Measuring Asymmetric Volatility of Bank Nifty Index using Egarch Model & Obstacle”
Ch. Naresh1, Ravi Sankar Kummeta2, N. Ramanjaneyulu3
1Dr. Ch. Naresh, Assistant Professor, School of Management Studies, Gurunanak Institutions Technical Campus, Khanapur (V), Ibrahimpatnam (Telangana), India.
2Dr. Ravi Sankar Kummeta, Associate Professor, School of Management Studies, Guru Nanak Institutions Technical Campus, Hyderabad (Telangana) India.
3Dr. N. Ramanjaneyulu, Professor & Head, Department of MBA, Malla Reddy Engineering College (Autonomous), Secunderabad (Telangana). India.
Manuscript received on 01 November 2023 | Revised Manuscript received on 08 November 2023 | Manuscript Accepted on 15 November 2023 | Manuscript published on 30 November 2023 | PP: 67-72 | Volume-3 Issue-2, November 2023 | Retrieval Number: 100.1/ijef.D16711210423 | DOI : 10.54105/ijef.D1671.03021123
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Abstract: The paper examines the significance of volatility models in forecasting future volatility for effective portfolio allocation and risk reduction. It compares the performance of symmetric and asymmetric models in estimating conditional variance, and linear versus non-linear GARCH models. Using secondary data from the National Stock Exchange’s Nifty Bank index, the study applies Exponential GARCH (1,1) to measure asymmetric volatility and conducts various tests to confirm the suitability of the data for analysis. The results indicate clustering of volatility in Nifty Bank returns over a four-year period, with the presence of asymmetrical effects and leverage constants. The study concludes that negative information has a greater impact on volatility than positive surprises, and that market fluctuations are inversely related to stock market performance. This research provides valuable insights for portfolio selection, risk management, and asset pricing in the context of increasing volatility across various markets and industries.
Keywords: National Stock Exchange’s, Clustering, Nifty Bank, volatility
Scope of the Article: Management and Cost Accounting