Bank Financial Stability and International Oil Prices: Evidence from Listed Russian Public Banks

ABSTRACT

Using data on 17 listed public banks from Russia over the period 2008 to 2016, we analyze whether international oil prices affect the bank stability in an oil-dependent country. We resort to a Pool Mean Group (PMG) estimator, and we show that an increase in oil prices has a long-run positive effect on Russian public banks stability. While positive oil-price shocks contribute to bank stability in the long run, an opposite effect is recorded for negative shocks. However, no significant impact is documented in the short run. Our findings are robust to different bank stability specifications and different samples.

KEYWORDS:

• bank financial stability
• international oil prices
• bank valuation
• Russian public banks
• panel data estimation

JEL CLASSIFICATION:

• C33
• G21
• Q43

Acknowledgements

This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS – UEFISCDI, project number PN-III-P1-1.1-TE-2019-0436.

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Notes

1. Since January 2014, the Russian ruble lost in two years around 50% of its value against the US dollar (Dreger et al. 2016). Given that about half of total corporate debt of Russia was in 2016 denominated in FX (IMF 2016), a depreciation of the Ruble threatened to harm the bank stability.

2. In line with most previous papers (e.g. Nguyen 2021), by the stability of a banking institution we understand the absence of excessive risk-taking by that institution.

3. https://www.cbr.ru/Collection/Collection/File/8376/fin-stab-2014-15_4-1_e.pdf.

4. https://www.cbr.ru/Collection/Collection/File/8372/OFS_17-01_e.pdf.

5. While the effect of international oil prices on corporate financial performances is well documented in the literature (e.g. Henriques and Sadorsky 2008, 2011; Dayanandan and Donker 2011), the impact on the bank financial stability is poorly investigated.

6. A recent paper by Fedoseeva (2018) shows that the pass-through between oil prices and the Rubble exchange rate to US dollar substantially increased during the oil price collapse in 2014. Rubble’s depreciation generated a sharp increase in import prices with a positive impact on inflation, threatening thus the banking sector stability.

7. For a detailed description of this transmission channel, please refer to Section 2.

8. Data on NPL for public banks in Russia are in most of the cases unavailable. Therefore, the use of Z-scores represents a solution and a proxy for bank financial stability.

9. Using a rolling window approach, Hamilton (2003) compares the oil price in the moment t with its maximum value over n previous periods to identify positive oil price shocks. Cong et al. (2008) compute both positive and negative oil price shocks but different from Hamilton, they identify oil price shocks by comparing the oil price in the moment t with its maximum/minimum values unregistered in all previous periods. Babatunde, Adenikinju, and Adenikinju (2013) combine these approaches and compute both positive and negative shocks, using in the same time a rolling window framework.

10. An exception is the paper by Fungáčová and Weill (2013) which, however, investigates the role of bank competition in explaining the bank failure in Russia.

11. For AK Bars Bank data are available starting with 2011, for RBC OJSC there are no data available for a series of indicators as liquidity ratio or net interest margins, and severe losses were recorded for the entire period. In addition, for the Best Efforts Bank data are available starting with 2014 only. These banks are therefore excluded from the analysis.

12. These two banks are Promsvyazbank and Tatfondbank.

13. The 17 public banks retained in our sample are: Avangard Joint Stock Bank, Bank Otkritie Financial Corporation, Bank St. Petersburg, Bank Zenit, Credit Bank of Moscow, Far East Bank, Gazprombank, Joint Stock Commercial Bank Rosbank, Moscovskiy Oblastnoi Bank, OTP Bank, Bank Uralsib, Promsvyazbank, Sberbank Russia, Tatfondbank, Vozrozhdenie Bank, VTB Bank and West Siberian Commercial Bank.

14. We use time-varying approaches for computing Z-scores and not static approaches (e.g. Hesse and Čihák 2007), because we want to see how the evolution of international oil prices influence the dynamics of bank risk taking.

15. As in (Lee and Lee 2019) we use WTI crude oil prices from the Energy Information Administration, expressed in log-returns.

16. Figure A1 – Appendix shows how shocks spread over time.

17. The Russian banking regulation framework recorded important changes after the banking crisis in 2014. This element may also affect the bank stability. We consider that the World Bank indicator assessing the regulatory quality capture the effect of the banking regulation reform.

18. The methodology used by Transparency International to assess the perception on corruption, associate a high value of $\mathrm{c}\mathrm{p}\mathrm{i}$ with a small level of corruption. Therefore, a positive sign for $\mathrm{c}\mathrm{p}\mathrm{i}$ is expected in our regressions.

19. The correlation matrix (Table A1 – Appendix) shows a high correlation between the two metrics of the Z-score, namely $\mathrm{z}1$ and $\mathrm{z}2$. A positive correlation appears between bank stability and our interest variables, as expected. In addition, bank performances are positively correlated with the Z-score ($\mathrm{n}\mathrm{o}\mathrm{c}\mathrm{f}$ represents an exception and shows a negative correlation with the Z-score). At the same time, it seems that the size is positively correlated with Z-score, indicating that larger banks are more stable. Further, bank stability is positively correlated with the economic growth, as expected, but also with $\mathrm{c}\mathrm{p}\mathrm{i}$ (a higher $\mathrm{c}\mathrm{p}\mathrm{i}$ is equivalent with a lower perception of corruption). The level of correlation of our variables seems, however, reduced (except for the two metrics of the Z-score).

20. The application of second-generation panel unit root tests (e.g. Pasaran, 2007) leads to similar findings. The author can provide these results upon request.

21. As noted by Pesaran, Shin, and Smith (1999), the PMG estimator can be used when the regressors are stationary, or when they follow unit root processes.

22. The use of the PMG estimator usually requires large samples. However, the PMG can also be used for small samples (Pesaran, Shin, and Smith 1999). In fact, the use of PMG estimator for macro panel analyses is not unusual (see, for example, Martínez-Zarzoso and Bengochea-Morancho 2004; Albulescu and Ionescu 2018).

23. The impact of the liquidity ratio might capture the effect of international oil prices.

Additional information

Funding

This work was supported by the Romanian National Authority for Scientific Research and Innovation, CNCS – UEFISCDI [PN-III-P1-1.1-TE-2019-0436].

Notes on contributors

Claudiu Tiberiu Albulescu

Claudiu Albulescu is currently full Professor at the Management Department, Faculty of Management in Production and Transportations, within the Politehnica University of Timisoara. He is associated researcher at CRIEF, University of Poitiers, and associated professor at the Doctoral School of Economics and Business Administration within the West University of Timisoara. He is a member of INFER board. His research interests are: banking and finance, financial macroeconomics, environmental and energy economics.