The determinants of volatility connectedness of South African equity super sectors
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DOIhttp://dx.doi.org/10.21511/imfi.21(4).2024.16
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Article InfoVolume 21 2024, Issue #4, pp. 200-213
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The paper aims to explore the determinants of total volatility connectedness of nine super sectors on the Johannesburg Stock Exchange (JSE) market from 3rd January 2006 to 31st December 2021. These sectors are Automobile and Parts, Chemical, Telecommunication, Technology, Energy, Health, Finance, Insurance, and General Industrials. The paper applied Diebold and Yilmaz connectedness matrix and the time-varying parameter – vector autoregressive (TVP-VAR) model to determine the sectorial total volatility connectedness index (STVCI). After that, the nonlinear autoregressive distributed lag model (NARDL) was used to determine the asymmetric effects and the drivers of STVCI. It was found that the partial sum decomposition of the South African volatility index (SAVI) and Economic Policy Uncertainty Index (EPU) are the key determinants of the STVCI both in the long and short run. However, domestic market return (DMR) shows no significant asymmetric effect on STVCI. The study concluded that SAVI and EPU are the key determinants of volatility connectedness among the JSE super-sectors. The results unveil important implications for sectorial investors and policymakers on potential regulations and stability of the significant determinants of spillover risk.
- Keywords
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JEL Classification (Paper profile tab)C05, C32, G01, G11
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References42
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Tables8
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Figures2
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- Figure 1. Total dynamic volatility connectedness between super sectors on the Johannesburg Stock Exchange for the full sample
- Figure 2. Graphs of CUSUM, CUSUMSQ, and multiplier graph
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- Table 1. Unit root for volatilities of super sectors
- Table 2. Bounds test for cointegration for the long-run ARDL and NARDL
- Table 3. NARDL asymmetry test result
- Table A1. Descriptive statistics for sector volatility
- Table A2. Correlation of sectorial volatility
- Table A3. Unit root test for determinants (returns)
- Table A4. Average sectorial dynamic volatility connectedness table for the full sample period
- Table А5. Error correction model for ARDL and NARDL models
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- Agyei, S. K., & Bossman, A. (2023). Exploring the dynamic connectedness between commodities and African equities. Cogent Economics & Finance, 11(1), 2186035.
- Akinola, G. W., Anderu, K. S., & Mbonigaba, J. (2021). The effect of a new wave of COVID-19 on the stock market performance: Evidence from the twenty JSE listed companies in South Africa. Investment Management and Financial Innovations, 67-79.
- Antonakakis, N., Gabauer, D., & Gupta, R. (2019). International monetary policy spillovers: Evidence from a time-varying parameter vector autoregression. International Review of Financial Analysis, 65, 101382.
- Antonakakis, N., Chatziantoniou, I., & Gabauer, D. (2020). Refined measures of dynamic connectedness based on time-varying parameter vector autoregression. Journal of Risk and Financial Management, 13(4), 84.
- Awartani, B., Aktham, M., Cherif, G., (2016). The connectedness between crude oil and financial markets: evidence from implied volatility indices. Journal of Commodity Market.
- Batten, J. A., Ciner, C., & Lucey, B. M. (2010). The macroeconomic determinants of volatility in precious metals markets. Resources Policy, 35(2), 65-71.
- Bekiros, S. D. (2014). Contagion, decoupling and the spillover effects of the US financial crisis: Evidence from the BRIC markets. International Review of Financial Analysis, 33, 58-69.
- Bouri, E., Lucey, B., Saeed, T., & Vo, X. V. (2021). The realized volatility of commodity futures: Interconnectedness and determinants. International Review of Economics & Finance, 73, 139-151.
- Chokoe, K. (2022). A Multivariate GARCH approach to Cross-Asset contagion in South Africa (Doctoral dissertation). University of Johannesburg).
- Chowdhury, S. S. H., & Irfan, M. (2022). A Study on the Time-Varying Volatility Connectedness between the Sectors in the Indian Stock Market. Montenegrin Journal of Economics, 18(3), 77-88.
- Diebold, F. X. (2007). Elements of Forecasting (4th ed.) (pp. 230-23). Thomson South-Western.
- Diebold, F. X., & Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. The Economic Journal, 119, 158-171.
- Diebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting 28, 57-66.
- Diebold, F. X., & Yilmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182, 119-134.
- Duncan, A., & Kabundi, A. (2011). Volatility spillovers across South African asset classes during domestic and foreign financial crises. Economic Research Southern Africa, Working Paper, 202(1), 517-532.
- Engle, R. F., Ito, T., & Lin, W.-L. (1990). Meteor showers or heat waves? Heteroskedastic intra-daily volatility in the foreign exchange market. Econometrica 58(3), 525-542.
- Fry-McKibbin, R., Martin, V. L., & Tang, C. (2014). Financial contagion and asset pricing. Journal of Banking & Finance, 47, 296-308.
- Garman, M. B., & Klass, M. J. (1980). On the estimation of security price volatilities from historical data. Journal of Business, 67-78.
- Heymans, A., & Da Camara, R. (2013). Measuring spill-over effects of foreign markets on the JSE before, during and after international financial crises. South African Journal of Economic and Management Sciences, 16(4), 418-434.
- Hkiri, B., Hammoudeh, S., Aloui, C., & Yarovaya, L. (2017). Are Islamic indexes a safe haven for investors? An analysis of total, directional and net volatility spillovers between conventional and Islamic indexes and importance of crisis periods. Pacific-Basin Finance J., 43, 124-150.
- Hussain Shahzad, S. J., Bouri, E., Arreola-Hernandez, J., Roubaud, D., & Bekiros, S. (2019). Spillover across Eurozone credit market sectors and determinants. Applied Economics, 51(59), 6333-6349.
- Inekwe, J. N. (2020). Liquidity connectedness and output synchronisation. Journal of International Financial Markets, Institutions and Money, 66, 101208.
- Karali, B., & Ramirez, O. A. (2014). Macro determinants of volatility and volatility spillover in energy markets. Energy Economics, 46, 413-421.
- Kawawa, D., & Hoveni, J. (2017). Inflation Hedging With South African Stocks: A JSE Sectoral Analysis.
- Koop, G., & Korobilis, D. (2013). Large time-varying parameter VARs. Journal of Econometrics, 177(2), 185-198.
- Kurz, M., Jin, H., & Motolese, M. (2005). Determinants of stock market volatility and risk premia. Annals of Finance, 1, 109-147.
- Liew, P. X., Lim, K. P., & Goh, K. L. (2022). The dynamics and determinants of liquidity connectedness across financial asset markets. International Review of Economics & Finance, 77, 341-358.
- Meyer, D. F., Manete, T., & Muzindutsi, P. F. (2017). The impact of government expenditure and sectoral investment on economic growth in South Africa. Journal of Advanced Research in Law and Economics, 8(6), 1844-1855.
- Moodley, F., Nzimande, N., & Muzindutsi, P. F. (2022). Stock Returns Indices and Changing Macroeconomic Conditions: Evidence from the Johannesburg Securities Exchange. The Journal of Accounting and Management, 12(3).
- Moratis, G. (2020). Quantifying the spillover effect in the cryptocurrency market. Finance Research Letters, 38, 101534.
- Muzindutsi, P. F., Obalade, A. A., & Gaston, R. T. (2020). Financial crisis and stock return volatility of the JSE general mining index: GARCH modelling approach. The Journal of Accounting and Management, 10(3).
- Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 28.
- Raza, S. A., & Jawaid, S. T. (2014). Foreign capital inflows, economic growth and stock market capitalization in Asian countries: an ARDL bound testing approach. Quality & Quantity, 48, 375-385.
- Shen, Y. Y., Jiang, Z. Q., Ma, J. C., Wang, G. J., & Zhou, W. X. (2021). Sector connectedness in the Chinese stock markets. Empirical Economics, 1-28.
- Shi, Y., Wang, L., & Ke, J. (2021). Does the US-China trade war affect co-movements between US and Chinese stock markets? Research in International Business and Finance, 58, 101477.
- Su, X. (2020). Dynamic behaviours and contributing factors of volatility spillovers across G7 stock markets. The North American Journal of Economics and Finance, 53, 101218.
- Vo, D. H. (2023). Volatility spillovers across sectors and their magnitude: A sector-based analysis for Australia. Plos one, 18(6), e0286528.
- Wu, F., Zhang, D., & Zhang, Z. (2019). Connectedness, and risk spillovers in China’s stock market: A Sectoral analysis. Economic Systems.
- Wyrobek, J., Stańczyk, Z., & Zachara, M. (2016) Global financial crisis and the decoupling hypothesis. In Wilimowska, Z., Borzemski, L., Grzech, A., & Świątek, J. (Eds.), Information systems architecture and technology: proceedings of 36th international conference on information systems architecture and technology – ISAT 2015 (part IV) (pp. 51-61). Springer International Publishing.
- Yarovaya, L., & Lau, M. C. K. (2016). Stock market comovement around the Global Financial Crisis: Evidence from the UK, BRICS and MIST markets. Research in International Business and Finance 37, 605-619.
- Zhang, W., Zhuang, X., Wang, J., & Lu, Y. (2020). Connectedness and systemic risk spillovers analysis of Chinese sectors based on tail risk network. The North American Journal of Economics and Finance, 54, 101248.
- Zhang, B., & Wang, P. (2014). Return and volatility spillovers between China and world oil markets. Economic Modelling, 42, 413-420.