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Threshold convergence between the federal fund rate and South African equity returns around the colocation period

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  • Phiri, Andrew

Abstract

Using weekly data collected from 20.09.2008 to 09.12.2016, this paper uses dynamic threshold adjustment models to demonstrate how the introduction of high-frequency and algorithmic trading on the Johannesburg Stock Exchange (JSE) has altered convergence relations between the federal fund rate and equity returns for aggregate and disaggregate South African market indices. We particularly find that for the post-crisis period, the JSE appears to operate more efficiently, in the weak-form sense, under high frequency trading platforms.

Suggested Citation

  • Phiri, Andrew, 2017. "Threshold convergence between the federal fund rate and South African equity returns around the colocation period," Business and Economic Horizons (BEH), Prague Development Center (PRADEC), vol. 13(1).
    • Handle: RePEc:ags:pdcbeh:264621
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    References listed on IDEAS

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    1. Bruce E. Hansen, 2000. "Sample Splitting and Threshold Estimation," Econometrica, Econometric Society, vol. 68(3), pages 575-604, May.
    2. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 39(3), pages 106-135.
    3. Michael Goldstein & Tina Viljoen & P. Joakim Westerholm & Hui Zheng, 2014. "Algorithmic Trading, Liquidity, and Price Discovery: An Intraday Analysis of the SPI 200 Futures," The Financial Review, Eastern Finance Association, vol. 49(2), pages 245-270, May.
    4. Carrion, Allen, 2013. "Very fast money: High-frequency trading on the NASDAQ," Journal of Financial Markets, Elsevier, vol. 16(4), pages 680-711.
    5. Viktor Manahov & Robert Hudson, 2014. "The implications of high-frequency trading on market efficiency and price discovery," Applied Economics Letters, Taylor & Francis Journals, vol. 21(16), pages 1148-1151, November.
    6. Andrew Phiri, 2017. "Long-run equilibrium adjustment between inflation and stock market returns in South Africa: a nonlinear perspective," International Journal of Sustainable Economy, Inderscience Enterprises Ltd, vol. 9(1), pages 19-33.
    7. Enders, Walter & Siklos, Pierre L, 2001. "Cointegration and Threshold Adjustment," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(2), pages 166-176, April.
    8. Benos, Evangelos & Sagade, Satchit, 2012. "High-frequency trading behaviour and its impact on market quality: evidence from the UK equity market," Bank of England working papers 469, Bank of England.
    9. Hasbrouck, Joel & Saar, Gideon, 2013. "Low-latency trading," Journal of Financial Markets, Elsevier, vol. 16(4), pages 646-679.
    10. Manahov, Viktor & Hudson, Robert & Gebka, Bartosz, 2014. "Does high frequency trading affect technical analysis and market efficiency? And if so, how?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 28(C), pages 131-157.
    11. Eun Jung Lee, 2015. "High Frequency Trading in the Korean Index Futures Market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 35(1), pages 31-51, January.
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    More about this item

    Keywords

    Financial Economics; Risk and Uncertainty;

    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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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