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Investigating Structural break-GARCH-based Unit root test in US exchange rates

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  • Yaya, OlaOluwa S
  • Akinlana, Damola M
  • Ogbonna, Ahamuefula E

Abstract

This paper applied a structural break-GARCH-based unit root test in studying the US exchange rates for twenty-two different currencies across America, Europe, Asia-Pacific and Southern Africa. The study employed three different data frequencies – daily, weekly and monthly with a view to understand the dynamics of a high frequency series that is characterized by alternating trend patterns and plausible presence of structural breaks. The chosen sample interval included periods of financial crisis or peculiar events. The exchange rates were found to exhibit ARCH effects at higher lags, thus informing the adaptation of the more parsimonious GARCH process in the residuals in contrast to the white noise disturbance assumption. The non-trended and trended structural break-GARCH-based unit root tests performances were adjudged with other existing tests. With significant break dates, between 2 and 5, the presence or otherwise of a unit root in foreign exchange rate series would be better captured when the inherent heteroscedasticity, trend and structural breaks in foreign exchange rate series are put into consideration

Suggested Citation

  • Yaya, OlaOluwa S & Akinlana, Damola M & Ogbonna, Ahamuefula E, 2017. "Investigating Structural break-GARCH-based Unit root test in US exchange rates," MPRA Paper 88768, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:88768
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    References listed on IDEAS

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    2. Salisu, Afees A. & Ogbonna, Ahamuefula E. & Adewuyi, Adeolu, 2020. "Google trends and the predictability of precious metals," Resources Policy, Elsevier, vol. 65(C).

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    More about this item

    Keywords

    Exchange rate; Heteroscedasticity; Unit root; Structural break;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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