Abstract
We analyze the directional predictability in foreign exchange markets of Brazil, Russia, India, China and South Africa (BRICS) using the quantilogram, based on long-spans of monthly historical data, at times covering over a century. We find that the efficient market hypothesis (EMH) holds at the extreme phases of the currency markets (and around the median for India and South Africa). Since predictability holds at certain parts of the unconditional distribution of exchange rate returns, we find support for the Adaptive Market Hypothesis (AMH). AMH, based on the idea of bounded rationality, suggests that currency return predictability will be intermittent, due to changing market conditions and institutional factors.
Keywords
- Correlogram Search this journal for Correlogram (opens in a new tab)
- Dependence Search this journal for Dependence (opens in a new tab)
- Quantiles Search this journal for Quantiles (opens in a new tab)
- Efficiency Search this journal for Efficiency (opens in a new tab)
- Currency Markets Search this journal for Currency Markets (opens in a new tab)
- BRICS Search this journal for BRICS (opens in a new tab)
References
- Almail, A. & Almudhaf, F. (2017). Adaptive Market Hypothesis: Evidence from three centuries of UK data. Economics and Business Letters, 6(2), 48-53.
- Aye, G. C., Gil-Alana, L. A., Gupta, R. & Wohar, M. E. (2017a). The Efficiency of the Art Market: Evidence from Variance Ratio Tests, Linear and Nonlinear Fractional Integration Approaches. International Review of Economics & Finance, 51, 283-294.
- Aye, G. C., Chang, T., Chen, W. Y., Gupta, R. & Wohar, M. E. (2017b). Testing the Efficiency of the Art Market Using Quantile‐Based Unit Root Tests with Sharp and Smooth Breaks. The Manchester School.
- Balcilar, M., Gupta, R., Kyei, C. & Wohar, M. E. (2016). Does Economic Policy Uncertainty Predict Exchange Rate Returns and Volatility? Evidence from a Nonparametric Causality-in-Quantiles Test. Open Economies Review, 27(2), 229-250.
- Bhattacharya, S. N., Bhattacharya, M. & Roychoudhury, B. (2018). Behavior of the Foreign Exchange Rates of BRICS: Is it Chaotic? The Journal of Prediction Markets, 11(2), 1-19.
- Cakan, E. & Gupta, R. (2017). Does U.S. Macroeconomic News Make the South African Stock Market Riskier? Journal of Developing Areas, 51(4), 15-27.
- Charfeddine, L., Ben Khediri, K., Aye, G. C. & Gupta, R. (2018). Time-Varying Efficiency of Developed and Emerging Bond Markets: Evidence from Long-Spans of Historical Data. Physical A: Statistical Mechanics and its Applications, 505, 632-647.
- Charles, A., Darné, O. & Kim, J. H. (2012). Exchange-rate return predictability and the adaptive markets hypothesis: Evidence from major foreign exchange rates. Journal of International Money and Finance, 31(6), 1607-1626.
- Christou, C., Gupta, R., Hassapis, C. & Suleman, T. (2018). The Role of Economic Uncertainty in Forecasting Exchange Rate Returns and Realized Volatility: Evidence from Quantile Predictive Regressions. Journal of Forecasting, 37(7), 705-719.
- Chung, J. & Hong, Y. (2007). Model-free evaluation of directional predictability in foreign exchange markets. Journal of Applied Econometrics, 22, 855–889.
- Fama, E. (1965). The behavior of stock market prices. Journal of Business, 38, 34–105.
- Government of India. (2012). The BRICS Report (New Delhi 2012). Oxford University Press, Oxford.
- Hong, Y. & Chung J. (2006). Are the directions of stock price changes predictable? A generalized cross-spectral approach. Working paper, Department of Economics, Cornell University.
- Hurst, H. E. (1951). Long-term storage capacity of reservoirs. Transactions of the American Society of Civil Engineers, 116, 770–799.
- Kumar, A. S. & Kamaiah, B. (2016). Efficiency, non-linearity and chaos: evidence from BRICS foreign exchange markets. Theoretical and Applied Economics, 23(1), 103-118.
- Lo, A. W. (2004). The adaptive markets hypothesis: Market efficiency from an evolutionary perspective. Journal of Portfolio Management, 30, 15–29.
- Lo, A. W. (2005). Reconciling efficient markets with behavioral finance: The adaptive markets hypothesis. Journal of Investment Consulting, 7, 21–44.
- Linton, O. & Whang, Y. J. (2007). A Quantilogram Approach to Evaluating Directional Predictability. Journal of Econometrics, 141, 250-282.
- Meese, R. A. & Rogoff, K. (1983). Empirical exchange rate models of the seventies: do they fit out of sample? Journal of International Economics, 14, 3–24.
- Papadimitriou, T., Gogas, P. & Plakandaras, V. (2016). Testing Exchange Rate models in a Small Open Economy: an SVR approach. Bulletin of Applied Economics, 3(2), 6-29.
- Peng, C. K., Buldyrev, S. V. & Havlin, S. (1994). Mosaic organization of DNA nucleotides, Physical Review E, 49, 1685–9.
- Plakandaras, V., Gupta, R., Gil-Alana, L. A. & Wohar, M. E. (Forthcoming). Are BRICS Exchange Rates Chaotic? Applied Economics Letters.
- Plakandaras, V., Papadimitriou, T. & Gogas, P. (2013). Directional forecasting in financial time series using support vector machines: the USD/ Euro exchange rate. Journal of Computational Optimization in Economics and Finance, 5(2) 125-138.
- Plakandaras, V., Papadimitriou, T. & Gogas, P. (2015a). Forecasting monthly and daily exchange rates with machine learning methodologies. Journal of Forecasting, 34(7), 560-573.
- Plakandaras, V., Papadimitriou, T., Gogas, P. & Konstantinos, D. (2015b). Market Sentiment and Exchange Rate Directional Forecasting. Algorithmic Finance, 4(1-2), 69-79.
- Ruzima, M. & Boachie, M. K. (2017). Exchange rate uncertainty and private investment in BRICS economies. Asia-Pacific Journal of Regional Science.
- Samuelson, P. A. (1965). Proof that properly anticipated prices fluctuate randomly. Industrial Management Review, 6, 41–9.
- Taylor, M. P. (1995). The economics of exchange rates. Journal of Economic Literature, 33, 13–47.
- Tiwari, A. K., Aye, G. C. & Gupta, R. (Forthcoming). Stock Market Efficiency Analysis using Long Spans of Data: A Multifractal Detrended Fluctuation Approach. Finance Research Letters.
- Wilson, D. & Purushothaman, R. (2003). Dreaming With BRICs: The Path to 2050. Goldman Sachs Global Economics, Paper No. 99.
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Copyright (c) 2019 Rangan Gupta, Vasilios Plakandaras (Author)

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Keywords
- Correlogram Search this journal for Correlogram (opens in a new tab)
- Dependence Search this journal for Dependence (opens in a new tab)
- Quantiles Search this journal for Quantiles (opens in a new tab)
- Efficiency Search this journal for Efficiency (opens in a new tab)
- Currency Markets Search this journal for Currency Markets (opens in a new tab)
- BRICS Search this journal for BRICS (opens in a new tab)
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