusions

The Big Questions

I

Which micro- or macro-prudential banking regulations are beneficial to financial stability?

I

Prevention and mitigation policies:

I

How to prevent severe downturns from occurring?

I

How to mitigate the cumulative economic losses?

Sander van der Hoog Bubbles, Crashes & the Financial Cycle

Activity Role Agent

Agent Role Activity

E u r a c e @ U n i b i Bank

InvGoodFirm Household

asset demand savings decision

Financial Market (index bond)

ECB Monetary policy

Gov Policy maker Investor

ConsGoodFirm

Consumer

Cons.

Goods Market (local malls) cgood demand consumption choice

Producer cgood supply posted prices labor supply

reservation wage

Employee labor demand

wage schedule Labor Market (search &

matching)

Creditor credit supply Debtor

rank credit risk

credit demand rank interest Credit Market (credit rationing)

Employer

Investor Producer

Capital Goods Market igood supply vintage menu posted prices

igood demand vintage choices

The Big Questions Eurace@Unibi Model Simulation Results Conclusions

Mechanisms in the model Capital Adequacy Requirement Reserve Ratio Requirement

Mechanisms in the model

1. Probability of Default (PD): Internal Risk-Based approach (IRB)

2. Interest rate rule for commercial banks

3. Debt-equity transformation: Insolvency / Illiquidity

4. Dividend payout rule

5. Credit rationing rule

6. Capital Adequacy Requirement (CAR)

7. Central Bank Reserve Ratio Requirement (RRR)

8. Future research: Capital Conservation Buffers & Counter-Cyclical Capital Buffers:

The Big Questions Eurace@Unibi Model Simulation Results Conclusions

Mechanisms in the model Capital Adequacy Requirement Reserve Ratio Requirement

Probability of Default, Interest rate rule

1. Firm’s default probability

PD^f_t =max{0.0003,1−e^−νDft/Eft}, ν=0.1

2. Interest rate offered by bankbto firmi r_t^bf=r^ECB

1+λ^B·PD_t^f+ε_t^b

, ε_t^b∼U[0,1]

r^ECB=0.01

λ^B=3: penalty rate for high-risk firm, uniform across banks ε_t^b: bank’s ideosyncratic operating costs

Sander van der Hoog Bubbles, Crashes & the Financial Cycle

The Big Questions Eurace@Unibi Model Simulation Results Conclusions

Mechanisms in the model Capital Adequacy Requirement Reserve Ratio Requirement

Capital Adequacy Requirement

1. Risk-exposure of credit request (Expected Loss at Default):

rwa^b_it=PDit·Lit. and RWA^b_t =

F

∑

i=1 K(i)

∑

k=0

PDkt·Lkt, (1) 2. Constraint6:Capital Adequacy Requirement(CAR)

RWA^b_t ≤α·E_t^b, α≥0 (2) 3. Risk-exposure "budget" of the bank:

V_t^b:=α·E_t^b−RWA^b_t (3) 4. Risk-constrained loan demand:

`¯^b_it=







L_it ifPD_it·L_it≤V_t^b 0 if 0≤V_t^b≤PDit·Lit

0 ifV_t^b<0.

(4)

The Big Questions Eurace@Unibi Model Simulation Results Conclusions

Mechanisms in the model Capital Adequacy Requirement Reserve Ratio Requirement

Reserve Ratio Requirement

I Constraint7:Reserve Ratio Requirement (RRR)

M_t^b≥β·Dep^b_t, β∈[0,1] (5)

I Excess liquidity "budget" of the bank:

W_t^b:=M^b_t −β·Dep^b_t (6)

I Loan granted: risk- and liquidity constrained credit request

`^b_i,t=







`¯<sup>b</sup><sub>i,t</sub> ifW<sub>t</sub><sup>b</sup>≥`¯^b_i,t φ·`¯<sup>b</sup><sub>i,t</sub> if 0≤W<sub>t</sub><sup>b</sup>≤¯`^b_i,t 0 ifW_t^b<0.

(7)

Possibility ofcredit rationing:{φ:W_t^b−φ·`¯<sup>b</sup><sub>i,t</sub>=0} →φ=W<sub>t</sub><sup>b</sup>/`¯^b_i,t

I Illiquid banks stop lending to all firms (bank lending channel)

I Risky firms cannot get loans (borrower’s balance sheet channel)

Sander van der Hoog Bubbles, Crashes & the Financial Cycle

The Big Questions Eurace@Unibi Model Simulation Results Conclusions

Amplitude of recessions

Prevention and mitigation: The Limits to Credit Growth

Parameter sensitivity analysis

0 200 400 600 800 1000

2000300040005000

Months

Eurostat_output

alfa_20_gamma

1.0 2.0 4.0 8.0 16.0 32.0

α-sensitivity: Cap. Adq. Req.

I Default:α=32 (3%)

I Lower:amplitude of recessions increases

0 200 400 600 800 1000

2000300040005000

Months

Eurostat_output

beta_20_gamma_10_alfa

0.01 0.02 0.05 0.10 0.20 0.50

β-sensitivity: Reserve Req.

I Default:β=0.05 (5%)

I Higher:amplitude of recessions decreases

0 20 40 60 80 100 120

6000 6500 7000 7500 8000 8500 9000

Recessions and expansions

Quarters

Output

The Big Questions Eurace@Unibi Model Simulation Results Conclusions

Amplitude of recessions

Prevention and mitigation: The Limits to Credit Growth

Parameter sensitivity analysis

α-sensitivity: Cap. Adq. Req.

I Basel III: 4.5−10.5%

α=22.2−9.5

I Lower:amplitude of recessions increases

−8000−6000−4000−2000

Parameter 1

full_amplitude_recession

0.00 0.01 0.02 0.05 0.10 0.20 0.50 0.90 0.99 1.00

−8000−6000−4000−2000

β-sensitivity: Reserve Req.

I EU:β=0.01, US:β=0.10, CA:

β=0

I Higher:amplitude of recessions decreases

The Big Questions Eurace@Unibi Model Simulation Results Conclusions

Amplitude of recessions

Prevention and mitigation: The Limits to Credit Growth

Parameter sensitivity analysis 2D-grid

alpha

beta

−1200

−1100

−1000

−900

−800

−700

4.0 6.0 8.0 10.0 12.0 16.0 20.0 24.0 28.0 32.0

0.000.020.100.250.350.450.901.00

Sander van der Hoog Bubbles, Crashes & the Financial Cycle

The Big Questions Eurace@Unibi Model Simulation Results Conclusions

Amplitude of recessions

Prevention and mitigation: The Limits to Credit Growth

Prevention and mitigation policies: The Limits to Credit Growth

Proposed regulations to limit excesses in banking (eg. Admati & Hellwig, 2013):

A. Default regulation: Capital ratio 12.5%, Reserve ratio 10%.

B. Banning bank dividend payouts→Increases bank equity capital

C. Using non-risk-weighted capital ratios→Prevents abuse of risk-weights ("risk-weight management optimization")

D. Cutting-off funding to all financiallyunsound firms→Prevents leverage

E. Cutting-off funding toPonzi firmsonly→Prevents further leverage

F. Combined effect of BCD→Does it help to prevent bubbles?

G. Combined effect of BCE→Does it help to prevent bubbles?

The Big Questions Eurace@Unibi Model Simulation Results Conclusions

Amplitude of recessions

Prevention and mitigation: The Limits to Credit Growth

Prevention and mitigation policies: The Limits to Credit Growth

Comparison across regulations A - G

−3000−2500−2000−1500−1000−500

Parameters

full_amplitude_recession

A B C D E F G

−3000−2500−2000−1500−1000−500

amplitude of recessions (output lost)

−10000−8000−6000−4000−2000

Parameters

full_cumm_loss_recession

A B C D E F G

−10000−8000−6000−4000−2000

cumulative loss of output (amplitude & duration)

Sander van der Hoog Bubbles, Crashes & the Financial Cycle

The Big Questions Eurace@Unibi Model Simulation Results Conclusions

Main Conclusions

I

To prevent large cumulative losses that follow from recessions, it is required to cut-off funding to all financially unsound firms (speculative and Ponzi firms).

I

Mere capital ratios, and increasing them incrementally, do not help to prevent credit bubbles.

I

Imposing strict limits to growth on the excessive supply of

credit seems to work best to mitigate the severity of economic

downturns.

Thank you for your attention!

Model documentation:

www.wiwi.uni-bielefeld.de/lehrbereiche/vwl/etace/Eurace_Unibi/

Papers:

I S van der Hoog & H Dawid (2015):

Bubbles, Crashes and the Financial Cycle, Working Paper Bielefeld University.

I H Dawid, S Gemkow, P Harting, S van der Hoog & M Neugart (2014):

Agent-Based Macroeconomic Modeling and Policy Analysis: The Eurace@Unibi Model. In: S-H Chen, M Kaboudan (Eds), Handbook on Computational Economics and Finance. Oxford University Press.

I H Dawid, S Gemkow, P Harting, S van der Hoog & M Neugart (2012):

The Eurace@Unibi Model: An Agent-Based Macroeconomic Model for Economic Policy Analysis. Working Paper University Bielefeld.

I H Dawid, S Gemkow, P Harting, S van der Hoog & M Neugart (2011):

Eurace@Unibi Model v1.0 User Manual. Working Paper Bielefeld University.

I H Dawid & P Harting (2012):Capturing Firm Behavior in Agent-Based Models of Industry Evolution and Macroeconomic Dynamics, in: G. Bünstorf (Ed), Applied Evolutionary Economics, Behavior and Organizations. Edward Elgar, pp.

103-130.

I H Dawid & M Neugart (2011):Agent-based Models for Economic Policy Design, Eastern Economic Journal 37, 44-50.

The Big Questions Eurace@Unibi Model Simulation Results Conclusions

Outlook & Future research

I Macroprudential regulation

I Systemic risk (SIFIs, SIBs)

I Bank-firm networks

I size effects

I balance sheet contagion

I Empirically-grounded bank behavior

I Credit quotas

I Credit rationing of SMEs

I Tighter integration of Basel III regulation

Sander van der Hoog Bubbles, Crashes & the Financial Cycle

The Big Questions Eurace@Unibi Model Simulation Results Conclusions

Scenario: Capital Adequacy Requirement

Output

0 100 200 300 400 500

1500200025003000

Months

Eurostat_output

alfa

2.0 8.0

Bank activity (α=2)

0 100 200 300 400 500

05101520

Months

Bank_active_multi

Bank_active_none Bank_active_exposure Bank_active_liquidity

Firm activity (α=2)

0 100 200 300 400 500

05101520

Months

Firm_insolvency_SL

Firm_insolvency_S Firm_insolvency_L Firm_illiquidity_S

Firm_illiquidity_L

The Big Questions Eurace@Unibi Model Simulation Results Conclusions

Scenario: Minimum Reserve Requirement

Output

0 100 200 300 400 500

20002200240026002800

Months

Eurostat_output

min_cash_reserve_ratio

0.10 0.50

Bank activity (β=0.50)

0 100 200 300 400 500

05101520

Months

Bank_active_multi

Bank_active_none Bank_active_exposure Bank_active_liquidity

Firm activity (β=0.50)

0 100 200 300 400 500

05101520

Months

Firm_insolvency_SL

Firm_insolvency_S Firm_insolvency_L

Firm_illiquidity_S Firm_illiquidity_L

Sander van der Hoog Bubbles, Crashes & the Financial Cycle

Scenario: Capital Adequacy Requirement Output

0 100 200 300 400 500

1500200025003000

Months

Eurostat_output

alfa

2.0 8.0

Bank activity (α=2)

0 100 200 300 400 500

05101520

Months

Bank_active_multi

Bank_active_none Bank_active_exposure Bank_active_liquidity

Firm activity (α=2)

0 100 200 300 400 500

05101520

Months

Firm_insolvency_SL

Firm_insolvency_S Firm_insolvency_L

Firm_illiquidity_S Firm_illiquidity_L

0 100 200 300 400 500

05000100001500020000

Months

Bank_equity

alfa

2.0 8.0

Bank equity

0 100 200 300 400 500

0.30.40.50.60.70.80.91.0

Months

F_EARatio

alfa

2.0 8.0

Firm fragility

0 100 200 300 400 500

0.0400.0450.0500.0550.0600.0650.070

Months

Firm_mean_interest

alfa

2.0 8.0

Mean interest

Scenario: Minimum Reserve Requirement Output

0 100 200 300 400 500

20002200240026002800

Months

Eurostat_output

min_cash_reserve_ratio

0.10 0.50

Bank activity (β=0.50)

0 100 200 300 400 500

05101520

Months

Bank_active_multi

Bank_active_none Bank_active_exposure Bank_active_liquidity

Firm activity (β=0.50)

0 100 200 300 400 500

05101520

Months

Firm_insolvency_SL

Firm_insolvency_S Firm_insolvency_L

Firm_illiquidity_S Firm_illiquidity_L

0 100 200 300 400 500

5000100001500020000

Months

Bank_equity

min_cash_reserve_ratio

0.10 0.50

Bank equity

0 100 200 300 400 500

0.30.40.50.60.70.80.9

Months

F_EARatio

min_cash_reserve_ratio 0.10 0.50

Firm fragility

0 100 200 300 400 500

0.0450.0500.0550.0600.0650.070

Months

Firm_mean_interest

min_cash_reserve_ratio

0.10 0.50

Mean interest

The Big Questions Eurace@Unibi Model Simulation Results Conclusions

Firm activity

Number of illiquid firms

No constraint

0 100 200 300 400 500

05101520

Months

Firm_insolvency_SL

Firm_insolvency_S Firm_insolvency_L

Firm_illiquidity_S Firm_illiquidity_L

Capital constraint (α=2)

0 100 200 300 400 500

05101520

Months

Firm_insolvency_SL

Firm_insolvency_S Firm_insolvency_L

Firm_illiquidity_S Firm_illiquidity_L

Liquidity constraint (β=0.50)

0 100 200 300 400 500

05101520

Months

Firm_insolvency_SL

Firm_insolvency_S Firm_insolvency_L

Firm_illiquidity_S Firm_illiquidity_L

The Big Questions Eurace@Unibi Model Simulation Results Conclusions

Bank activity

Number of active banks (unconstrained + constrained by equity/liquidity constraint)

No constraint

0 100 200 300 400 500

05101520

Months

Bank_active_multi

Bank_active_none Bank_active_exposure Bank_active_liquidity

Capital constraint (α=2)

0 100 200 300 400 500

05101520

Months

Bank_active_multi

Bank_active_none Bank_active_exposure Bank_active_liquidity

Liquidity constraint (β=0.5)

0 100 200 300 400 500

05101520

Months

Bank_active_multi

Bank_active_none Bank_active_exposure Bank_active_liquidity

Sander van der Hoog Bubbles, Crashes & the Financial Cycle

The Big Questions Eurace@Unibi Model Simulation Results Conclusions

Scenarios: Firm Fragility

Firm E/A-ratio = 1/leverage Capital constraint

0 100 200 300 400 500

0.30.40.50.60.70.80.91.0

Months

F_EARatio

alfa

2.0 8.0

Liquidity constraint

0 100 200 300 400 500

0.30.40.50.60.70.80.9

Months

F_EARatio

min_cash_reserve_ratio 0.10 0.50

The Big Questions Eurace@Unibi Model Simulation Results Conclusions

Prevention and mitigation - Bank dividend payout

−3000−2500−2000−1500−1000−500

Parameters

full_amplitude_recession

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90

−3000−2500−2000−1500−1000−500

amplitude of recessions

−10000−8000−6000−4000−2000

Parameters

full_cumm_loss_recession

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90

−10000−8000−6000−4000−2000

cumulative loss

Sander van der Hoog Bubbles, Crashes & the Financial Cycle

The Big Questions Eurace@Unibi Model Simulation Results Conclusions

Bank accounting

1.Bank profit

π_t^b=r_i^bL^b_i −r^b(

∑

∑

2.Bank cash and reserves

M_t+1^b =M_t^b+ ∆M_h^b+ ∆M_i^b+ (1−τ)max[0,π_t^b]−d^b(1−τ)max[0,π_t^b]

The Big Questions Eurace@Unibi Model Simulation Results Conclusions

Debt-equity transformation

3a.Insolvency bankruptcy

Debt renegotiation is addressed by re-scaling the total debtD^f_t with a debt rescaling parameterϕ.

Target debt is given by:

D^∗=ϕA^f_t with 0≤ϕ≤1. (8) After debt restructuring, the equity of the firm is now positive:

E^∗= (1−ϕ)A^f_t>0. (9) The new debt/equity-ratio is given by the constantD^∗/E^∗=ϕ/(1−ϕ)<1.

Sander van der Hoog Bubbles, Crashes & the Financial Cycle

The Big Questions Eurace@Unibi Model Simulation Results Conclusions

Debt-equity transformation

3b.Illiquidity bankruptcy

Debt-renegotiation is not necessary per se, rescaling of the debt is either based on the level of total assets or on the level of the original debt:

D^∗=

ϕA^f_t ifϕA^f_t ≤D_t^f

ϕD^f_t ifϕA^f_t >D_t^f. with 0≤ϕ≤1. (10) The new debt/equity-ratio is given by the following piece-wise function:

D^∗/E^∗=

ϕ/(1−ϕ) ifϕA^f_t≤D^f_t

ϕ/(A/D−ϕ) ifϕA^f_t>D^f_t. (11)

The Big Questions Eurace@Unibi Model Simulation Results Conclusions

Dividend payout rule

I hR^fi_nR: average revenues over previousnRmonths (nR=3,6,12)

I hΠ^fin_E: average net earnings (after-tax profits) over the lastn_Emonths

hR^fin_R= 1 n_R

n_R−1

∑

i=0

R^f_t−i (12)

hΠ^fi_nE= 1 nE

n_E−1

∑

i=0

Π^f_t−i (13)

I Prevent liquidity hoarding by firms: Liquidity Buffer Stock 4. Dividend payout rule:

Div^f=

d· hΠ^fi4 ifM_t^f≤µ· hR^fi6

hΠ^fi₄ ifM_t^f>µ· hR^fi₆. d=0.7,µ=0.5 (14)

Sander van der Hoog Bubbles, Crashes & the Financial Cycle

The Big Questions Eurace@Unibi Model Simulation Results Conclusions

Exogenous Credit Rationing

5a.Full/Partial credit rationingis based on the (exogenously prescribed, ex ante) constraints of the bank (CAR, CRR).

I Full rationing for CAR constraint:

`¯^b_it=







L_it ifPD_it·L_it≤V_t^b 0 if 0≤V_t^b≤PDit·Lit

0 ifV_t^b<0.

(15)

I Partial rationing ("filling up to constraint") for CAR constraint:

`¯^b_it=







Lit ifPDit·Lit≤V_t^b V_t^b/PDit if 0≤V_t^b≤PDit·Lit

0 ifV_t^b<0.

(16)