Modelling the Sensitivity of Zimbabwean Commercial Banks’ Non-performing Loans to Shocks in  Macro-economic Variables and Micro-economic Variables: (2009-2014)

Jacob Muvingi; Kudzai Sauka; David Chisunga; Crispen Chirume

doi:doi:10.11648/j.ijebo.20170504.12

| Peer-Reviewed

Modelling the Sensitivity of Zimbabwean Commercial Banks’ Non-performing Loans to Shocks in Macro-economic Variables and Micro-economic Variables: (2009-2014)

Jacob Muvingi, Kudzai Sauka, David Chisunga, Crispen Chirume

Published in International Journal of Economic Behavior and Organization (Volume 5, Issue 4)

Received: 27 April 2017 Accepted: 2 June 2017 Published: 21 October 2017

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

This paper used complementary panel data models that are fixed effect regression model and panel vector auto regression model. The study was motivated by the hypothesis that both macroeconomic and microeconomic variables have an effect on the loan quality. The first part of the research was to determine the specific macro and microeconomic variables that give rise to the non-performing loans (NPLs) using fixed effect regression model. The empirical findings of this study provide evidence that nonperforming loans depends on macro and micro economic variables, the trend analysis of Zimbabwean commercial banks’ shows an upward movement of over the period of study. The study found out that Gross domestic product (GDP), Inflation, loan deposit ratio and bank size had a statistical significant effect on the level of non-performing loans (NPLs). The second part was mainly to model the dynamic relationship of all the variables that were found to affect non-performing loans (NPLs); this was done through impulse response analysis based on PANEL VAR model. One standard shock to credit growth will be greatly felt in the sixth year, whereas of size of the bank will have a great negative impulse in the seventh year.

Published in	International Journal of Economic Behavior and Organization (Volume 5, Issue 4)
DOI	10.11648/j.ijebo.20170504.12
Page(s)	92-99
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Non-performing Loans, Panel Data, Panel VAR, Individuality, Impulse Response

References

[1]	Shingjergji, A. (2013), The Impact of Bank Specific Variables on the Non-Performing Loans Ratio in the Albanian Banking System, Research Journal of Finance and Accounting 4, 7, 2222-1697.
[2]	Katchova, A. (2013) Panel Data Models, https://www.scribd.com/doc/297814618/Panel-Data-Models-pdf, viewed, Feb 3 2016.
[3]	Fofack, H. (2005). NPLs in sub-Saharan Africa: Causal Analysis and Macroeconomic Implications. World Bank Policy Research Working Paper No. 3769, November.
[4]	Hu, J., Li, H., & Chiu, H. (2007). Ownership and non-performing loans: Evidence from Taiwan banks. The Developing Economies, 42, 405-420.
[5]	Jimenez, G. & Saurina, J. (2005). Credit cycles, credit risk, and prudential regulation. Banco de Espana, January 2005.
[6]	Messai, A. S. and Jouini, F. (2013). Micro and macro determinants of nonperforming loans. International. Journal of economics and financial issues, 3(4), 852-860.
[7]	Louzis, D. P., Vouldis, A. T. & Metaxas, V. L., (2011). Macroeconomic and bank-specific determinants of non-performing loans in Greece: A comparative study of mortgage, business and consumer loan portfolios. Journal of Banking & Finance, 36(4), 1012–1027.
[8]	Nkusu, M. (2011): Nonperforming Loans and Macro Financial Vulnerabilities in Advanced Economies. IMF Working Paper 11/161 (Washington: International Monetary Fund).
[9]	Rajan, R. & C. Dhal, S. C. (2003): NPLs and Terms of Credit of Public Sector Banks in India: An Empirical Assessment. Reserve Bank of India, Occasional Papers, 24(3), 81-121,
[10]	Salas, V., & Saurina, J., (2002): Credit risk in two institutional regimes: Spanish commercial and savings banks. Journal of Financial Services Research 22, 203–224.
[11]	RBZ 2009-2015; Monetary policy statements.
[12]	RBZ 2010- 2014; bank supervisory reports.
[13]	Klein, N., (2013): Non-Performing Loans in CESEE: Determinants and Impact on Macroeconomic Performance. IMF Working Paper WP/13/72 (European Department: International Monetary Fund).
[14]	Quagliarello, M., (2007). Banks’ riskiness over the business cycle: a panel analysis on Italian intermediaries. Applied Financial Economics 17, 119–138.
[15]	Reinhart, C., & Rogoff, K. (2010). From Financial Crash to Debt Crisis. NBER Working Paper 15795.
[16]	Rajan, R. & Dhal, S. (2003). Non-performing loans and terms of credit of public sectorbanks in India: an empirical assessment. Reserve Bank of India Occasional Paper 24, 81–121.
[17]	Gujarati, D. N, (2004): Basic Econometrics, 4th Ed., McGraw-Hill Companies.

Cite This Article

Plain Text BibTeX RIS

APA Style

Jacob Muvingi, Kudzai Sauka, David Chisunga, Crispen Chirume. (2017). Modelling the Sensitivity of Zimbabwean Commercial Banks’ Non-performing Loans to Shocks in Macro-economic Variables and Micro-economic Variables: (2009-2014). International Journal of Economic Behavior and Organization, 5(4), 92-99. https://doi.org/10.11648/j.ijebo.20170504.12

Copy | Download

ACS Style

Jacob Muvingi; Kudzai Sauka; David Chisunga; Crispen Chirume. Modelling the Sensitivity of Zimbabwean Commercial Banks’ Non-performing Loans to Shocks in Macro-economic Variables and Micro-economic Variables: (2009-2014). Int. J. Econ. Behav. Organ. 2017, 5(4), 92-99. doi: 10.11648/j.ijebo.20170504.12

Copy | Download

AMA Style

Jacob Muvingi, Kudzai Sauka, David Chisunga, Crispen Chirume. Modelling the Sensitivity of Zimbabwean Commercial Banks’ Non-performing Loans to Shocks in Macro-economic Variables and Micro-economic Variables: (2009-2014). Int J Econ Behav Organ. 2017;5(4):92-99. doi: 10.11648/j.ijebo.20170504.12

Copy | Download

@article{10.11648/j.ijebo.20170504.12,
  author = {Jacob Muvingi and Kudzai Sauka and David Chisunga and Crispen Chirume},
  title = {Modelling the Sensitivity of Zimbabwean Commercial Banks’ Non-performing Loans to Shocks in  Macro-economic Variables and Micro-economic Variables: (2009-2014)},
  journal = {International Journal of Economic Behavior and Organization},
  volume = {5},
  number = {4},
  pages = {92-99},
  doi = {10.11648/j.ijebo.20170504.12},
  url = {https://doi.org/10.11648/j.ijebo.20170504.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijebo.20170504.12},
  abstract = {This paper used complementary panel data models that are fixed effect regression model and panel vector auto regression model. The study was motivated by the hypothesis that both macroeconomic and microeconomic variables have an effect on the loan quality. The first part of the research was to determine the specific macro and microeconomic variables that give rise to the non-performing loans (NPLs) using fixed effect regression model. The empirical findings of this study provide evidence that nonperforming loans depends on macro and micro economic variables, the trend analysis of Zimbabwean commercial banks’ shows an upward movement of over the period of study. The study found out that Gross domestic product (GDP), Inflation, loan deposit ratio and bank size had a statistical significant effect on the level of non-performing loans (NPLs). The second part was mainly to model the dynamic relationship of all the variables that were found to affect non-performing loans (NPLs); this was done through impulse response analysis based on PANEL VAR model. One standard shock to credit growth will be greatly felt in the sixth year, whereas of size of the bank will have a great negative impulse in the seventh year.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Modelling the Sensitivity of Zimbabwean Commercial Banks’ Non-performing Loans to Shocks in  Macro-economic Variables and Micro-economic Variables: (2009-2014)
AU  - Jacob Muvingi
AU  - Kudzai Sauka
AU  - David Chisunga
AU  - Crispen Chirume
Y1  - 2017/10/21
PY  - 2017
N1  - https://doi.org/10.11648/j.ijebo.20170504.12
DO  - 10.11648/j.ijebo.20170504.12
T2  - International Journal of Economic Behavior and Organization
JF  - International Journal of Economic Behavior and Organization
JO  - International Journal of Economic Behavior and Organization
SP  - 92
EP  - 99
PB  - Science Publishing Group
SN  - 2328-7616
UR  - https://doi.org/10.11648/j.ijebo.20170504.12
AB  - This paper used complementary panel data models that are fixed effect regression model and panel vector auto regression model. The study was motivated by the hypothesis that both macroeconomic and microeconomic variables have an effect on the loan quality. The first part of the research was to determine the specific macro and microeconomic variables that give rise to the non-performing loans (NPLs) using fixed effect regression model. The empirical findings of this study provide evidence that nonperforming loans depends on macro and micro economic variables, the trend analysis of Zimbabwean commercial banks’ shows an upward movement of over the period of study. The study found out that Gross domestic product (GDP), Inflation, loan deposit ratio and bank size had a statistical significant effect on the level of non-performing loans (NPLs). The second part was mainly to model the dynamic relationship of all the variables that were found to affect non-performing loans (NPLs); this was done through impulse response analysis based on PANEL VAR model. One standard shock to credit growth will be greatly felt in the sixth year, whereas of size of the bank will have a great negative impulse in the seventh year.
VL  - 5
IS  - 4
ER  -

Copy | Download

Author Information

Jacob Muvingi

Department of Financial Engineering, Harare Institute of Technology, Harare, Zimbabwe
Kudzai Sauka

Department of Financial Engineering, Harare Institute of Technology, Harare, Zimbabwe
David Chisunga

Department of Forensic Accounting and Auditing, Harare Institute of Technology, Harare, Zimbabwe
Crispen Chirume

Department of Forensic Accounting and Auditing, Harare Institute of Technology, Harare, Zimbabwe

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Jacob Muvingi, Kudzai Sauka, David Chisunga, Crispen Chirume. (2017). Modelling the Sensitivity of Zimbabwean Commercial Banks’ Non-performing Loans to Shocks in Macro-economic Variables and Micro-economic Variables: (2009-2014). International Journal of Economic Behavior and Organization, 5(4), 92-99. https://doi.org/10.11648/j.ijebo.20170504.12

Copy | Download

ACS Style

Jacob Muvingi; Kudzai Sauka; David Chisunga; Crispen Chirume. Modelling the Sensitivity of Zimbabwean Commercial Banks’ Non-performing Loans to Shocks in Macro-economic Variables and Micro-economic Variables: (2009-2014). Int. J. Econ. Behav. Organ. 2017, 5(4), 92-99. doi: 10.11648/j.ijebo.20170504.12

Copy | Download

AMA Style

Jacob Muvingi, Kudzai Sauka, David Chisunga, Crispen Chirume. Modelling the Sensitivity of Zimbabwean Commercial Banks’ Non-performing Loans to Shocks in Macro-economic Variables and Micro-economic Variables: (2009-2014). Int J Econ Behav Organ. 2017;5(4):92-99. doi: 10.11648/j.ijebo.20170504.12

Copy | Download

@article{10.11648/j.ijebo.20170504.12,
  author = {Jacob Muvingi and Kudzai Sauka and David Chisunga and Crispen Chirume},
  title = {Modelling the Sensitivity of Zimbabwean Commercial Banks’ Non-performing Loans to Shocks in  Macro-economic Variables and Micro-economic Variables: (2009-2014)},
  journal = {International Journal of Economic Behavior and Organization},
  volume = {5},
  number = {4},
  pages = {92-99},
  doi = {10.11648/j.ijebo.20170504.12},
  url = {https://doi.org/10.11648/j.ijebo.20170504.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijebo.20170504.12},
  abstract = {This paper used complementary panel data models that are fixed effect regression model and panel vector auto regression model. The study was motivated by the hypothesis that both macroeconomic and microeconomic variables have an effect on the loan quality. The first part of the research was to determine the specific macro and microeconomic variables that give rise to the non-performing loans (NPLs) using fixed effect regression model. The empirical findings of this study provide evidence that nonperforming loans depends on macro and micro economic variables, the trend analysis of Zimbabwean commercial banks’ shows an upward movement of over the period of study. The study found out that Gross domestic product (GDP), Inflation, loan deposit ratio and bank size had a statistical significant effect on the level of non-performing loans (NPLs). The second part was mainly to model the dynamic relationship of all the variables that were found to affect non-performing loans (NPLs); this was done through impulse response analysis based on PANEL VAR model. One standard shock to credit growth will be greatly felt in the sixth year, whereas of size of the bank will have a great negative impulse in the seventh year.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Modelling the Sensitivity of Zimbabwean Commercial Banks’ Non-performing Loans to Shocks in  Macro-economic Variables and Micro-economic Variables: (2009-2014)
AU  - Jacob Muvingi
AU  - Kudzai Sauka
AU  - David Chisunga
AU  - Crispen Chirume
Y1  - 2017/10/21
PY  - 2017
N1  - https://doi.org/10.11648/j.ijebo.20170504.12
DO  - 10.11648/j.ijebo.20170504.12
T2  - International Journal of Economic Behavior and Organization
JF  - International Journal of Economic Behavior and Organization
JO  - International Journal of Economic Behavior and Organization
SP  - 92
EP  - 99
PB  - Science Publishing Group
SN  - 2328-7616
UR  - https://doi.org/10.11648/j.ijebo.20170504.12
AB  - This paper used complementary panel data models that are fixed effect regression model and panel vector auto regression model. The study was motivated by the hypothesis that both macroeconomic and microeconomic variables have an effect on the loan quality. The first part of the research was to determine the specific macro and microeconomic variables that give rise to the non-performing loans (NPLs) using fixed effect regression model. The empirical findings of this study provide evidence that nonperforming loans depends on macro and micro economic variables, the trend analysis of Zimbabwean commercial banks’ shows an upward movement of over the period of study. The study found out that Gross domestic product (GDP), Inflation, loan deposit ratio and bank size had a statistical significant effect on the level of non-performing loans (NPLs). The second part was mainly to model the dynamic relationship of all the variables that were found to affect non-performing loans (NPLs); this was done through impulse response analysis based on PANEL VAR model. One standard shock to credit growth will be greatly felt in the sixth year, whereas of size of the bank will have a great negative impulse in the seventh year.
VL  - 5
IS  - 4
ER  -

Copy | Download