Impact of credit risk and profitability on liquidity shocks of Namibian banks: an application of the structural VAR model

Albert V. KAMUINJO; Ravinder RENA; Andrew MAREDZA

doi:10.15637/jlecon.8.3.07

Authors

Albert V. KAMUINJO University of Namibia/NAMIBIA 0000-0001-5606-6891
Ravinder RENA North-West University Business School/REPUBLIC OF SOUTH AFRICA 0000-0002-4156-8693
Andrew MAREDZA University of Mpumalanga/REPUBLIC OF SOUTH AFRICA 0000-0002-4879-8766

10.15637/jlecon.8.3.07

Abstract

The main purpose of this paper was to investigate the relationship between banks’ credit risk and profitability and liquidity shocks in Namibia for the period 2009 to 2018 using the SVAR model. In estimating the SVAR regression model, granger causality, impulse-response functions and forecast error variance decomposition were employed and evaluated. The sample consisted of Namibian commercial banks. By auditing liquidity data between 2009 and 2018, empirical results showed that liquidity risk is caused by a combination of structural shocks. The granger causality, impulse-response functions and forecast error variance decomposition documented that credit risk (non-performing loans) is key factor affecting liquidity conditions in Namibia in the medium to long run. In addition, the empirical results showed that quality earnings (ROA) have minimal impact on liquidity conditions in the short run. Reforming assets quality policies and earnings quality policies can be valuable policy tools to minimize liquidity shortages and avoid insolvent banks in Namibia.

Keywords:

credit risk Namibian Banking Industry camels liquidity shocks liquidity risk

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References

AFFES, Z. & HENTATI-KAFFEL, R. (2017). Predicting US banks bankruptcy: Logit versus Canonical Discriminant analysis. (29th Australasian Finance and Banking Conference 2016 organised by The University of New South Wales. Sydney. p. 1-32).

ALTMAN, E.I. (1977). Predicting performance in the savings and loan association industry. Journal of Monetary Economics, 3:443-466.

AMISANO, G. & GIANNINI, C. (1997). Topics in Structural VAR Econometrics, 2nd ed. New York: Springer.

ANGORA, A. & ROULET, C., (2011). The use of a Basel III liquidity ratio to predict bank financial distress. University of Limoges, Working Paper.

BAEK, S., BALASUBRAMANIAN, S.K. & LEE, K.Y. (2015). Capital structure and monitoring bank failure. Journal of Accounting and Finance, 15(4): 95-107.

BANTI, C. & PHYLAKTIS, K. (2019). Global liquidity, house prices and policy response. Journal of Financial Stability, 43 (C): 79-96.

BARNICHON, R. & BROWNLEES, C. (2018). Impulse response estimation by smooth local projections. http://dx.doi.org/10.2139/ssrn.2892508. [Date accessed: 10/12/2019]

BARTH, J., DAN BRUMBAUGH, R., SAUERHAFT, D. & WANG, G.H.K. (1985). Thrift-Institution failure: causes and policy issues. (Proceedings 68 organized by Federal Reserve Bank of Chicago, Chicago. p. 380-395).

BASEL COMMITTEE ON BANKING SUPERVISION (2008). Principles for Sound Liquidity Risk Management and Supervision. Consultative document.

BERRIOS, M. R. (2013). The relationship between bank credit risk and profitability and liquidity. The International Journal of Business and Finance Research, 7 (3) :105-118.

BONFIN, D. & KIM, M. (2017). Liquidity risk and collective moral hazard, http://dx.doi.org/10.2139/ssrn.2163547. [Date accessed: 21/11/2018].

CASU, B., PIETRO, F. & TRUJILLO-PONCE, A. (2017). Liquidity creation and bank capital in the Eurozone, http://dx.doi.org/10.2139/ssrn.2828619. [Date accessed: 18/07/2020].

COLE, R.A. & WU, Q. (2014). Hazard versus probit in predicting U.S. bank failures: a regulatory perspective over two crises. http://dx.doi.org/10.2139/ssrn.1460526. [Date accessed: 20/04/2020]

DEMIRGUC-KUNT, A. (1989). Deposit-Institution failure: A review of empirical literature. Economic Review, 25 (Q IV): 2-18.

DISTINGUIN, I., ROULET, C. & TARAZI, A. (2013). Bank Regulatory Capital and Liquidity: Evidence from U.S. and European publicly traded banks.

http://dx.doi.org/10.2139/ssrn.1884811. [Date accessed: 20/04/2020].

FLORI, A., GIANSANTE, S. & PAMMOLLI, F. (2016). Peer-group detection of banks and resilience to distress, IMT Lucca EIC Working Paper Series.

GAUTAM, J., JOSHI, N., SINGH, S. & KUMAR, D. (2014). Analyzing performance of banks & predicting bank failure, http://dx.doi.org/10.2139/ssrn.2441567. pdf Date of access: 11 Jul. 2020.

GHURTSKAIA, K. & LEMONJAVA, G. (2016). A study of relationship between liquidity and profitability in Georgian banking sector. International Journal of Science and Research, 7 (4):1609-1613.

GIUSTINIANI, A. & THORNTON, J. (2011). Post-crisis financial reform: where do we stand? Journal of Finance Regulation and Compliance, 19 (4): 323-336.

GOBAT, J., YANASE, M. & MALONEY, J. (2014). The Net Stable Funding Ratio: Impact and Issues for Consideration, IMF Working Paper, U.S.

GOTTSCHALK, J. (2001). An Introduction into the SVAR Methodology: Identification, Interpretation and Limitations of SVAR models. Kiel working paper, Kiel Inst. of World Economics.

GOWRI, M. & RAMYA, G. (2013). An empirical study on banking sector with the use of CAMEL model, Sona Global Management Review, 8 (1): 10-20.

GUPTA, R. (2014). An analysis of Indian public sector banks using CAMEL approach, Journal of Business and Management, 16 (1): 94-102.

HAJJA, A.A.Y. & HUSSAIN, H. (2015). Impact of credit risk (NPLs) and capital on liquidity risk of Malaysian banks. (Proceedings of the International Conference on Accounting Studies (ICAS) organized by Universiti Utara Malaysia, Johor Bahru, Johor, p. 252-256).

HOSSAIN, K., KHAN, A. & SADIQUE, S. (2018). Basel III and perceived resilience of banks in the BRICS economies, Applied Economics, 50 (19): 2133-2146.

HORVATH, R., SEIDLER, J., & WEILL, L., (2014). Bank Capital and Liquidity Creation: Granger-

Causality Evidence. Journal of Financial Research, 45 (3): 341-361.

ISANZU, J.N. (2016). Analysis using CAMEL model: evidence from large commercial banks in Tanzania, International journal of research in commerce & management, 7 (1): 71-75.

ISHAG, A., KARIM, A., ZAHEER, A. & AHMED, S. (2015). Evaluation performance of commercial banks in Pakistan: An application of CAMEL Model, Army Public College of Management Sciences, Pakistan.

KANDRAC, J. (2014). Modelling the causes and manifestation of bank stress: an example from the financial crisis, Applied Economics, 46 (35): 4290-4301.

KAPAN, T. & MINOIU, C. (2017). Balance sheet strength and bank lending: Evidence from the Global Financial Crisis. Journal of Banking & Finance, 92 (C): 35-50.

KARRI, H.K., MEGHANI, K. & MISHRA, B.M. (2015). A comparative study on financial performance of public sector banks in India: An analysis on CAMEL model, Arabian Journal of Business and Management Review, 4 (8): 18-34.

KOWANDA, D., PASARIBU, R.B.F. & FIRDAUS, M. (2014). Financial distress prediction on public listed banks in Indonesia stock exchange. (The 3rd International Congress on Interdisciplinary Behaviour and Social Science organized by ICIBSOS, Dynasty Resort Kuta, Bali. p. 364-370).

KUMAR, K.A. & MURTY, A.V. (2017). Financial performance of selected public and private sector banks based on CAMEL model with reference to Indian banking sector, International Journal in Management and Social Science, 5 (4): 2321-1784.

LALLOUR, A. & MIO, H. (2016). Do we need a stable funding ratio? Banks’ funding in the global financial crisis. Staff Working Paper, Bank of England.

LE, T. (2017). Financial soundness of Vietnamese commercial banks: An CAMELS approach. http://dx.doi.org/10.2139/ssrn.3068529. [Date accessed: 13/05/2020].

MAKINEN, M. & SOLANKO, L. (2017). Determinants of bank closures: Do changes of CAMEL variables matter? The Bank of Finland Institute for Economies in Transition, BOFIT Discussion Papers.

MARTIN, D. (1977). Early warning of bank failure. Journal of Banking Finance, 1 (C): 249 –276.

MAZREKU, I., & MORINA, F. (2016). Banking supervision and evaluation with CAMELS: a case study in Kosovo. http://dx.doi:org/10.2139/ssrn.2819932. [Date accessed: 20/06/2017].

MAJUMDER, T.H. & RAHMAN, M.M. (2016). A CAMEL Model analysis of selected banks in Bangladesh, International Journal of Business and Technopreneurship, 6 (2): 233-266.

NABILAH, N.F. & KHUSHIRI, A. (2018). The relationship between risk and performance of CityBank Singapore Limited. http://dx.doi.org/10.2139/ssrn.3302079. [Date accessed: 11/04/2020].

NAMIBIA STATISTICS AGENCY (2019). Gross Domestic Product. Windhoek: National Statistics Agency.

NURAZI, R. & USMAN, B. (2016). Bank stock returns in responding the contribution of fundamental and macroeconomic effects. Journal of Economics and Policy, 9 (1): 134-149.

OLIVEIRA, A., MARTINS, F.V. & BRANDAO, E. (2015). Bank failure and the financial crisis: an econometric analysis of U.S. banks, http://dx.doi: 10.2139/ssrn.269888. [Date accessed: 14/07/2019].

PANIGRAHI, A.K. (2014). Relationship of working capital with liquidity, Profitability and solvency: A case study of ACC limited. Asian Journal of Management Research, 4 (2): 308-322.

PAPANIKOLAOU, N. (2017). A dual early warning model of bank distress, Economic Letters, 162 (C):127-130.

PAPANIKOLAOU, N. & Wolff, C.C.P. (2015). Does the CAMEL bank ratings system follow a pro-cyclical pattern, CEPR Working Paper 10965, London.

PRADHAN, R.S. & SHRESTHA, D. (2016). Impact of liquidity on banking profitability in Nepalese commercial banks. http://dx.doi.org/10.2139/ssrn.2793458. [Date accessed: 11/04/2020].

SIMS, C.A. (1980). Macroeconomics and Reality. Econometric, 48 (1): 1–48.

SINKEY, J.F. (1975). A multivariate statistical analysis of the characteristics of problem banks, The Journal of Finance, 30 (1): 21 -36.

SIRONI, A. (2018). The evolution of banking regulation since the financial crisis: a critical assessment. http://dx.doi: 10.22409/economica.15i1.p54. [Date accessed: 24/01/2020].

SJAHRIL, R., PRIHARTA, A., PAREWANGI, A.M. & HERMIYETTI, H. (2014). Modeling Financial Distress: The case of Indonesian Banking Industry. https://ssrn.com/abstract=3053731. [Date accessed: 10/05/2020].

SRINIVASAN, P. & SAMINATHAN, Y.P. (2016). A Camel Model Analysis of Public, Private and Foreign Sector Banks in India, Pacific Business Review International, 8 (9): 45-57.

TATOM, J.A. AND HOUSTON, R. (2011). Predicting failure in the commercial banking industry, https://mpra.ub.uni-muenchen.de/34608. [Date accessed: 11/09/2016].

TRIPATHI, D., MEGHANI, K. & MAHAJAN, S. (2014). Financial Performance of Axis Bank and Kotak Mahindra Bank in the post reform era: Analysis on CAMEL model. International Journal of Business Quantitative Economics and Applied Management Research, 1(2): 108-141.

ROSE, P. AND HUDGINS, S. (2008). Bank Management and Financial Services. 7th Ed. New York: McGraw-Hill.

VAROTTO, S. (2011). Liquidity risk, credit risk, market risk and bank capital. International Journal of Managerial Finance, 7(2): 134-152.

VENKATESH, J. & SURESH, C. (2014). Comparative performance evaluation of selected commercial banks in Kingdom of Bahrain using CAMELS method. http://dx.doi: 10.2139/ssrn.2418144. [Date accessed: 10/07/2018].

VOUSINAS, G.L. (2018). Analysing the financial performance of Greek systemic banks during crisis: An application of CAMELS rating system. http://dx.doi:10.2139/ssrn.3163324[Date accessed: 10/07/2018].

WEI, W. (2013). An empirical analysis of relationship between imports and export of China’s foreign-invested enterprises based on vertical specialization. http://dx.doi: 10.1016/B978-0-85709-446-9.50006-X. [Date accessed: 16/07/2018].

YU, W., JU’E, G. & YOUMIN, X. (2008). Study on the dynamic relationship between economic growth and China energy based on cointegration analysis and impulse response function. China Population, Resources and Environment, 18(4): 056 -061.