Tail Behaviour of the Nifty-50 Stocks during Crises Periods

Authors

  • Srilakshminarayana Gali Shri Dharmasthala Manjunatheshwara Institute for Management Development Corresponding Author

DOI:

https://doi.org/10.47654/v25y2021i4p115-151

Keywords:

COVID-19, Extreme events, Crisis events, Classification, Nifty 50, Tail behaviour

Abstract

Purpose: This paper examines the behaviour of the NIFTY 50 stocks during the crisis periods by estimating the tail index for each of the stocks.

Design/methodology /approach: The time horizon between 2007 to 2020 was considered and was divided into six periods, at the time points where a crisis had occurred. During each period the behaviour of the stocks was observed and the tail index was estimated using the weighted least squares (WLS) estimator, proposed by Nair et.al. (2019).

Findings: The study finds that the crisis events have changed the tail behaviour of a few stocks, and did not impact the behaviour of other stocks. Also, that these stocks could sustain the change caused by the crisis events during a few periods and could not during other periods. The study finds the periods that are severe and not severe using the tail index values. Towards the end, a classification table that divides the stocks into high risk, moderate risk and low risk, was presented based on the findings.

Practical Implications: We suggest the practitioners and researchers estimate the tail index of the stocks before taking any decision on its probability structure or decisions related to investments etc. This process can be adopted along with other processes. It helps in identifying the stocks that are riskier for investment. Also, identify the periods that are sensitive to the crisis events. The study also recommends estimating the tail index and then deciding upon any other methodology for analyzing the data.

References

Akgiray, V. and Booth, G.G. (1988). The Stable-Law Model of Stock Returns, Journal of Business & Economic Statistics, 6(1), 51-57.https://doi.org/10.1080/07350015.1988.10509636

Al-Awadhi, A. M., Alsaifi, K., Al-Awadhi, A., & Alhammadi, S. (2020). Death and contagious infectious diseases: Impact of the COVID-19 virus on stock market returns, Journal of behavioral and experimental finance, 27, 100326. https://doi.org/10.1016/j.jbef.2020.100326

Ali, M., Alam, N., & Rizvi, S. (2020). Coronavirus (COVID-19) - An epidemic or pandemic for financial markets, Journal of behavioral and experimental finance, 27, 100341. https://doi.org/10.1016/j.jbef.2020.100341

Al-rjoub, Samer & Azzam, Hussam. (2012). Financial crises, stock returns, and volatility in an emerging stock market, The case of Jordan. Journal of Economic Studies, 39, 178-211. https://doi.org/10.1108/01443581211222653

Berkes, I., Horvath, L., & Schauer, J. (2012). Asymptotic behavior of trimmed sums, Stochastics and Dynamics, 12(1), 1150002-1-1150002-14. https://doi.org/10.1142/S0219493712003602

Borak S, Misiorek A, Weron R (2011). Models for heavy-tailed asset returns. In Cizek P, Härdle W, Weron R (Ed.) Statistical Tools for Finance and Insurance, 21-55, Springer, Berlin, Heidelberg.

Chong, C.Y. (2011). Effect of Subprime Crisis on U.S. Stock Market Return and Volatility. Global Economy and Finance Journal, 4 (1), 102-111.

Contessi, S. and De Pace, P. (2020), The International Spread of COVID-19 Stock Market Collapses. Finance Research Letters, 101894. https://doi.org/10.1016/j.frl.2020.101894

Csorgo S, Haeusler E, Mason D (1988). The Asymptotic Distribution of Trimmed Sums. The Annals of Probability, 16(2): 672-699. https://www.jstor.org/stable/2243832

Dimitriou, Dimitrios & Kenourgios, Dimitris. (2014). Contagion Effects of the Global Financial Crisis in the US and European Real Economy Sectors. Panoeconomicus, 275-288. http://dx.doi.org/10.2298/PAN1403275K

Duc Hong Vo & Quang Van Tuan & Trung Vu-Thanh Pham (2019). Sectoral Risks in Vietnam and Malaysia A Comparative Analysis, Advances in Decision Sciences, Asia University, Taiwan, vol. 23(1), 62-87. https://iads.site/wp-content/uploads/papers/2019/Sectoral-Risks-in-Vietnam-and-Malaysia-A-Comparative-Analysis.pdf

Dungey, Mardi & Gajurel, Dinesh. (2014). Equity market contagion during the global financial crisis: Evidence from the world's eight largest economies, Economic Systems, 38 (2), 161-177. https://doi.org/10.1016/j.ecosys.2013.10.003

Embrechts P.; Klueppelberg C.; Mikosch T. (1997). Modelling extremal events for insurance and finance, Stochastic Modelling and Applied Probability, 33. Berlin: Springer. https://link.springer.com/book/10.1007/978-3-642-33483-2

Engelhardt, Nils & Krause, Miguel & Neukirchen, Daniel & Posch, Peter. (2020). What Drives Stocks during the Corona-Crash? News Attention vs. Rational Expectation, Sustainability, 12. 5014. https://doi.org/10.3390/su12125014

Fama, E. (1963). Mandelbrot and the Stable Paretian Hypothesis. Journal of Business, 36, 420-429. http://www.e-m-h.org/Fama63.pdf

Fama, E. (1965). The Behaviour of Stock-Market Price, The Journal of Business, Vol. 38, No.1., 34-105. https://www.jstor.org/stable/2350752

Fauzi, Rizaldi & Wahyudi, Imam. (2016). The effect of firm and stock characteristics on stock returns: Stock market crash analysis, The Journal of Finance and Data Science, 2 (2), 112-124. https://doi.org/10.1016/j.jfds.2016.07.001

Feller W. (1950). An introduction to probability theory and its applications. Wiley Publisher. http://www.ru.ac.bd/stat/wp-content/uploads/sites/25/2019/03/101_06_Feller_An-Introduction-to-Probability-Theory-and-Its-Applications-Vol.-2.pdf

Gençay, R & Selçuk, F. (2004). Extreme value theory and Value-at-Risk: Relative performance in emerging markets, International Journal of Forecasting, 20(2), 287-303. https://doi.org/10.1016/j.ijforecast.2003.09.005

Graham, M., Nikkinen, J., & Peltomäki, J. (2020). Web-Based Investor Fear Gauge and Stock Market Volatility: An Emerging Market Perspective, Journal of Emerging Market Finance, 19, 127 - 153. https://doi.org/10.1177/0972652719877473

Grima, Simon & Caruana, Luca. (2017). The Effect of the Financial Crisis on Emerging Markets: A comparative analysis of the stock market situation before and after, European Research Studies Journal, XX, 727-753. https://www.ersj.eu/dmdocuments/2017-xx-4-b-54.pdf

Hall, P. (1984). On the Influence of Extremes on the Rate of Convergence in the Central Limit Theorem, Ann. Probab, 12(1), 154-172. DOI: 10.1214/aop/1176993380

Hing-Ling Lau, A., Hon-Shiang Lau, and Wingender, J.R. (1990). The distribution of stock returns: new evidence against the stable model, Journal of Business & Economic Statistic, 8(2), 217-223. https://doi.org/10.2307/1391984

João Paulo Vieito & K. V. Bhanu Murthy & Vanita Tripathi (2013). Market Efficiency In G-20 Countries: The Paradox Of Financial Crisis, Annals of Financial Economics, 8(1), 1-27. https://doi.org/10.1142/S2010495213500036

Jukka Ilomaeki & Hannu Laurila (2018). The Noise Trader Effect In A Walrasian Financial Market, Advances in Decision Sciences, 22(1), 405-419. https://iads.site/wp-content/uploads/papers/2018/Ilomaki-and-Laurila_The-Noise-Trader-Effect_ADS.pdf

Joerg Osterrieder & Julian Lorenz (2017). A Statistical Risk Assessment Of Bitcoin And Its Extreme Tail Behavior, Annals of Financial Economics, 12(1), 1-19. https://doi.org/10.1142/S2010495217500038

Khoa Dang Duong & Qui Nhat Nguyen & Truong Vinh Le & Diep Van Nguyen (2021), Limit-To-Arbitrage Factors And Ivol Returns Puzzle: Empirical Evidence From Taiwan Before And During Covid-19, Annals of Financial Economics, 16(1), 1-18.

Mandal, Arindam & Bhattacharjee, Prasun. (2005). The Indian Stock Market and the Great Recession. Theoretical and Applied Economics. XIX (2012). No.3 (568), 59-76. http://store.ectap.ro/articole/698.pdf

Mandelbrot, B. (1963). The variation of certain speculative prices. The Journal of Business, 36(4), 394-419. https://www.jstor.org/stable/2351623

Markwat, T. (2014). The Rise of Global Stock Market Crash Probabilities. Quantitative Finance. 14:4, 557-571. https://doi.org/10.1080/14697688.2013.848463

Mazur, M., & Dang, M., & Vega, M. (2020). COVID-19 and the march 2020 stock market crash. Evidence from S&P 1500. Finance Research Letters, 38, 101690. https://doi.org/10.1016/j.frl.2020.101690

Officer, R.R. (1972). The Distribution of Stock Returns. Journal of the American Statistical Association, 67(340), 807-812. https://doi.org/10.2307/2284641

Rangan Gupta & Chi Keung Marco Lau & Seong-Min Yoon (2019). OPEC News Announcement Effect on Volatility in the Crude Oil Market: A Reconsideration, Advances in Decision Sciences, 23(4), 1-23. https://repository.up.ac.za/bitstream/handle/2263/77043/Gupta_OPEC_2019.pdf?sequence=1&isAllowed=y

Rastogi, S. (2014). The financial crisis of 2008 and stock market volatility - Analysis and impact on emerging economies pre and post-crisis. Afro-Asian J. of Finance and Accounting. 4. 443. DOI: 10.1504/AAJFA.2014.067017

Foss S., Korshunov D., Zachary S. (2013). An Introduction to Heavy-Tailed and Subexponential Distributions, Springer Science & Business Media.

Salisu, A.A. and Vo, X.V. (2020). Predicting stock returns in the presence

of COVID-19 pandemic: The role of health news, International Review of Financial

Analysis, 71. https://doi.org/10.1016/j.irfa.2020.101546

Samsi, Siti & Cheong, Kee-Cheok & Yusof, Zarinah. (2019). Financial Crisis, Stock Market and Economic Growth: Evidence from ASEAN-5, Southeast Asian Economies, 36. 37-56. https://www.jstor.org/stable/26664252

Thach, P. and Duc, V. (2019). Estimating sectoral systematic risk for China, Malaysia, Singapore, and Thailand. Annals of Financial Economics, 14(3), 1-18. https://doi.org/10.1142/S2010495219500118

Zhu, Z., & Bai, Z. & Vieito, J., & Wong, Wing-Keung. (2018). The Impact of the Global Financial Crisis on the Efficiency and Performance of Latin American Stock Markets. SSRN Electronic Journal. 10.2139/ssrn.3208090. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3393671

Ziemba, William T. (2020), The COVID-19 Crash in the US Stock Market. Available at https://ssrn.com/abstract=3632410.

Published

2022-01-09

How to Cite

Gali, S. (2022). Tail Behaviour of the Nifty-50 Stocks during Crises Periods. Advances in Decision Sciences, 25(4), 115-151. https://doi.org/10.47654/v25y2021i4p115-151