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South African Sector Return Correlations: using DCC and ADCC Multivariate GARCH techniques to uncover the underlying dynamics

Author

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  • Nico Katzke

    (Department of Economics, University of Stellenbosch)

Abstract

This paper explores the dynamics of return co-movements between the largest economic sectors in South Africa, specifically with a view to shed light on the inter-sector diversification potential of domestic investors over time. It has been widely documented that investors have a home-bias when it comes to investing, and as such may be exposed to periods of increased co-movement between assets held locally across different sectors in their portfolios. Such periods of increased homogeneity in the movement of asset prices negate the benefits from diversification within the domestic financial market. The paper utilizes Dynamic Conditional Correlation (DCC) and Asymmetric-DCC Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MV-GARCH) techniques to isolate the time-varying conditional correlations from the conditional variance component. These series are then used to study whether changes in market conditions and overall sentiment influence the dynamics and aggregate level of co-movement between sectors. The results firstly suggest that using static measures of historic co-movement between asset returns across sectors in order to evaluate a portfolio’s diversification potential are inaccurate. Significant leverage effects are also found in the dynamics of co-movement between the sector pairs, with negative shocks being followed in all cases by higher aggregate levels of co-movement. The results also suggest that periods of heightened global- and domestic market uncertainty magnifies the co-movements between sectors and in so doing undermines the ability of investors to diversify across local sectors.

Suggested Citation

  • Nico Katzke, 2013. "South African Sector Return Correlations: using DCC and ADCC Multivariate GARCH techniques to uncover the underlying dynamics," Working Papers 17/2013, Stellenbosch University, Department of Economics.
  • Handle: RePEc:sza:wpaper:wpapers193
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    File URL: https://www.ekon.sun.ac.za/wpapers/2013/wp172013/wp-17-2013.pdf
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    References listed on IDEAS

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    2. Amira Akl Ahmed & Rania Ihab Naguib, 2018. "DCCs among Sector Indexes and Dynamic Causality between Foreign Exchange and Equity Sector Volatility: Evidence from Egypt," Applied Economics and Finance, Redfame publishing, vol. 5(1), pages 14-28, January.
    3. Škrinjarić Tihana, 2015. "Measuring Dynamics of Risk and Performance of Sector Indices on Zagreb Stock Exchange," Croatian Review of Economic, Business and Social Statistics, Sciendo, vol. 1(1-2), pages 27-41, December.
    4. Škrinjarić Tihana & Šego Boško, 2016. "Dynamic Portfolio Selection on Croatian Financial Markets: MGARCH Approach," Business Systems Research, Sciendo, vol. 7(2), pages 78-90, September.
    5. Parul Bhatia & Priya Gupta, 2020. "Sub-prime Crisis or COVID-19: A Comparative Analysis of Volatility in Indian Banking Sectoral Indices," FIIB Business Review, , vol. 9(4), pages 286-299, December.
    6. Rafique, Amir & Iqbal, Khurram & Zakaria, Muhammad & Mujtaba, Ghulam, 2019. "Investigating ICAPM with mean-reverting dynamic conditional correlation: Evidence from an emerging stock exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 514-523.
    7. Ashfaq, Saleha & Tang, Yong & Maqbool, Rashid, 2019. "Volatility spillover impact of world oil prices on leading Asian energy exporting and importing economies’ stock returns," Energy, Elsevier, vol. 188(C).
    8. Morema, Kgotso & Bonga-Bonga, Lumengo, 2018. "The impact of oil and gold price fluctuations on the South African equity market: volatility spillovers and implications for portfolio management," MPRA Paper 87637, University Library of Munich, Germany.

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    More about this item

    Keywords

    Conditional Variance; Multivariate GARCH; Dynamic Conditional Correlation; Sector Indices;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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