Volatility Modelling of Stock Returns in the Petroleum Marketing Sector of the Nigerian Stock Exchange
DOI:
https://doi.org/10.47672/ajf.777Keywords:
Nigeria Stock Exchange (NSE), Jumps, Volatility, Stock returns, ARCH/GARCHAbstract
Introduction: Financial markets play key role in the growth and sustainability of the economy. However, high levels of volatility in the markets may adversely affect the financial system and weaken the economy.
Purpose: This paper examined the presence of volatility in the stock returns of the petroleum marketing sector of the Nigerian Stock Exchange using ten petroleum marketing firms quoted on the Nigerian Stock Exchange for a period of twenty-four months that is from January 2017 to December 2018.
Methodology: The study adopted empirical research design using time series data where ordinary least squares was employed to run the analysis through the use of ARCH/GARCH models.
Findings: Among other results, it was seen that a unit increase in volatility (VLT) will lead to 0.006916 decrease in stock returns (STR). Also, the result of R-squared implies that about eight per cent (8%) of the changes in stock returns (STR) is captured by volatility (VLT) while the remaining ninety-two per cent (92%) of the variation in the model is captured by the error term. The ARCH effect observed is statistically significant. The coefficient of the GARCH effect which is significantly positive at 5% shows that past volatility of stock market return is significant and has effect on current volatility.
Unique Contribution to theory, Practice and Policy: The implication of this is that an increase in volatility is linked to a significant increase of returns, which is an expected result and thus conforms to economic theory. The results of static and dynamic forecasting of GARCH volatility showed that the volatility is stable. As a result, investors can hold the stock. Among other things, the author recommends that Government should make sufficient regulatory effort that will improve efficiency of stocks performance and reduce volatility aimed at boosting investors' confidence in the petroleum marketing sector and since the various ARCH and GARCH models showed volatility movement in stock returns, Nigerian government should look for new ways to diversify the economy from dependence on oil and explore other sectors like manufacturing sector and agricultural sector to reduce volatility in the economy and the overall effect on it.
Downloads
References
Abdalla, S. Z & Winker, P. (2012) Modelling stock market volatility using univariate GARCH models: Evidence from Sudan and Egypt. International Journal of Economics and Finance; 4(8), 50-50
Adeosun, M. E. & Edeki, S.O. & Ugbebor, Olabisi O. (2015) Stochastic analysis of stock market price models: A Case Study of the Nigerian Stock Exchange (NSE). WSEAS TRANSACTIONS on MATHEMATICS, 14(2), 2224-2880
Adesina, O.S, Oyewole O. & Adekola, L.O. (2017). Modeling volatility in nigeria foreign exchange market using GARCH-type Models, mathematical theory and modeling, www.iiste.org, 7 (10)
Adesina, S. K. (2013) Modelling stock market return volatility: GARCH evidence from Nigerian stock exchange. International Journal of Financial Management, 3, (3), 37-46
Asemota, O. J & Ekejiuba, U. C. (2017). An application of asymmetric GARCH models on volatility of banks equity in Nigeria's stock market. CBN Journal of Applied Statistics, 8 (1), 73-99
Babajide K. & Akpan U. (2018). Nigeria's oil revenue rises by 129% to N9.44 trillion in 2018. (http://www.vanguardngr.com/)
Baker, S.R., Bloom, N., Davis, S. & Sammon, M. (2019). What triggers stock market jumps?
Bedowska-Sojka, B. (2015). Liquidity dynamics around jumps. The evidence from the warsaw stock exchange, https://www.researchgate.net/publication/282443089
Bibinger, M & Winkelmann, L. (2018). Common price and volatility jumps in noisy high-frequency data, Electronic Journal of Statistics, 12, 2018-2073.
Dritsaki, C. (2017). An empirical evaluation in GARCH volatility modeling: Evidence from the stockholm stock exchange. Journal of Mathematical Finance, 7, 366-390.
Duffie, D. & Pan, J. (2001). Analytical value-at-risk with jumps and credit risk, Journal of Finance and Stochastics, 5(2), 155-180.
Emenike, K. O. (2010). Modeling stock returns volatility in Nigeria using GARCH models. https://mpra.ub.uni-muenchen.de/22723/ MPRA Paper No. 22723,
Hanousek, J., Kocenda, E. & Novotny, J. (2014). Price jumps on European stock markets, Borsa Istanbul Review, http://www.elsevier.com/journals/borsa-istanbul-review/2214-8450, http://dx.doi.org/10.1016/j.bir.2013.11.003
Huang, X. & Tauchen, G., (2005), The relative contribution of jumps to total price variance, Journal of Financial Econometrics, 3 (4), 456-499.
Jarow R.A. & Rosenfeld E.R., (1984), Jump risk and the intertemporal capital asset pricing model, The Journal of Business, 57, 3, 337-351.
Jiang, G. J. & Yao, T. (2008). Stock price jumps and cross-sectional return predictability. Journal of Financial and Quantitative Analysis, 48 (5), 1519-1544.
Kumari, S. (2018). Modelling stock return volatility in India. Munich Personal RePEc Archive, MPRA Paper No. 86673. University Library of Munich, Germany. https://mpra.ub.uni-muenchen.de/86673/
Lee, S. S. & Mykland, P.A. (2012).. Jumps in financial markets: A new nonparametric test and jump dynamics. Review of Financial Studies, 21 (6), 2535-2563.
Maqsood, A., Safdar, S., Shafi, R. & Lelit, N.J. (2017). Modeling stock market volatility using GARCH models: A case study of Nairobi securities exchange (NSE). Open Journal of Statistics, 7, 369-381.
Merton, R. C. (1976). Option pricing when underlying stock returns are discontinuous. Journal of Financial Economics, 3 (1-2), 125 - 144.
Merton, R. C. (1994). Influence of mathematical models in finance on practice: Past, Present, and future. Philosophical Transactions of the Royal Society of London. Series A: physical and Engineering Sciences, 347, 451 - 463.
Osarumwense, O. I. (2015). Day of the week effect in the Nigerian stock market returns and volatility: Does the distributional assumptions influence disappearance? European Financial and Accounting Journal, 10 (4), 33-44.
Peiris, T. S. G. & Peiris, T. U. I. (2011). Measuring stock market volatility in an emerging economy: Empirical evidence from the Colombo Stock Exchange (CSE), International Research conference, university of Kelaniya - Sri lanka,
Piazzesi M. (2005), Bond yields and the federal reserve, Journal of Political Economy, 113 (2), 311-344.
Ramos, F., Waddington, I., Mussili, G.L., Pinto, Y.L.O., Pimentel, A. & Carvalho, L. M. (2016). The nature of jumps in Brazil's stock market, http://sbfin.org.br/wp-content/uploads/2016/05/137-The-nature-of-Jumps-in-Brazil%E2%80%99s-stock-market.pdf
Tankov, P. & Voltchkova, E. (2009). Jump-diffusion models: a practitioner's guide. Banque et Marchs Journal, 99 (1), 24 -48
Taruvinga, B., and Kang, B, & Sklibosios Nikitopoulos, C. (2018). Pricing american options with jumps in asset and volatility. Quantitative Finance Research Centre, University of Technology, Sydney. RePEc:uts:rpaper:394 Research Paper Series 394,
Thanh N. V. (2018). Impact of crude oil volatility on stock returns: evidence from Southeast Asian markets. Southeast Asian Journal of Economics 7(2), 107 - 125.
Wei, S. X. & Zhang, C. (2006). Why did individual stocks become more volatile? The Journal of Business, 79 (1), 259-292.
Yaya, S. O., Bada, S. A. & Atoi, N. V. (2016). Volatility in the Nigerian stock market: Empirical Application of Beta-t-GARCH Variants. CBN Journal of Applied Statistics, 7 (2), 137 - 157
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 Adolphus J. Toby, Glory B. Austen
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
