Indian Journal of Research in Capital Markets

Open Access Open Access  Restricted Access Subscription Access

The Persistence of Volatility in Nifty 50


Affiliations
1 Department of Commerce, Alagappa University, Karaikudi - 630 003, Tamil Nadu, India
2 Bharathidasan University, Tiruchirappalli - 620 024, Tamil Nadu, India
3 Department of Commerce, College of Science & Humanities, SRM Institute of Science & Technology, Vadapalani Campus, Chennai - 600 026, Tamil Nadu, India

   Subscribe/Renew Journal


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.

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, 2022 ; Paper Acceptance Date : September 28, 2022

User
Subscription Login to verify subscription
Notifications
Font Size

  • 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


Abstract Views: 183

PDF Views: 0




  • The Persistence of Volatility in Nifty 50

Abstract Views: 183  |  PDF Views: 0

Authors

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

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.

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, 2022 ; Paper Acceptance Date : September 28, 2022


References





DOI: https://doi.org/10.17010/ijrcm%2F2022%2Fv9i2-3%2F172549