Preview

Finance: Theory and Practice

Advanced search

Transmission of systemic Risk between the banking systems of Asia-Pacific Countries and Russia

https://doi.org/10.26794/2587-5671-2023-27-5-182-194

Abstract

The subject of this research is systemic risk transmission between financial sectors in the international financial market.

The purpose of our paper is to determine topology characteristics for the network connecting banking systems in the Asia-Pacific region (APR) and Russia. Given the growing role of this region in the global financial market, its susceptibility to crises can be dangerous for other countries.

This determines the relevance of our study. To build the network, we used the SRISK indicators, which reflect capital losses in the financial institutions’ capital losses in case of a large-scale crisis. The networks were built with the use of the NETS algorithm, proposed by Barigozzi, M., & Brownlees, C. (2019).

This method is based on sparse vector autoregressions estimated by LASSO. As a result of the application the algorithm, we get two networks simultaneous interconnections and using the values of the lagged variables. The networks were constructed for the 2005–2020 time period and separately for sub-periods including the global financial crisis (2005– 2013) and the COVID-19 pandemic period (2014–2020).

Based on the results obtained, the networks over the entire time period seem to be quite susceptible to external risks. China, Japan, Singapore and Taiwan are the largest shock donors in this region. Russia mainly accepts risks, generated by other countries, in the period 2014–2020. Strengthened/weakened cooperation with the largest risk exporters in this region will increase/decrease the likelihood of systemic risk transfer to the Russian financial sector.

About the Authors

S. A. Dzuba
Far Eastern Federal University
Russian Federation

Sergey A. Dzuba — D r. Sci. (Econ.)

Vladivostok


Competing Interests:

The authors have no conflicts of interest to declare.



V. S. Tishkovetz
Far Eastern Federal University
Russian Federation

Vladislav S. Tishkovetz —  Bachelor Sci.

Vladivostok


Competing Interests:

The authors have no conflicts of interest to declare.



M. A. Shchepeleva
National Research University Higher School of Economics; MGIMO MFA of Russia
Russian Federation

Maria A. Shchepeleva —  Cand. Sci. (Econ.), Assoc. Prof.; Research Associate, New Trends in International Finance Laboratory

Moscow


Competing Interests:

The authors have no conflicts of interest to declare.



References

1. Stolbov M., Shchepeleva M. Systemic risk in Europe: Deciphering leading measures, common patterns and real effects. Annals of Finance. 2018;14(1):49–91. DOI: 10.1007/s10436–017–0310–3

2. Barigozzi M., Brownlees C. NETS: Network estimation for time series. Journal of Applied Econometrics. 2019;34(3):347–364. DOI: 10.1002/jae.2676

3. Allen F., Gale D. Financial contagion. The Journal of Political Economy. 2000;108(1):1–33. DOI: 10.1086/262109

4. Freixas X., Parigi B. M., Rochet J.-Ch. Systemic risk, interbank relations, and liquidity provision by the central bank. Journal of Money, Credit and Banking. 2000;32(3):611–638. DOI: 10.2307/2601198

5. Nier E., Yang J., Yorulmazer T., Alentorn A. Network models and financial stability. Journal of Economic Dynamics and Control. 2007:31(6):2033–2060. DOI: 10.1016/j.jedc.2007.01.014

6. Čihák M., Muñoz S., Scuzzarella R. The bright and the dark side of cross-border banking linkages. IMF Working Paper. 2011;(186). URL: https://www.imf.org/external/pubs/ft/wp/2011/wp11186.pdf

7. Glasserman P., Young H. P. How likely is contagion in financial networks? Journal of Banking & Finance. 2015;50:383–399. DOI: 10.1016/j.jbankfin.2014.02.006

8. Acemoglu D., Ozdaglar A., Tahbaz-Salehi A. Systemic risk and stability in financial networks. American Economic Review. 2015;105(2):564–608. DOI: 10.1257/aer.20130456

9. Gai P., Kapadia S. Contagion in financial networks. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2010;466(2120):2401–2423. DOI: 10.1098/rspa.2009.0410

10. Minoiu C., Reyes J. A. A network analysis of global banking: 1978–2010. Journal of Financial Stability. 2013;9(2):168–184. DOI: 10.1016/j.jfs.2013.03.001

11. Chinazzi M., Fagiolo G., Reyes J. A., Schiavo S. Post-mortem examination of the international financial network. Journal of Economic Dynamics and Control. 2013;37(8):1692–1713. DOI: 10.1016/j.jedc.2013.01.010

12. Hale G. Bank relationships, business cycles, and financial crises. Journal of International Economics. 2012;88(2):312–325. DOI: 10.1016/j.jinteco.2012.01.011

13. Hale G., Kapan T., Minoiu C. Crisis transmission in the global banking network. IMF Working Paper. 2016;(91). URL: https://www.imf.org/external/pubs/ft/wp/2016/wp1691.pdf

14. Cerutti E., Zhou H. The global banking network in the aftermath of the crisis: Is there evidence of deglobalization? IMF Working Paper. 2017;(232). DOI: 10.5089/9781484324868.001

15. Dungey M., Fry R., Martin V. L. Correlation, contagion, and Asian evidence. Asian Economic Papers. 2006;5(2):32–72. DOI: 10.1162/asep.2006.5.2.32

16. Forbes K. J., Rigobon R. No contagion, only interdependence: Measuring stock market comovements. The Journal of Finance. 2002;57(5):2223–2261. DOI: 10.1111/0022–1082.00494

17. Sander H., Kleimeier S. Contagion and causality: An empirical investigation of four Asian crisis episodes. Journal of International Financial Markets, Institutions and Money. 2003;13(2):171–186. DOI: 10.1016/S1042–4431(02)00043–4

18. Baur D., Schulze N. Coexceedances in financial markets — a quantile regression analysis of contagion. Emerging Markets Review. 2005;6(1):21–43. DOI: 10.1016/j.ememar.2004.10.001

19. Dahlhaus R., Eichler M. Causality and graphical models in time series analysis. In: Green P., Hjort N., Richardson S., eds. Highly structured stochastic systems. Oxford: Oxford University Press; 2003:115–137. URL: https://galton.uchicago.edu/~eichler/hsss.pdf

20. Eichler M. Granger causality and path diagrams for multivariate time series. Journal of Econometrics. 2007;137(2):334–353. DOI: 10.1016/j.jeconom.2005.06.032

21. Giudici P., Parisi L. CoRisk: Credit risk contagion with correlation network models. Risks. 2018;6(3):95. DOI: 10.3390/risks6030095

22. Avdjiev S., Giudici P., Spelta A. Measuring contagion risk in international banking. Journal of Financial Stability. 2019;42:36–51. DOI: 10.1016/j.jfs.2019.05.014

23. Chiang T. C., Jeon B. N., Li H. Dynamic correlation analysis of financial contagion: Evidence from Asian markets. Journal of International Money and Finance. 2007;26(7):1206–1228. DOI: 10.1016/j.jimonfin.2007.06.005

24. Diebold F. X., Yılmaz K. On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics. 2014;182(1):119–134. DOI: 10.1016/j.jeconom.2014.04.012

25. Barigozzi M., Hallin M. A network analysis of the volatility of high-dimensional financial series. Journal of the Royal Statistical Society: Series C (Applied Statistics). 2016;66(3):581–605. DOI: 10.1111/rssc.12177

26. Barigozzi M., Hallin M., Soccorsi S., von Sachs R. Time-varying general dynamic factor models and the measurement of financial connectedness. Journal of Econometrics. 2021;222(1.Pt.B):324–343. DOI: 10.1016/j.jeconom.2020.07.004

27. Rodriguez J. C. Measuring financial contagion: A copula approach. Journal of Empirical Finance. 2007;14(3):401–423. DOI: 10.1016/j.jempfin.2006.07.002

28. Wen X., Wei Y., Huang D. Measuring contagion between energy market and stock market during financial crisis: A copula approach. Energy Economics. 2012;34(5):1435–1446. DOI: 10.1016/j.eneco.2012.06.021

29. Delgado P. C., Congregado E., Golpe A. A., Vides J. C. The yield curve as a recession leading indicator. An application for gradient boosting and random forest. International Journal of Interactive Multimedia and Artificial Intelligence. 2022;7(3):7–19. DOI: 10.48550/arXiv.2203.06648

30. Sabes D., Sahuc J.-G. Do yield curve inversions predict recessions in the euro area? Finance Research Letters. 2023;52:103416. DOI: 10.1016/j.frl.2022.103416

31. Hasse J.-B., Lajaunie Q. Does the yield curve signal recessions? New evidence from an international panel data analysis. The Quarterly Review of Economics and Finance. 2022;84:9–22. DOI: 10.1016/j.qref.2022.01.001

32. Page L., Brin S., Motwani R., Winograd T. The PageRank citation ranking: Bringing order to the web. Stanford Digital Library Technologies Project. 1998. URL: https://www.cis.upenn.edu/~mkearns/teaching/NetworkedLife/pagerank.pdf

33. Kleinberg J. M. Authoritative sources in a hyperlinked environment. Journal of the ACM. 1999;46(5):604– 632. DOI: 10.1145/324133.324140


Review

For citations:


Dzuba S.A., Tishkovetz V.S., Shchepeleva M.A. Transmission of systemic Risk between the banking systems of Asia-Pacific Countries and Russia. Finance: Theory and Practice. 2023;27(5):182-194. https://doi.org/10.26794/2587-5671-2023-27-5-182-194

Views: 358


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2587-5671 (Print)
ISSN 2587-7089 (Online)