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Modelling South African Currency Crises As Structural Changes In The Volatility Of The Rand

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  • Andrew Stuart Duncan
  • Guangling“dave” Liu

Abstract

This study tests the theory that currency crises are associated with sudden large changes in the structure of foreign exchange market volatility. Due to increases in market uncertainty, crisis periods exhibit abnormally high levels of volatility. By studying short‐term changes in volatility dynamics, it is possible to identify the start and end dates of crisis periods with a high degree of precision. We use the iterative cumulative sum of squares algorithm to detect multiple shifts in the volatility of rand returns between January 1994 and March 2009. Dummy variables controlling for the detected shifts in variance are incorporated in a generalised autoregressive conditional heteroscedasticity modelling framework. The analysis indicates that previously identified crisis periods in the rand coincide with significant structural changes in market volatility.

Suggested Citation

  • Andrew Stuart Duncan & Guangling“dave” Liu, 2009. "Modelling South African Currency Crises As Structural Changes In The Volatility Of The Rand," South African Journal of Economics, Economic Society of South Africa, vol. 77(3), pages 363-379, September.
    • Handle: RePEc:bla:sajeco:v:77:y:2009:i:3:p:363-379
    DOI: 10.1111/j.1813-6982.2009.01215.x
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    Cited by:

    1. Sean Joss Gossel & Nicholas Biekpe, 2012. "The effects of capital inflows on South Africa's economy," Applied Financial Economics, Taylor & Francis Journals, vol. 22(11), pages 923-938, June.
    2. Duncan, Andrew S. & Kabundi, Alain, 2013. "Domestic and foreign sources of volatility spillover to South African asset classes," Economic Modelling, Elsevier, vol. 31(C), pages 566-573.
    3. Sandile Hlatshwayo & Mr. Magnus Saxegaard, 2016. "The Consequences of Policy Uncertainty: Disconnects and Dilutions in the South African Real Effective Exchange Rate-Export Relationship," IMF Working Papers 2016/113, International Monetary Fund.
    4. Fiona Tregenna & Kabeya C. Mulamba, 2019. "Spatial dependence of per capita property tax income in South Africa," Working Papers 202, Economic Research Southern Africa.
    5. King, Daniel & Botha, Ferdi, 2015. "Modelling stock return volatility dynamics in selected African markets," Economic Modelling, Elsevier, vol. 45(C), pages 50-73.
    6. Nasha Maveé & Mr. Roberto Perrelli & Mr. Axel Schimmelpfennig, 2016. "Surprise, Surprise: What Drives the Rand / U.S. Dollar Exchange Rate Volatility?," IMF Working Papers 2016/205, International Monetary Fund.

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • F31 - International Economics - - International Finance - - - Foreign Exchange

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