Stock Market Volatility Towards COVID-19 Drawbacks: Case of Rwanda Stock Exchange

Year 2021, Volume: 6 Issue: 2, 140 - 150, 30.12.2021

Edouard Sıngırankabo Jean Marie Vianney Hakızımana Jean Paul Hakızakubana Ngoboka

Abstract

The purpose of this study is to look into the impact of the COVID-19 on the Rwanda Stock Exchange, which is of interest to a variety of financial institutions and investors. We conducted an empirical analysis of the daily stock returns data from January 4th, 2020 to April 12th, 2021 of four Rwanda Stock Exchange-listed companies. The AR, MA, ARIMA, and ARCH models were applied one after another. To verify the series stationarity, we checked the time series stationarity before building an ARIMA model. The ARIMA model fit the data of all companies well, but using the Ljung-Box test, we failed to reject the null hypothesis and concluded that there are no ARCH effects present for BLR, KCB, and EQRY companies, despite the fact that the p-value of test was less than 0.05 for BOK data, indicating the ARCH effect. The results show that the AR model is adequate and can be used to model data from BLR, KCB, and EQTY because there is no serial correlation and no ARCH effect depicted in the data, and the only data of BOK can be modelled using the ARCH model and the result shows that alpha 1 is 1.000e+00 which is very high and this indicates that the BOK market is jumpy (unstable).

Keywords

AR, MA, ARIMA, ARCH, COVID-19, Financial Markets

Supporting Institution

Rwanda Stock Exchange website

COVİD-19 Dezavantajlarına Yönelik Hisse Senedi Oynaklıkları: Ruanda Borsası Örneği

Abstract

Bu çalışmanın amacı, COVID-19'un çeşitli finansal kurum ve yatırımcıların ilgisini çeken Ruanda Menkul Kıymetler Borsası üzerindeki etkisini incelemektir. Ruanda Menkul Kıymetler Borsası'nda işlem gören dört şirketin 4 Ocak 2020 ile 12 Nisan 2021 arasındaki günlük hisse senedi getirisi verilerinin ampirik bir analizini gerçekleştirdik. Çalışmada, AR, MA, ARIMA ve ARCH modelleri birbiri ardına uygulanmıştır. Serilerin durağanlığını doğrulamak için bir ARIMA modeli oluşturmadan önce zaman serisi durağanlığını kontrol ettik. ARIMA modeli tüm şirketlerin verilerine iyi uyuyor, ancak Ljung-Box testini kullanarak boş hipotezi reddetmeyi başaramadık, ARCH etkisini gösteren BOK verileri için testin p-değerinin 0.05'ten küçük olmasına rağmen, BLR, KCB ve EQRY şirketleri için ARCH etkisinin olmadığı sonucuna vardık. Sonuçlar AR modelinin yeterli olduğunu göstermektedir ve seri korelasyonlu olmadığı için BLR, KCB ve EQTY' den verileri modellemek için kullanılabilir ve verilerde ARCH etkisi tespit edilmemiştir. BOK'un tek verisi ARCH modeli kullanılarak modellenebilir ve sonuç alfa 1'in 1.000e+00 olduğunu gösteriyor ki bu çok yüksek ve bu BOK piyasasının istikrarsız olduğunu göstermektedir. 

Keywords

AR, MA, ARIMA, ARCH, COVID-19, Finansal piyasalar

References

• Abdalla, S. Z. S., & Winker, P. (2012). Modelling stock market volatility using univariate GARCH models: Evidence from Sudan and Egypt. International Journal of Economics and Finance, 4(8), 161–176. Ahmed, A. E. M., & Suliman, S. Z. (2011). Modeling stock market volatility using GARCH models evidence from Sudan. International journal of business and social science, 2(23).
• Arturo, M., Estrada, R., & Lee, M. (2020). Stagpression: The Economic and Financial Impact of COVID-19 Pandemic. Contemporary Economics, 15(1), 19-33, https://doi.org/10.2139/ssrn.3593144
• Bahamonde, N., Torres, S., & Tudor, C. A. (2018). ARCH model and fractional Brownian motion. Statistics & Probability Letters, 134, 70–78.
• Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3),307–327.
• Bollerslev, T., Chou, R. Y., & Kroner, K. F. (1992). ARCH modeling in finance: A review of the theory and empirical evidence. Journal of econometrics, 52(1-2), 5–59.
• Bollerslev, T., Engle, R. F., & Nelson, D. B. (1994). ARCH models. Handbook of econometrics, 4, 2959–3038.
• Brooks, R. (2007). Power arch modelling of the volatility of emerging equity markets. Emerging Markets Review, 8(2), 124–133.
• Celner, A. (2020). Understanding the sector impact of Banking & Capital Markets.
• Chen, Q. (2017). Market risk management for financial institutions based on GARCH family models. MA Thesis: Washington University: St. Louis
• Cheong, C. W. (2009). Modeling and forecasting crude oil markets using ARCH-type models. Energy policy, 37(6), 2346–2355.
• Dana, A.-N. (2016). Modelling and estimation of volatility using ARCH/GARCH models in Jordan’s stock market. Asian Journal of Finance & Accounting, 8(1), 152–167.
• Demir, O., & Esen, A. (2021). Destructive Economic Effects of Covid 19 and Transformation Need in Turkish Economy. Journal of Emerging Economies and Policy, 6(1), 88-105.
• Ekong, C. N., & Onye, K. U. (2017). Application of GARCH Models to Estimate and Predict Financial Volatility of Daily Stock Returns in Nigeria. International Journal of Managerial Studies and Research 5(8), 18-34.
• El-Basuon, H. (2020). Effect of COVID-19 on the Arab financial markets evidence from Egypt and KSA. IOSR Journal of Business and Management, 22(6), 14-21. https://doi.org/10.9790/487X-2206051421
• Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the econometric society, 987–1007.
• Haldar, A., & Sethi, N. (2020). The News Effect of COVID-19 on Global Financial Market Volatility. Buletin Ekonomi Moneter dan Perbankan, 24, 33-58. https://doi.org/10.21098/bemp.v24i0.1464
• Innocent, G., Shukla, J., & Mulyungi, P. (2018). Effects of macroeconomic variables on stock market performance in Rwanda. Case study of Rwanda Stock Exchange. European Journal of Economic and Financial Research, 3(1), 104-125. http://dx.doi.org/10.46827/ejefr.v0i0.364
• Kamdem, J. S., Essomba, R. B., & Berinyuy, J. N. (2020). Deep learning models for forecasting and analyzingthe implications of covid-19 spread on some commodities markets volatilities. Chaos, Solitons & Fractals, 140, 110215. https://doi.org/10.1016/j.chaos.2020.110215
• Kansiime, G. (2019). Share price change and investment decision on the Rwanda stock exchange (2011–2016). PhD thesis, University of Rwanda: Rwanda
• Mahina, J. N., Muturi, W. M., & Memba, F. S. (2014). Effect of behavioural biases on investments at the Rwanda stock exchange. International journal of social sciences and information technology, 3(3), 1917–1933.
• Mathur, S., Chotia, V., & Rao, N. (2016). Modelling the impact of global financial crisis on the Indian stock market through GARCH models. Asia-Pacific Journal of Management Research and Innovation, 12(1), 11–22.
• Moews, B., & Ibikunle, G. (2020). Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning. Physica A: Statistical Mechanics and its Applications, 547, 124392.
• Murenzi, R., Thomas, K., & Mung’atu, J. K. (2015). Modeling exchange market volatility risk in Rwanda using GARCH-EVT approach. International Journal of Thesis Projects and Dissertations (IJTPD), 3(3), 67–80.
• Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 347–370.
• Ngoboka, J. P. H., & Singirankabo, E. (2021). Dividend policy and firm value: A study of companies quoted at the Rwanda stock exchange. Journal of Research in Business and Management.
• Noella, Z. U. (2017). Stock market listing and financial performance of companies in Rwanda a case study of listed companies in Rwanda stock exchange (RSE). PhD thesis, Mount Kenya University.
• Odhiambo, J. O., Ngare, P., Weke, P., & Otieno, R. O. (2020). Modelling of covid-19 transmission in Kenya using compound poisson regression model. Journal of Advances in Mathematics and Computer Science, 101–111.
• Sansa, N. A. (2020). The Impact of the COVID-19 on the Financial Markets: Evidence from China and USA. Electronic Research Journal of Social Sciences and Humanities, (2) II, 29–39.
• Sattar, M. A., Arcilla, F. E., & Sattar, M. F. (2020). The response of financial market indices to covid-19 pandemic. Financial Studies, 24(3 (89)), 83-92.
• Tsay, R. S., & Tiao, G. C. (1984). Consistent estimates of autoregressive parameters and extended sample autocorrelation function for stationary and nonstationary ARMA models. Journal of the American Statistical Association, 79(385), 84–96.
• Wang, Y., Hu, M., Li, Q., Zhang, X.-P., Zhai, G., & Yao, N. (2020). Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with covid-19 in an accurate and unobtrusive manner. arXiv preprint arXiv:2002.05534.
• Wei, J. (2012). Modeling and predicting of different stock markets with GARCH model. Master Thesis, Uppsala University: Sweden.