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New Evidence on the Predictability of South Africa FX Volatility in Heterogeneous Bilateral Markets

Author

Listed:
  • Gordon H. Dash
  • Nina Kajiji

    (University of Rhode Island)

Abstract

The purpose of this paper is to model the nonparametric realized volatility of the U.S. based futures contract for dollar exchange with the South African Rand (ZAR). We find that the Kajiji-4 Bayesian regularization radial basis function neural network confirms the hypothesis that bilateral mineral alliances contribute to the observed volatility patterns of the ZAR contract. We also confirm the role of conditional volatility, trade-weighted state variables and news effects from the U.S. on the ZAR volatility prediction. Finally, the modelling results provide new evidence to support the heterogeneous trading hypothesis across the bilateral trade dimensions at the daily level.

Suggested Citation

  • Gordon H. Dash & Nina Kajiji, 2003. "New Evidence on the Predictability of South Africa FX Volatility in Heterogeneous Bilateral Markets," The African Finance Journal, Africagrowth Institute, vol. 5(1), pages 1-15.
    • Handle: RePEc:afj:journl:v:5:y:2003:i:1:p:1-15
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    Cited by:

    1. Gordon H. Dash & Nina Kajiji & Domenic Vonella, 2018. "The role of supervised learning in the decision process to fair trade US municipal debt," EURO Journal on Decision Processes, Springer;EURO - The Association of European Operational Research Societies, vol. 6(1), pages 139-168, June.

    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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