Examining Systemic Risk using Google PageRank Algorithm: An Application to Indian Non-Bank Financial Companies (NBFCs) Crisis
In the recent financial crises, attention has shifted towards "too-central-to-fail" to recognize the sources of systemic risk. The NBFC Crisis of 2018-19 adversely affected other financial institutions and the real economy of India. The NBFCs crisis highlighted the role of smaller institu...
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| Format: | Article |
| Language: | English |
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Ram Arti Publishers
2022-07-01
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| Series: | International Journal of Mathematical, Engineering and Management Sciences |
| Subjects: | |
| Online Access: | https://www.ijmems.in/cms/storage/app/public/uploads/volumes/37-IJMEMS-22-0011-7-4-575-588-2022.pdf |
| _version_ | 1811294157497434112 |
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| author | Anurag Chaturvedi Archana Singh |
| author_facet | Anurag Chaturvedi Archana Singh |
| author_sort | Anurag Chaturvedi |
| collection | DOAJ |
| description | In the recent financial crises, attention has shifted towards "too-central-to-fail" to recognize the sources of systemic risk. The NBFC Crisis of 2018-19 adversely affected other financial institutions and the real economy of India. The NBFCs crisis highlighted the role of smaller institutions in perpetuating and amplifying the crisis. Thus, the present study models the interconnection of NBFCs with the rest of financial institutions using a complex Granger-causality network based on returns data. The PageRank algorithm identifies the central and important nodes and ranks financial institutions in pre-crisis and crisis periods. The financial institutions are also ranked based on the maximum percentage loss suffered during the crises. Using non-parametric rank-based regression, the PageRank ranking of financial institutions in the pre-crises period (explanatory variable) is regressed with the ranking of financial institutions based on maximum percentage loss suffered by them during the crises period (dependent variable) along with Leverage and Size as control variables. We found that PageRank from pre-crisis can significantly identify most financial institutions that suffered loss during NBFCs crises even in the presence of control variables. |
| first_indexed | 2024-04-13T05:13:18Z |
| format | Article |
| id | doaj.art-d1ebfcca0e7042afaae4e27abc4e5fa9 |
| institution | Directory Open Access Journal |
| issn | 2455-7749 |
| language | English |
| last_indexed | 2024-04-13T05:13:18Z |
| publishDate | 2022-07-01 |
| publisher | Ram Arti Publishers |
| record_format | Article |
| series | International Journal of Mathematical, Engineering and Management Sciences |
| spelling | doaj.art-d1ebfcca0e7042afaae4e27abc4e5fa92022-12-22T03:00:59ZengRam Arti PublishersInternational Journal of Mathematical, Engineering and Management Sciences2455-77492022-07-017457558810.33889/IJMEMS.2022.7.4.037Examining Systemic Risk using Google PageRank Algorithm: An Application to Indian Non-Bank Financial Companies (NBFCs) CrisisAnurag Chaturvedi0Archana Singh1University School of Management & Entrepreneurship, Delhi Technological University, East Delhi Campus, Vivek Vihar, Phase-2, Delhi-110095, India.Delhi School of Management, Delhi Technological University, Shahbad Daulatpur, Main Bawana Road, Delhi-110042, India.In the recent financial crises, attention has shifted towards "too-central-to-fail" to recognize the sources of systemic risk. The NBFC Crisis of 2018-19 adversely affected other financial institutions and the real economy of India. The NBFCs crisis highlighted the role of smaller institutions in perpetuating and amplifying the crisis. Thus, the present study models the interconnection of NBFCs with the rest of financial institutions using a complex Granger-causality network based on returns data. The PageRank algorithm identifies the central and important nodes and ranks financial institutions in pre-crisis and crisis periods. The financial institutions are also ranked based on the maximum percentage loss suffered during the crises. Using non-parametric rank-based regression, the PageRank ranking of financial institutions in the pre-crises period (explanatory variable) is regressed with the ranking of financial institutions based on maximum percentage loss suffered by them during the crises period (dependent variable) along with Leverage and Size as control variables. We found that PageRank from pre-crisis can significantly identify most financial institutions that suffered loss during NBFCs crises even in the presence of control variables.https://www.ijmems.in/cms/storage/app/public/uploads/volumes/37-IJMEMS-22-0011-7-4-575-588-2022.pdfcomplex financial networknetwork centralitypagerank centralitysystemic risknon-banking financial companiesearly warning signaltoo-central-to-fail |
| spellingShingle | Anurag Chaturvedi Archana Singh Examining Systemic Risk using Google PageRank Algorithm: An Application to Indian Non-Bank Financial Companies (NBFCs) Crisis International Journal of Mathematical, Engineering and Management Sciences complex financial network network centrality pagerank centrality systemic risk non-banking financial companies early warning signal too-central-to-fail |
| title | Examining Systemic Risk using Google PageRank Algorithm: An Application to Indian Non-Bank Financial Companies (NBFCs) Crisis |
| title_full | Examining Systemic Risk using Google PageRank Algorithm: An Application to Indian Non-Bank Financial Companies (NBFCs) Crisis |
| title_fullStr | Examining Systemic Risk using Google PageRank Algorithm: An Application to Indian Non-Bank Financial Companies (NBFCs) Crisis |
| title_full_unstemmed | Examining Systemic Risk using Google PageRank Algorithm: An Application to Indian Non-Bank Financial Companies (NBFCs) Crisis |
| title_short | Examining Systemic Risk using Google PageRank Algorithm: An Application to Indian Non-Bank Financial Companies (NBFCs) Crisis |
| title_sort | examining systemic risk using google pagerank algorithm an application to indian non bank financial companies nbfcs crisis |
| topic | complex financial network network centrality pagerank centrality systemic risk non-banking financial companies early warning signal too-central-to-fail |
| url | https://www.ijmems.in/cms/storage/app/public/uploads/volumes/37-IJMEMS-22-0011-7-4-575-588-2022.pdf |
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