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Efficiency in BRICS Currency Markets using Long-Spans of Data: Evidence from Model-Free Tests of Directional Predictability

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  • Rangan Gupta

    (Department of Economics, University of Pretoria, Pretoria, South Africa)

  • Vasilios Plakandaras

    (Department of Economics, Democritus University of Thrace, Greece)

Abstract

In this paper, we analyze the directional predictability in foreign exchange markets of Brazil, Russia, India, China and South Africa (i.e., the BRICS) using the quantilogram, which in turn, is a model-free econometric procedure involving a simple diagnostic statistic based on a sample correlation. Our analysis uses the longest possible available monthly data set covering the periods of 1812M01-2018M05, 1814M01-2018M05, 1822M07-2018M05, 1948M08-2018M05, and 1844M01-2018M05, respectively for the dollar-based exchange rates of the BRICS countries. We find that, barring the extreme phases of the currency markets, and around the median for India and South Africa, we do observe directional predictability, i.e., the efficient market hypothesis (EMH) is only accepted at these quantiles. The fact that predictability holds at certain parts of the unconditional distribution of exchange rate returns, capturing stages of the currency market, tend to support the so-called Adaptive Market Hypothesis (AMH).

Suggested Citation

  • Rangan Gupta & Vasilios Plakandaras, 2018. "Efficiency in BRICS Currency Markets using Long-Spans of Data: Evidence from Model-Free Tests of Directional Predictability," Working Papers 201836, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:201836
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    References listed on IDEAS

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    1. Mehmet Balcilar & Rangan Gupta & Clement Kyei & Mark E. Wohar, 2016. "Does Economic Policy Uncertainty Predict Exchange Rate Returns and Volatility? Evidence from a Nonparametric Causality-in-Quantiles Test," Open Economies Review, Springer, vol. 27(2), pages 229-250, April.
    2. Vasilios Plakandaras & Theophilos Papadimitriou & Periklis Gogas, 2015. "Forecasting Daily and Monthly Exchange Rates with Machine Learning Techniques," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(7), pages 560-573, November.
    3. Charles, Amélie & Darné, Olivier & Kim, Jae H., 2012. "Exchange-rate return predictability and the adaptive markets hypothesis: Evidence from major foreign exchange rates," Journal of International Money and Finance, Elsevier, vol. 31(6), pages 1607-1626.
    4. Plakandaras, Vasilios & Papadimitriou, Theophilos & Gogas, Periklis, 2012. "Directional forecasting in financial time series using support vector machines: The USD/Euro exchange rate," DUTH Research Papers in Economics 5-2012, Democritus University of Thrace, Department of Economics.
    5. Anoop S. KUMAR & Bandi KAMAIAH, 2016. "Efficiency, non-linearity and chaos: evidences from BRICS foreign exchange markets," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 0(1(606), S), pages 103-118, Spring.
    6. Aye, Goodness C. & Gil-Alana, Luis A. & Gupta, Rangan & Wohar, Mark E., 2017. "The efficiency of the art market: Evidence from variance ratio tests, linear and nonlinear fractional integration approaches," International Review of Economics & Finance, Elsevier, vol. 51(C), pages 283-294.
    7. Sarno,Lucio & Taylor,Mark P., 2003. "The Economics of Exchange Rates," Cambridge Books, Cambridge University Press, number 9780521485845.
    8. Theophilos Papadimitriou & Periklis Gogas & Vasilios Plakandaras, 2016. "Testing Exchange Rate Models in a Small Open Economy: an SVR Approach," Bulletin of Applied Economics, Risk Market Journals, vol. 3(2), pages 9-29.
    9. Vasilios Plakandaras & Rangan Gupta & Luis A. Gil-Alana & Mark E. Wohar, 2019. "Are BRICS exchange rates chaotic?," Applied Economics Letters, Taylor & Francis Journals, vol. 26(13), pages 1104-1110, July.
    10. Nikolaos Antonakakis & Mehmet Balcilar & Rangan Gupta & Clement Kyei, 2016. "Components of Economic Policy Uncertainty and Predictability of US Stock Returns and Volatility: Evidence from a Nonparametric Causality-in-Quantile Approach," Working Papers 201639, University of Pretoria, Department of Economics.
    11. Papadimitriou, Theophilos & Gogas, Periklis & Plakandaras, Vasilios, 2013. "Forecasting the NOK/USD Exchange Rate with Machine Learning Techniques," DUTH Research Papers in Economics 5-2013, Democritus University of Thrace, Department of Economics.
    12. Charfeddine, Lanouar & Khediri, Karim Ben & Aye, Goodness C. & Gupta, Rangan, 2018. "Time-varying efficiency of developed and emerging bond markets: Evidence from long-spans of historical data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 632-647.
    13. Christina Christou & Rangan Gupta & Christis Hassapis & Tahir Suleman, 2018. "The role of economic uncertainty in forecasting exchange rate returns and realized volatility: Evidence from quantile predictive regressions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(7), pages 705-719, November.
    14. Plakandaras, Vasilios & Papadimitriou, Theophilos & Gogas, Periklis & Diamantaras, Konstantinos, 2015. "Market sentiment and exchange rate directional forecasting," Algorithmic Finance, IOS Press, vol. 4(1-2), pages 69-79.
    15. Jaehun Chung & Yongmiao Hong, 2007. "Model-free evaluation of directional predictability in foreign exchange markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(5), pages 855-889.
    16. Ali Almail & Fahad Almudhaf, 2017. "Adaptive Market Hypothesis: Evidence from three centuries of UK data," Economics and Business Letters, Oviedo University Press, vol. 6(2), pages 48-53.
    17. Meese, Richard A. & Rogoff, Kenneth, 1983. "Empirical exchange rate models of the seventies : Do they fit out of sample?," Journal of International Economics, Elsevier, vol. 14(1-2), pages 3-24, February.
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    2. Lin Liu, 2022. "Economic Uncertainty and Exchange Market Pressure: Evidence From China," SAGE Open, , vol. 12(1), pages 21582440211, January.

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    More about this item

    Keywords

    Correlogram; dependence; quantiles; efficiency; currency markets; BRICS;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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