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Stochastic volatility models for exchange rates and their estimation using quasi-maximum-likelihood methods: an application to the South African Rand

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  • M. Kulikova
  • D. Taylor

Abstract

This paper is concerned with the volatility modeling of a set of South African Rand (ZAR) exchange rates. We investigate the quasi-maximum-likelihood (QML) estimator based on the Kalman filter and explore how well a choice of stochastic volatility (SV) models fits the data. We note that a data set from a developing country is used. The main results are: (1) the SV model parameter estimates are in line with those reported from the analysis of high-frequency data for developed countries; (2) the SV models we considered, along with their corresponding QML estimators, fit the data well; (3) using the range return instead of the absolute return as a volatility proxy produces QML estimates that are both less biased and less variable; (4) although the log range of the ZAR exchange rates has a distribution that is quite far from normal, the corresponding QML estimator has a superior performance when compared with the log absolute return.

Suggested Citation

  • M. Kulikova & D. Taylor, 2013. "Stochastic volatility models for exchange rates and their estimation using quasi-maximum-likelihood methods: an application to the South African Rand," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(3), pages 495-507.
    • Handle: RePEc:taf:japsta:v:40:y:2013:i:3:p:495-507
    DOI: 10.1080/02664763.2012.740791
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    Cited by:

    1. Marianne Matthee & André Heymans, 2013. "How South African SMEs Can Become Better Candidates for Export Finance," Managing Global Transitions, University of Primorska, Faculty of Management Koper, vol. 11(4 (Winter), pages 391-407.
    2. Kim, Dongwhan & Kang, Kyu Ho, 2021. "Conditional value-at-risk forecasts of an optimal foreign currency portfolio," International Journal of Forecasting, Elsevier, vol. 37(2), pages 838-861.
    3. Dondukova Oyuna & Liu Yaobin, 2021. "Forecasting the Crude Oil Prices Volatility With Stochastic Volatility Models," SAGE Open, , vol. 11(3), pages 21582440211, July.

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