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

Q3 Economics, Econometrics and Finance
S. A. Dzuba, V. S. Tishkovetz, M. A. Shchepeleva
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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.
亚太国家和俄罗斯银行系统之间的系统性风险传导
本研究的主题是国际金融市场中金融部门之间的系统性风险传导。本文的目的是确定连接亚太地区(APR)和俄罗斯银行系统的网络的拓扑特征。鉴于该地区在全球金融市场中日益重要的作用,它对危机的敏感性对其他国家来说可能是危险的。这决定了我们研究的相关性。为了构建网络,我们使用了SRISK指标,该指标反映了大规模危机情况下金融机构资本损失中的资本损失。这些网络是使用由Barigozzi, M., &布朗利斯,C.(2019)。该方法基于LASSO估计的稀疏向量自回归。通过对该算法的应用,我们得到了两个网络-同时互连和使用滞后变量的值。该网络是为2005 - 2020年期间构建的,并分别为全球金融危机(2005 - 2013年)和COVID-19大流行期间(2014-2020年)等子时期构建的。根据所获得的结果,整个时间段内的网络似乎很容易受到外部风险的影响。中国大陆、日本、新加坡和台湾是该地区最大的冲击捐助国。2014-2020年期间,俄罗斯主要接受由其他国家产生的风险。加强/削弱与该地区最大风险出口国的合作将增加/减少系统性风险向俄罗斯金融部门转移的可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Finance: Theory and Practice
Finance: Theory and Practice Economics, Econometrics and Finance-Economics, Econometrics and Finance (miscellaneous)
CiteScore
1.30
自引率
0.00%
发文量
84
审稿时长
8 weeks
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