The determinants of volatility connectedness of South African equity super sectors

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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.

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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
    • 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
    • Conceptualization
      Babatunde Samuel Lawrence, Adefemi A. Obalade
    • Data curation
      Babatunde Samuel Lawrence
    • Formal Analysis
      Babatunde Samuel Lawrence, Adefemi A. Obalade
    • Funding acquisition
      Babatunde Samuel Lawrence, Mishelle Doorasamy, Adefemi A. Obalade
    • Investigation
      Babatunde Samuel Lawrence
    • Methodology
      Babatunde Samuel Lawrence
    • Project administration
      Babatunde Samuel Lawrence, Mishelle Doorasamy, Adefemi A. Obalade
    • Resources
      Babatunde Samuel Lawrence, Mishelle Doorasamy, Adefemi A. Obalade
    • Software
      Babatunde Samuel Lawrence
    • Validation
      Babatunde Samuel Lawrence, Mishelle Doorasamy, Adefemi A. Obalade
    • Visualization
      Babatunde Samuel Lawrence, Mishelle Doorasamy, Adefemi A. Obalade
    • Writing – original draft
      Babatunde Samuel Lawrence
    • Writing – review & editing
      Babatunde Samuel Lawrence, Mishelle Doorasamy, Adefemi A. Obalade
    • Supervision
      Mishelle Doorasamy, Adefemi A. Obalade