The Function of Financial Stress Testing Amid the COVID-19 Crisis: How Banks Maintained Adherence to Basel III
Keywords:
Capital adequacy, risk assessment, liquidity risk, systemic riskAbstract
The COVID-19 epidemic caused financial strain on global financial institutions, which tested the resilience of traditional risk management systems particularly the stress testing models needed for Basel III adherence. A basic feature of modern banking control, stress testing aims to evaluate a bank's capacity to resist significant financial disruptions by modeling the consequences of hypothetical negative events on its capital sufficiency and liquidity. Unexpected changes in consumer behavior, financial risk, and global supply networks during the epidemic revealed the flaws in present stress testing systems. Globally, banks were obliged to quickly review their risk profiles and change their models to fit the sudden economic downturn.The COVID-19 disaster revealed hitherto unheard-of features that highlighted many flaws in these stress tests, which usually rely on historical data and accepted stress models. Many models needed help to handle the large and fast effects of the epidemic, therefore exposing weaknesses in scenario construction and prediction. Moreover, authorities have to consider temporary changes to capital requirements to prevent a liquidity crisis, which emphasizes even more the importance of adaptability inside legislative systems. Notwithstanding these limitations, stress testing was essential in preparing financial institutions for potential capital shortfalls and in support of proactive initiatives to increase liquidity and maintain stability. This article examines the changes in stress testing methods during the epidemic and assesses how banks used these models to comply with Basel III in face of unanticipated market volatility. Analyzing these adaptations, restrictions, and regulatory reactions helps to enhance stress testing models for future crises by means of dynamic, forward-looking models that more successfully capture real-time economic risks and equip financial systems for unprecedented global disruptions.
References
1. Samitas, A., & Polyzos, S. (2015). To Basel or not to Basel? Banking crises and contagion. Journal of Financial Regulation and Compliance, 23(3), 298-318.
2. Restoy, F. (2019). Central banks and financial stability: A reflection after the Covid-19 outbreak1. In Proceedings of OeNB Workshops (p. 51).
3. Kapoor, S., & Kaur, M. (2017). Basel III norms: a SWOT and TOWS approach. Vision, 21(3), 250-258.
4. Board, F. S. (2010). the Basel Committee on Banking Supervision. Macroeconomic Assessment Group.
5. Morais, L. S. (2016). 8 THE EU fiscal response to the COVID-19 crisis and the Banking sector: risks and opportunities. Pandemic Crisis and Financial Stability, 265.
6. Pakravan, K. (2014). Bank capital: the case against Basel. Journal of Financial Regulation and Compliance, 22(3), 208-218.
7. Bank, M. (2011). Pillar 3 disclosures.
8. Joosen, B. (2016). 11. Releasability Combined Buffer Requirements after the COVID-19 pandemic. Financial Stability amidst the Pandemic Crisis: On Top of the Wave, 359.
9. Varotto, S. (2011). Liquidity risk, credit risk, market risk and bank capital. International Journal of Managerial Finance, 7(2), 134-152.
10. Uylangco, K., & Li, S. (2016). An evaluation of the effectiveness of Value-at-Risk (VaR) models for Australian banks under Basel III. Australian Journal of Management, 41(4), 699-718.
11. Chockalingam, A., Dabadghao, S., & Soetekouw, R. (2018). Strategic risk, banks, and Basel III: estimating economic capital requirements. The Journal of Risk Finance, 19(3), 225-246.
12. Panama reached high-income, L. A. (1985). STRONG FUNDAMENTALS PRE-PANDEMIC. World Economic Outlook, 1995(2005), 2015.
13. Alibrandi, A. S., & Frigeni, C. (2016). 12. Restriction for bank capital remuneration in the pandemic: future or an outright extraordinary measure?. Financial Stability amidst the Pandemic Crisis: On Top of the Wave, 393.
14. Clarke, B. (2016). 4 Cultural reforms in Irish banks. Walking the walk during the COVID-19 pandemic. Pandemic Crisis and Financial Stability, 127.
15. Gortsos, C. V. (2016). 11 The application of the EU banking resolution framework amidst the pandemic crisis. Pandemic Crisis and Financial Stability, 361.
16. Komandla, V. Enhancing Security and Fraud Prevention in Fintech: Comprehensive Strategies for Secure Online Account Opening.
17. Komandla, V. Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction.
18. Gade, K. R. (2019). Data Migration Strategies for Large-Scale Projects in the Cloud for Fintech. Innovative Computer Sciences Journal, 5(1).
19. Gade, K. R. (2018). Real-Time Analytics: Challenges and Opportunities. Innovative Computer Sciences Journal, 4(1).
20. Boda, V. V. R., & Immaneni, J. (2019). Streamlining FinTech Operations: The Power of SysOps and Smart Automation. Innovative Computer Sciences Journal, 5(1).
21. Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2019). End-to-End Encryption in Enterprise Data Systems: Trends and Implementation Challenges. Innovative Computer Sciences Journal, 5(1).
22. Muneer Ahmed Salamkar, and Karthik Allam. Architecting Data Pipelines: Best Practices for Designing Resilient, Scalable, and Efficient Data Pipelines. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Jan. 2019
23. Muneer Ahmed Salamkar. ETL Vs ELT: A Comprehensive Exploration of Both Methodologies, Including Real-World Applications and Trade-Offs. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Mar. 2019
24. Muneer Ahmed Salamkar. Next-Generation Data Warehousing: Innovations in Cloud-Native Data Warehouses and the Rise of Serverless Architectures. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Apr. 2019
25. Muneer Ahmed Salamkar. Real-Time Data Processing: A Deep Dive into Frameworks Like Apache Kafka and Apache Pulsar. Distributed Learning and Broad Applications in Scientific Research, vol. 5, July 2019
26. Naresh Dulam. Apache Spark: The Future Beyond MapReduce. Distributed Learning and Broad Applications in Scientific Research, vol. 1, Dec. 2015, pp. 136-5
27. Naresh Dulam. NoSQL Vs SQL: Which Database Type Is Right for Big Data?. Distributed Learning and Broad Applications in Scientific Research, vol. 1, May 2015, pp. 115-3
28. Naresh Dulam. Data Lakes: Building Flexible Architectures for Big Data Storage. Distributed Learning and Broad Applications in Scientific Research, vol. 1, Oct. 2015, pp. 95-114
29. Naresh Dulam. The Rise of Kubernetes: Managing Containers in Distributed Systems. Distributed Learning and Broad Applications in Scientific Research, vol. 1, July 2015, pp. 73-94
30. Sarbaree Mishra. A Distributed Training Approach to Scale Deep Learning to Massive Datasets. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Jan. 2019
31. Sarbaree Mishra, et al. Training Models for the Enterprise - A Privacy Preserving Approach. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Mar. 2019
32. Sarbaree Mishra. Distributed Data Warehouses - An Alternative Approach to Highly Performant Data Warehouses. Distributed Learning and Broad Applications in Scientific Research, vol. 5, May 2019
33. Sarbaree Mishra, et al. Improving the ETL Process through Declarative Transformation Languages. Distributed Learning and Broad Applications in Scientific Research, vol. 5, June 2019
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