Capital adequacy and systemic risk: Evidence from selected Indian private sector banks
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2024.15.spl-2.31Keywords:
Capital adequacy test, Private sector banksDimensions Badge
Issue
Section
License
Copyright (c) 2024 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This study investigates the relationship between systemic risk and capital adequacy in certain private-sector banks in India. Based on the CAMEL paradigm, this study examines five key financial ratios: CAR, DER, TATA, GSTR, and CDR. These ratios measure the extent to which funds have been advanced relative to total assets. Banks' risk profiles and financial health are assessed using these ratios in light of regulatory requirements and market stability. To examine the impact of these ratios on systemic risk indicators, we use the average data from 2018–19 to 2022–23.Abstract
How to Cite
Downloads
Similar Articles
- Maysam A. Khabisi, Azar B. Masoudzade, Neda F. Rad, On the effectiveness of receiving teacher and peer feedback as a mediator on Iranian English as a Foreign Language learners’ writing skill: Mobile-mediated vs. direct instruction , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- A.P. Asha Sapna, C. Anbalagan, Towards a better living environment-compressive strength and water absorption testing of mini compressed stabilized earth blocks and fired bricks , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Kalpana Deshmukh, Aparna Dighe, Harshal Raje, Impact of mindfulness-based programs on reducing stress and enhancing academic performance in college students , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Arenlila Jamir, Sangeeta Kharde, Anita Dalal, Health-seeking behavior of first-time mothers toward pregnancy , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Pravin P. Adivarekar1, Amarnath Prabhakaran A, Sukhwinder Sharma, Divya P, Muniyandy Elangovan, Ravi Rastogi, Automated machine learning and neural architecture optimization , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Tursunova N. Isroilovna, Dilbar M. Almuradova, Orifjon A. Talipov, Features of diagnosing ovarian tumors in women of pre- and postmenopausal age , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Shripada Patil, Sandeep N. Jagdale, Prashant Kalshetti, Management education system in the 21st century: Challenges and opportunities , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Ashoke D. Maliki, Taiwo A. Muritala, Saji George, Frank A. Ogedengbe, Impact of project financiers’ strategies on de-risking infrastructural projects: A conceptual review , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Bajeesh Balakrishnan, Swetha A. Parivara, E-HRM: Learning approaches, applications and the role of artificial intelligence , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Jayalakshmi K., M. Prabakaran, The role of big data in transforming human resource analytics: A literature review , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
You may also start an advanced similarity search for this article.
Most read articles by the same author(s)
- Amit Maru, Dhaval Vyas, Hybrid deep learning approach for pre-flood and post-flood classification of remote sensed data , The Scientific Temper: Vol. 16 No. Spl-1 (2025): The Scientific Temper

