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Hedge Ratios in South African Stock Index Futures

Author

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  • Stavros Degiannakis

    (Stavros Degiannakis, Department of Economics, University of Portsmouth, Portsmouth Business School, Portsmouth, Portland Street, PO1 3DE, United Kingdom.)

  • Christos Floros

    (Christos Floros (corresponding author), Department of Economics, University of Portsmouth, Portsmouth Business School, Portsmouth, Portland Street, PO1 3DE, United Kingdom. E-mail: Christos.Floros@port.ac.uk)

Abstract

This article examines hedging in South African stock index futures market. The hedge ratios are estimated by six econometric techniques: the standard OLS regression, simple and vector error correction models, the ECM with generalised autoregressive heteroskedasticity (GARCH), as well as time-varying CCC-ARCH and Diag-BEKK ARCH models. The empirical results show that the ECM-GARCH model (capturing volatility clustering) provides best hedging ratios, while CCC-ARCH is superior to OLS, ECM and VECM. We conclude that there is not a unique model specification for measuring hedge ratios. For each market (emerging and mature), a model’s comparative analysis must be conducted in order to extract the best performing model.

Suggested Citation

  • Stavros Degiannakis & Christos Floros, 2010. "Hedge Ratios in South African Stock Index Futures," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 9(3), pages 285-304, December.
    • Handle: RePEc:sae:emffin:v:9:y:2010:i:3:p:285-304
    DOI: 10.1177/097265271000900302
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    Citations

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    Cited by:

    1. Mohd Aminul Islam, 2017. "An Empirical Evaluation of Hedging Effectiveness of Crude Palm Oil Futures Market in Malaysia," International Journal of Economics and Financial Research, Academic Research Publishing Group, vol. 3(11), pages 303-314, 11-2017.
    2. Stavros Degiannakis & Christos Floros & Enrique Salvador & Dimitrios Vougas, 2022. "On the stationarity of futures hedge ratios," Operational Research, Springer, vol. 22(3), pages 2281-2303, July.
    3. Lumengo Bonga-Bonga & Ekerete Umoetok, 2016. "The effectiveness of index futures hedging in emerging markets during the crisis period of 2008-2010: Evidence from South Africa," Applied Economics, Taylor & Francis Journals, vol. 48(42), pages 3999-4018, September.
    4. ÇELİK, İsmail, 2021. "Optimal Hedge Ratio In Turkish Stock Index Futures Market: A Deco-Fiaparch Approach," Studii Financiare (Financial Studies), Centre of Financial and Monetary Research "Victor Slavescu", vol. 25(4), pages 17-33, December.
    5. Katsiampa, Paraskevi & Corbet, Shaen & Lucey, Brian, 2019. "High frequency volatility co-movements in cryptocurrency markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 62(C), pages 35-52.
    6. Konstantinos Gkillas & Dimitrios Vortelinos & Christos Floros & Athanasios Tsagkanos, 2019. "Economic News Releases and Financial Markets in South Africa," Economies, MDPI, vol. 7(4), pages 1-13, November.
    7. Degiannakis, Stavros & Filis, George & Hassani, Hossein, 2018. "Forecasting global stock market implied volatility indices," Journal of Empirical Finance, Elsevier, vol. 46(C), pages 111-129.

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    More about this item

    Keywords

    JEL Classification: G13; JEL Classification: G15; Hedging; hedge ratio; futures; SAFEX; OLS; ECM; VECM; GARCH;
    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
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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