2017 | Volume 13 | Issue 1 | 1-9
Article title

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

Title variants
Languages of publication
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.
Physical description
  • Department of Economics, Faculty of Business and Economic Studies Nelson Mandela Metropolitan University, South Africa
  • Benos, E., Sagade, S. (2012). High-frequency trading behaviour and its impact on market quality: Evidence from the UK equity market. (Bank of England Working Paper Series, Working Paper No. 469).
  • Burke, S., Hunter, J. (2005). Modelling non-stationary economic time series: A multivariate approach. Palgrave, Basingstoke.
  • Carrion, A. (2013). Very fast money: High frequency trading on NASDAQ. Journal of Financial Markets, 16, 637-645.
  • Enders, W., Silkos P. (2001). Cointegration and threshold adjustment. Journal of Business and Economic Statistics, 19(2), 166-176.
  • Engle, R., Granger, C. (1987). Co-integration and error correction: Representation, estimation, and testing. Economertrica, 55, 369-384.
  • Hansbrouck, J., Saar, G. (2013). Low-latency trading. Journal of Financial Markets, 16, 741-770.
  • Hansen, B. (2000). Sample splitting and threshold estimation. Econometrica, 68, 575-603. 10.1111/1468-0262.00124
  • Lee, E. (2015). High frequency trading in the Korean index futures market. Journal of Futures Market, 35, 31-51. 10.1002/fut.21640
  • Manahov, V., Hudson, R. (2014). The implications of high-frequency trading on market efficiency and price discovery. Applied Economics Letters, 21(16), 1148-1151.
  • Manahov, V., Hudson, R., Gebka, B. (2014). Does high frequency trading affect technical analysis and market efficiency? And if so, how? Journal of International Financial Markets, Institutions and Money, 28, 131-157.
  • Phiri, A. (2016). Long-run equilibrium adjustment between inflation and stock market returns in South Africa: A nonlinear perspective. International Journal of Sustainable Economy, 9(1), 19-33.
  • Riordan, R., Storkenmaier, A. (2012). Latency, liquidity, and price discovery. Journal of Financial Studies, 43, 767-797.
  • Viljoen, T., Westerholm, J., Zheng, H. (2014). Algorithmic trading, liquidity and price discovery: An intraday analysis of the SPI 200 futures. The Financial Review, 49(2), 245-270. 10.1111/fire.12034
  • Virgilio, G. (2016). The impact of high-frequency trading on marketing volatility. The Journal of Trading, 11(2), 55-63. 10.3905/jot.2016.11.2.055
  • Zhang, F. (2010). The effect of high-frequency trading on stock volatility and price discovery. Retrieved on March 25, 2017,
Document Type
Publication order reference
YADDA identifier
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.