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New evidence from the random walk hypothesis for BRICS stock indices: a wavelet unit root test approach

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  • Tiwari, Aviral Kumar
  • Kyophilavong, Phouphet

Abstract

We examine the use of the random walk hypothesis on the BRICS stock indices. Our examination of the stock indices uses a recently developed wavelet-based unit root test by Fan and Gençay (2010) along with a battery of unit root tests. We also examine the sensitivity of the wavelet-based unit root test. Our wavelet-based unit root tests show evidence that rejects the null of the unit root for all of the BRICS countries except for the Russian Federation. Hence, the tests provide support for the predictability of stock market indices in these economies on the basis of historical information. However, there is a need for caution because the results are based on a relatively small sample of only 11years of monthly observations.

Suggested Citation

  • Tiwari, Aviral Kumar & Kyophilavong, Phouphet, 2014. "New evidence from the random walk hypothesis for BRICS stock indices: a wavelet unit root test approach," Economic Modelling, Elsevier, vol. 43(C), pages 38-41.
    • Handle: RePEc:eee:ecmode:v:43:y:2014:i:c:p:38-41
    DOI: 10.1016/j.econmod.2014.07.005
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    2. Gil-Alana, Luis A. & Infante, Juan & Martín-Valmayor, Miguel Angel, 2023. "Persistence and long run co-movements across stock market prices," The Quarterly Review of Economics and Finance, Elsevier, vol. 89(C), pages 347-357.
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    5. Mukta Kanvinde & Muneer Shaik, 2020. "Are BRICS Stock Market Indices Mean Reverting? Evidence Based on Expected Lifetime Range Ratio," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 19(2), pages 169-186, September.
    6. Mishra, Vinod & Smyth, Russell, 2016. "Are natural gas spot and futures prices predictable?," Economic Modelling, Elsevier, vol. 54(C), pages 178-186.
    7. Adedoyin Isola Lawal & Russel O Somoye & Abiola Ayopo Babajide, 2017. "Are African stock markets efficient? Evidence from wavelet unit root test for random walk," Economics Bulletin, AccessEcon, vol. 37(4), pages 2665-2679.
    8. Siva Kiran & Prabhakar Rao.R, 2019. "Analysis of Stock Market Efficiency in Emerging Markets: Evidence from BRICS," Romanian Economic Journal, Department of International Business and Economics from the Academy of Economic Studies Bucharest, vol. 22(72), pages 60-77, June.
    9. Jing Hao & Feng He & Feng Ma & Shibo Zhang & Xiaotao Zhang, 2025. "Machine learning vs deep learning in stock market investment: an international evidence," Annals of Operations Research, Springer, vol. 348(1), pages 93-115, May.
    10. Ali, Sajid & Shahzad, Syed Jawad Hussain & Raza, Naveed & Al-Yahyaee, Khamis Hamed, 2018. "Stock market efficiency: A comparative analysis of Islamic and conventional stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 139-153.
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    13. Fan He & Xuansen He, 2019. "A Continuous Differentiable Wavelet Shrinkage Function for Economic Data Denoising," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 729-761, August.
    14. Narayan, Paresh Kumar & Phan, Dinh Hoang Bach & Bannigidadmath, Deepa, 2017. "Is the profitability of Indian stocks compensation for risks?," Emerging Markets Review, Elsevier, vol. 31(C), pages 47-64.
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    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • 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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