The Persistence of Volatility in Nifty 50

Authors

  •   S. Vevek Department of Commerce, Alagappa University, Karaikudi - 630 003, Tamil Nadu
  •   M. Selvam Bharathidasan University, Tiruchirappalli - 620 024, Tamil Nadu
  •   S. Sivaprakkash Department of Commerce, College of Science & Humanities, SRM Institute of Science & Technology, Vadapalani Campus, Chennai - 600 026, Tamil Nadu

DOI:

https://doi.org/10.17010/ijrcm/2022/v9i2-3/172549

Keywords:

COVID-19

, GARCH, Nifty 50, Shock, Volatility.

JEL Classification Codes

, G10, G17, O16

Paper Submission Date

, August 25, 2022, Paper sent back for Revision, September 16, Paper Acceptance Date, September 28, 2022

Abstract

The market participants trade in the Nifty index to mitigate the risk, as trading in Nifty leads to wealth and exposes the market participants to external shocks. Normally, market participants disfavor volatility as it is a serious concern. So, volatility measures the magnitude of the information’s impact on any index or stock. The unpredictability of external shock toward the market was a cause for concern because it had adverse effects on the market. The index values worldwide had been substantially sensitive to the external stock. With several factors contributing to the stock market’s performance, distilling volatility is impossible. In addition, the COVID-19 pandemic was the starlight in fuelling the unpredictability in the security markets. The study empirically investigated the impact of events (shocks) on the Nifty 50 index. To achieve our objective, we applied the GARCH model for estimating the volatility of daily returns of the closing price of the Nifty 50 index from January 1, 2019 to December 15, 2021. A total of 732 observations were sourced from the NSE website and transformed into natural log returns for volatility pattern analysis. The findings revealed that the Indian secondary market had experienced unanticipated volatility during the study period. The shock was strong even when the positive information arrived. The existing negative information had a stronger hold over the market movement.

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Published

2022-09-01

How to Cite

Vevek, S., Selvam, M., & Sivaprakkash, S. (2022). The Persistence of Volatility in Nifty 50. Indian Journal of Research in Capital Markets, 9(2-3), 8–18. https://doi.org/10.17010/ijrcm/2022/v9i2-3/172549

References

Azimli, A. (2020). The impact of COVID-19 on the degree of dependence and structure of risk-return relationship: A quantile regression approach. Finance Research Letters, 36, 101648. https://doi.org/10.1016/j.frl.2020.101648

Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1

Bora, D., & Basistha, D. (2021). The outbreak of COVID‐19 pandemic and its impact on stock market volatility: Evidence from a worst‐affected economy. Journal of Public Affairs, 21(4), e2623. https://doi.org/10.1002/pa.2623

Cepoi, C.-O. (2020). Asymmetric dependence between stock market returns and news during COVID-19 financial turmoil. Finance Research Letters, 36, 101658. https://doi.org/10.1016/j.frl.2020.101658

Dey, K., & Brown, A. (2021). Indian stock market's response in five - phases to the COVID-19 pandemic. Indian Journal of Research in Capital Markets, 8(1–2), 26–45. https://doi.org/10.17010/10.17010/ijrcm/2021/v8i1-2/160230

Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. https://doi.org/10.2307/1912773

Faniband, M., & Faniband, T. (2021). Government bonds and stock market: Volatility spillover effect. Indian Journal of Research in Capital Markets, 8(1–2), 61–71. https://doi.org/10.17010/ijrcm/2021/v8i1-2/165087

Kanas, A. (1998). Volatility spillovers across equity markets: European evidence. Applied Financial Economics, 8(3), 245–256. http://dx.doi.org/10.1080/096031098333005

Kaur, H. (2004). Time varying volatility in the Indian stock market. Vikalpa, 29(4), 25–42. https://doi.org/10.1177%2F0256090920040403

Lakshmanasamy, T. (2022). The causal relationship between volatility in crude oil price, exchange rate, and stock price in India: GARCH estimation of spillover effects. Indian Journal of Research in Capital Markets, 8(3), 8–21. https://doi.org/10.17010/ijrcm/2021/v8i3/167954

Mavuluri, P. K., & Boppana, N. (2006). Revisiting volume-volatility relationship: Evidence from India. Available at SSRN. https://dx.doi.org/10.2139/ssrn.958219

Mehta, K., & Sharma, R. (2011). Measurement of time varying volatility of Indian stock market through GARCH model. Asia Pacific Business Review, 7(3), 34–46. https://doi.org/10.1177%2F097324701100700304

Mondal, S. (2020, April 6). Impact of Covid-19 on Indian stock market. Adamas University.

Ng, A. (2000). Volatility spillover effects from Japan and the US to the Pacific-Basin. Journal of International Money and Finance, 19(2), 207–233. https://doi.org/10.1016/S0261-5606(00)00006-1

Ozili, P. K., & Arun, T. (2020). Spillover of COVID-19: Impact on the global economy. Available at SSRN. https://dx.doi.org/10.2139/ssrn.3562570

Prakash, R. P. (2021). An analysis of the macroeconomic variables impacting the Indian stock market at NSE Nifty 50. Indian Journal of Research in Capital Markets, 8(1–2), 72–78. http://doi.org/10.17010/ijrcm/2021/v8i1-2/165088

Raja Ram, A. (2020, April 21). COVID-19 and stock market crash. Outlook Money. https://www.outlookmoney.com/equity/covid-19-impact-on-stock-market-4666

Raju, M. T., & Ghosh, A. (2004). Stock market volatility - An international comparison (Working Paper Series No. 8). Securities and Exchange Board of India. https://www.mmimert.edu.in/images/digital-library/Stock-Market-Volatility-International-Comparison.pdf

Rastogi, S., & Srivastava, V. K. (2011). Comparative study of conditional volatility of Indian and US stock markets using Garch (1, 1) model. Asia Pacific Business Review, 7(1), 92–101. https://doi.org/10.1177%2F097324701100700106

Sakthivel, P., Bodkhe, N., & Kamaiah, B. (2012). Correlation and volatility transmission across international stock markets: A bivariate GARCH analysis. International Journal of Economics and Finance, 4(3), 253–264. http://dx.doi.org/10.5539/ijef.v4n3p253

Saud, A. S., Shakya, S., & Neupane, B. (2022). Intelligent stock trading strategy based on Aroon indicator. Indian Journal of Research in Capital Markets, 9(1), 18–29. https://doi.org/10.17010/ijrcm/2022/v9i1/170401

Shehzad, K., Xiaoxing, L., & Kazouz, H. (2020). COVID-19's disasters are perilous than global financial crisis: A rumour or fact ? Finance Research Letters, 36, 101669. https://doi.org/10.1016/j.frl.2020.101669

Singh, G. (2017). Time varying volatility in the Indian stock market. Business Perspectives, 16(1), 21–38.

Tulsian, R. P., & Shrivastav, R. K. (2020). Trend analysis of the Indian capital market. Indian Journal of Research in Capital Markets, 7(2–3), 23–38. https://doi.org/10.17010/ijrcm/2020/v7i2-3/154511

Vevek, S., & Selvam, D. M. (2021). Modelling on volatility of Indian macroeconomic indicator - Nifty. Empirical Economics Letters, 20(3), 79–92.

Zhang, D., Hu, M., & Ji, Q. (2020). Financial markets under the global pandemic of COVID-19. Finance Research Letters, 36, 101528. https://doi.org/10.1016/j.frl.2020.101528