Range Volatility Spillover across Sectoral Stock Indices during COVID-19 Pandemic: Evidence from Indian Stock Market

Datta, Susanta and Hatekar, Neeraj (2022): Range Volatility Spillover across Sectoral Stock Indices during COVID-19 Pandemic: Evidence from Indian Stock Market.

Abstract

The study examines volatility spillover across sectoral stock indices from one Emerging Market Economies, viz. India during COVID-19 pandemic. Our contributions are threefold: (a) incorporation of range volatility during the pandemic, (b) comparative assessment of volatility spillover at the sectoral level, and (c) identify evidence of volatility spillover across different sectoral indices. Using daily historical price data for 11 sectoral stock indices during the first wave of the pandemic; we find that Range GARCH (1,1) performs better not only during the crisis but also during pandemic periods. The multivariate Range DCC model confirms evidence of volatility spillover across sectoral stock indices.

• Item Type: MPRA Paper
• Language: English
• Keywords: Forecasting, Volatility, Spillover, Return, Range, NIFTY, COVID 19
• Subjects: C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C58 - Financial Econometrics; G - Financial Economics > G1 - General Financial Markets > G17 - Financial Forecasting and Simulation

References

1) Akhtaruzzaman, M., Boubaker, S., & Sensoy, A. (2020). Financial contagion during COVID–19 crisis. Finance Research Letter. http://dx.doi.org/10.1016/j.frl.2020.101604. 2) Alizadeh, S., Brandt, M. W., & Diebold, F. X. (2002). Range-based estimation of stochastic volatility models. Journal of Finance, 57, 1047−1091. 3) Andersen, T. & Bollerslev, T. (1997). Heterogeneous information arrivals and return volatility dynamics: uncovering the long run-in high frequency returns. Journal of Finance, 52, 975−1005. 4) Baker, S.R., Bloom, N., Davis, S.J., Kost, K.J., Sammon, M.C. & Viratyosin, T.(2020). The unprecedented stock market impact of COVID-19. National Bureau of Economic Research Working Paper (26945). https://www.nber.org/papers/w26945. 5) Baldwin, R. & di Mauro, B.W. (2020). Mitigating the COVID economic crisis: Act fast and do whatever it takes. ePubs | VOX, CEPR Policy Portal - Vox EU, https://voxeu.org 6) Banerjee, S. & Chauhan, A. (2020, September 10-12). Financial Markets during the Pandemics and New Finance in The Post Covid Era, [S3IL - KEYNOTE ADDRESS III], EconTR2020@Eskişehir, International Conference on Economics, Eskişehir Osmangazi University, Eskişehir, Turkey, https://econtr.org/ 7) Blancard, G. C. & Desroziers, A. (2020). The stock market and the economy: Insights from the COVID-19 crisis. ePubs | VOX, CEPR Policy Portal - Vox EU, https://voxeu.org 8) Bohdan M. P. (2020). Regression Approach for Modelling COVID-19 Spread and its Impact on Stock Market. Papers 2004.01489, https://arxiv.org/.DOI: https://doi.org/10.48550/arXiv.2004.01489 9) Brandt, M. & Jones, C. (2006). Volatility forecasting with range-based EGARCH models. Journal of Business & Economic Statistics, 24, 470−486. 10) Chou, R. Y. (2005). Forecasting financial volatilities with extreme values: the conditional autoregressive range (CARR) model. Journal of Money, Credit and Banking, 37, 561−582. 11) Chou, R. Y. (2006). Modelling the asymmetry of stock movements using price ranges. Advances in Econometrics, 20, 231−257. 12) Chou, R.Y., Wu, C.C., & Liu, N. (2009). Forecasting time-varying covariance with a range-based dynamic conditional correlation model. Review of Quantitative Finance and Accounting, 33, 327 (2009), DOI:10.1007/s11156-009-0113-3. 13) Chou, R. Y & Cai, Y. (2009). Range based Multivariate Volatility model with double smooth transition in conditional correlation, Global Finance Journal. 20 (2), 137-152, Doi: 10.1016/j.gfj.2008.12.001. 14) Chou, R. Y.; Chou, H. & Liu, N. (2010). Range volatility models and their applications in finance, Handbook of Quantitative Finance & Risk Management, Chapter 83, Springer.Page 13 of 30 15) Corbet, S., Larkin, C., & Lucey, B. (2020). The contagion effects of the covid-19 pandemic: Evidence from gold and cryptocurrencies. Finance Research Letter. http://dx.doi.org/10.1016/j.frl.2020.101554 16) Datta. S. (2019). Range Volatility in Commodity Future Price Index: Evidence from Indian Multi Commodity Exchange (MCX), International Journal of Advance and Innovative Research (Conference Special), Volume 6, Issue 2 (XIV). 17) Datta, S. & Hatekar, N. (2019, January 8-10). Comparative performance of Range Based and Return Based Volatility Models: Evidence from Stock Market of Emerging Market Economies (EMEs) [Conference paper]. 55th Annual Conference of The Indian Econometrics Society (TIES), NISM Patalganga campus. 18) Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of 23 the variance of United Kingdom inflation. Econometrica, 50, 987-1007. 19) Engle R. F. (2002). Dynamic conditional correlation—a simple class of multivariate GARCH models”. Journal of Business and Economic Statistics 20: 339–350. 20) Fiszeder P., Fałdziński, M. & Molnár, P. (2019). Range-based DCC models for covariance and value-at-risk forecasting. Journal of Empirical Finance, Volume 54, 58- 76. 21) Garman, M. and M. Klass (1980). On the estimation of security price volatilities from historical data. Journal of Business, 53, 67-78. 22) Hanspal, T.; Weber, A. & Wohlfart, J. (2020). Income and Wealth Shocks and Expectations during the COVID-19 Pandemic. CESifo Working Paper, No. 8244, Center for Economic Studies and ifo Institute (CESifo), Munich. 23) Haroon, O. & Rizvi, S.A.R. (2020a). COVID-19: Media coverage and financial markets behavior—A sectoral inquiry. J. Behav. Exp. Finance, Volume 27, 100343. http://dx.doi.org/10.1016/j.jbef.2020.100343 24) Haroon, O. & Rizvi, S.A.R. (2020b). Flatten the curve and stock market liquidity–an inquiry into emerging economies. Emerging Market Finance Trade 56 (10), 2151–2161. http://dx.doi.org/10.1080/1540496X.2020.1784716 25) McKibbin, Warwick J. & Fernando, Roshen. (2020). The Global Macroeconomic Impacts of COVID-19: Seven Scenarios. CAMA Working Paper No. 19/2020, http://dx.doi.org/10.2139/ssrn.3547729 26) Molnar, P. (2012). Properties of Range based volatility estimators. International Review of Financial Analysis, 23: 20-29. doi:10.1016/j.irfa2011.06.012. 27) Molnar, P. (2016). High-low range in GARCH models of stock return volatility. Applied Economics, Volume, 48 (51), 4977-4991. https://doi.org/10.1080/00036846.2016.1170929 28) Parkinson, M. (1980). The extreme value method for estimating the variance of the rate of return, Journal of Business, 53, 61–65. 29) Prabheesh, K.P., Garg, B., & Padhan, R. (2020a). Time-varying dependence between stock markets and oil prices during COVID-19: The case of net oil-exporting countries. Econ. Bull. 40 (3), 2408–2418, http://www.accessecon.com/Pubs/EB/2020/Volume40/EB-20-V40-I3-P210.pdf 30) Prabheesh, K.P., Padhan, R., & Garg, B. (2020b). COVID-19 and the oil price–stock market nexus: Evidence from net oil-importing countries. Energy Res. Lett. 1 (2). http://dx.doi.org/10.46557/001c.13745 31) Padhan, R. & Prabheesh, K.P. (2021). The Economics of COVID 19 pandemic: A Survey, Economic Analysis and Policy, 70, 220–237. 32) Rai, K. & Garg, B. (2021). Dynamic correlations and volatility spillover between stock price and exchange rate in BRIICS economies: evidence from the COVID-19 outbreak period. Applied Economic Letters, DOI: http://dx.doi.org/10.1080/13504851.2021.1884835. 33) Rogers, L. & Satchell, S. (1991). Estimating variance from high, low and closing prices, Annals of Applied Probability, 1, 504-512. 34) Salisu, A.A. & Sikiru, A.A. (2020). Pandemics and the Asia-Pacific Islamic stocks. Asian Econ. Lett. 1 (1), 17413. http://dx.doi.org/10.46557/001c.17413. 35) Sharma, S.S. (2020). A note on the Asian market volatility during the COVID-19 pandemic. Asian Econ. Lett. 1 (2) http://dx.doi.org/10.46557/001c.17661. 36) Tse, Y. & Tsui, A. (2002). A Multivariate GARCH Model with Time- Varying Correlations. Journal of Business and Economic Statistics, 20, 351 – 362. 37) Wiggins J. (1991). Empirical tests of the bias and efficiency of the extreme-value variance estimator for common stocks. Journal of Business, 64, 417-432. 38) Xiaolin, H. & Zhigang, Q. (2020). How does China’s stock market react to the announcement of the COVID-19 pandemic lockdown? Economic and Political Studies, DOI: 10.1080/20954816.2020.1780695 39) Yang, D. & Zhang, Q. (2000). Drift-independent volatility estimation based on high, low, open, and closing Prices”, Journal of Business, 73, 477-491.