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Keep your friends close and your enemies closer – the case of monetary policy and financial imbalances

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Published/Copyright: April 1, 2021

Abstract

The global financial crisis (GFC) has shown that monetary policy focused on a stable price level may negatively affect the stability of the financial system. Therefore, achieving price and financial stability using interest rates as the main tool is difficult. In this paper, we analyse how often monetary policy strengthened imbalances in the financial system in 20 countries from 1999Q1 to 2020Q2. To this end, we compare monetary policy stance with a novel financial imbalance index (FII). We find that monetary policy is material in aggravating financial imbalances mostly in Eurozone countries. We attribute this finding to the ECB’s “too loose, too long” monetary policy and to difficulties with applying single monetary policies in countries with different economic conditions and in different phases of credit and financial cycles. Our results point to a need for a proactive macroprudential policy in the environment of low interest rates.

JEL Classification: E37; E52; E58; E61

Funding source: Narodowe Centrum Nauki

Award Identifier / Grant number: 2017/25/N/HS4/00323

Funding statement: This work was supported by the Polish National Science Centre (NCN) under grant number 2017/25/N/HS4/00323.

Acknowledgment

The authors would like to thank Agnieszka Paluch, Paweł Smaga and SENAMEK seminar participants at the Warsaw School of Economics for their helpful discussions and comments.

  1. Conflict of interest: The opinions expressed herein are those of the authors and do not reflect those of the associated institutions.

Appendix A Min-max-based FIIs for specific countries

Figure A.1 
Min-max-based FIIs for specific countries.
Figure A.1

Min-max-based FIIs for specific countries.

Appendix B Simulation of weights and corresponding AUC levels

Figure A.2 
Simulation of weights and corresponding AUC levels. Note: The horizontal axis represents the particular weight simulation. The height of a given colour in a given simulation represents the weight level (vertical axis). The red line shows the AUC level of the FII index with the weights set in the given simulation.
Figure A.2

Simulation of weights and corresponding AUC levels. Note: The horizontal axis represents the particular weight simulation. The height of a given colour in a given simulation represents the weight level (vertical axis). The red line shows the AUC level of the FII index with the weights set in the given simulation.

Appendix C Monetary policy and financial imbalances (CDF-based FII)

Figure A.3 
Monetary policy and financial imbalances (CDF-based FII). Note. The threshold value of the FII for the CDF-based version is equal to 0.6169 (according to Table 3).
Figure A.3

Monetary policy and financial imbalances (CDF-based FII). Note. The threshold value of the FII for the CDF-based version is equal to 0.6169 (according to Table 3).

Appendix D Results of policy interactions depending on FII variable modification

Table A.1

Results of policy interactions depending on FII variable modification.

Country Robustness type Conflicting Complementary Neutral
BE NFC credit 15.12 % 3.49 % 81.40 %
HH credit 4.65 % 3.49 % 91.86 %
Without real economy 11.63 % 3.49 % 84.88 %
CH NFC credit 6.98 % 0.00 % 93.02 %
HH credit 0.00 % 0.00 % 100.00 %
Without real economy 6.98 % 0.00 % 93.02 %
CN NFC credit 24.42 % 1.16 % 74.42 %
HH credit 2.33 % 0.00 % 97.67 %
Without real economy 13.95 % 1.16 % 84.88 %
CZ NFC credit 16.74 % 2.33 % 70.93 %
HH credit 11.63 % 4.65 % 83.72 %
Without real economy 13.95 % 5.81 % 80.23 %
DE NFC credit 19.77 % 2.33 % 77.91 %
HH credit 4.65 % 1.16 % 94.19 %
Without real economy 10.47 % 2.33 % 87.21 %
DK NFC credit 0.00 % 24.42 % 75.58 %
HH credit 0.00 % 22.09 % 77.91 %
Without real economy 11.63 % 24.42 % 63.95 %
ES NFC credit 29.07 % 4.65 % 66.28 %
HH credit 25.58 % 4.65 % 69.77 %
Without real economy 29.07 % 4.65 % 66.28 %
FI NFC credit 16.28 % 2.33 % 81.40 %
HH credit 8.14 % 3.49 % 88.37 %
Without real economy 19.77 % 4.65 % 75.58 %
FR NFC credit 18.60 % 2.33 % 79.07 %
HH credit 4.65 % 2.33 % 93.02 %
Without real economy 13.95 % 3.49 % 82.56 %
HU NFC credit 2.33 % 29.07 % 68.60 %
HH credit 0.00 % 27.91 % 72.09 %
Without real economy 2.33 % 25.58 % 72.09 %
IT NFC credit 22.09 % 4.65 % 73.26 %
HH credit 17.44 % 4.65 % 77.91 %
Without real economy 22.09 % 4.65 % 73.26 %
JP NFC credit 9.30 % 8.14 % 82.56 %
HH credit 3.49 % 0.00 % 96.51 %
Without real economy 8.14 % 5.81 % 86.05 %
KR NFC credit 5.81 % 0.00 % 94.19 %
HH credit 0.00 % 0.00 % 100.00 %
Without real economy 1.16 % 0.00 % 98.84 %
NL NFC credit 1.16 % 0.00 % 98.84 %
HH credit 3.49 % 2.33 % 94.19 %
Without real economy 3.49 % 2.33 % 94.19 %
NO NFC credit 11.63 % 6.98 % 81.40 %
HH credit 0.00 % 5.81 % 94.19 %
Without real economy 15.12 % 8.14 % 76.74 %
PL NFC credit 16.28 % 9.30 % 74.42 %
HH credit 9.30 % 8.14 % 82.56 %
Without real economy 8.14 % 6.98 % 84.88 %
PT NFC credit 24.42 % 4.65 % 70.93 %
HH credit 24.42 % 4.65 % 70.93 %
Without real economy 31.40 % 4.65 % 63.95 %
SE NFC credit 13.95 % 9.30 % 76.74 %
HH credit 0.00 % 4.65 % 95.35 %
Without real economy 10.47 % 11.63 % 77.91 %
UK NFC credit 11.63 % 22.09 % 66.28 %
HH credit 11.63 % 17.44 % 70.93 %
Without real economy 9.30 % 16.28 % 74.42 %
US NFC credit 31.40 % 5.81 % 62.79 %
HH credit 19.77 % 5.81 % 74.42 %
Without real economy 19.77 % 5.81 % 74.42 %
  1. Note: The table presents policy interactions depending on particular modifications of the FII variables. For NFC credit, we calculate the credit-to-GDP gap based only on corporate loans rather than based on all loans provided to the nonfinancial sector. For HH credit, we calculate the credit-to-GDP gap based only on household loans. The FII without the real economy includes only variables from the financial sector, global economic conditions and financial markets. For these calculations, we apply the min-max approach for variable normalization and weights of the particular sector that maximize the AUC.

Appendix E Descriptive statistics of the FII depending on variable modifications

Table A.2

Descriptive statistics of the FII depending on variable modifications.

Statistic NFC credit HH credit Without the real economy
Financial sector weight 0.38 0.38 0.4
Financial market weight 0.12 0.18 0.24
Global economic conditions weight 0.4 0.3 0.36
Real economy weight 0.1 0.14 X
AUC 0.8271 0.8857 0.8525
FII* – level that minimizes the loss function 0.5436 0.5340 0.5441

Appendix F ROC depending on FII variable modifications

Figure A.4 
ROC depending on FII variable modifications. Note: The charts present the ROC of the FII for crisis prediction. For these calculations, we apply the min-max approach to variable normalization and weights of the particular sector that maximize the AUC.
Figure A.4

ROC depending on FII variable modifications. Note: The charts present the ROC of the FII for crisis prediction. For these calculations, we apply the min-max approach to variable normalization and weights of the particular sector that maximize the AUC.

Appendix G Comparison of the official and shadow interest rates

Figure A.5 
Comparison of the official and shadow interest rates. Note: The charts present official and shadow interest rates for three main central banks. In calculating shadow rates, we use Wu and Xia’s (2016) approach.
Figure A.5

Comparison of the official and shadow interest rates. Note: The charts present official and shadow interest rates for three main central banks. In calculating shadow rates, we use Wu and Xia’s (2016) approach.

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Published Online: 2021-04-01
Published in Print: 2021-11-30

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