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Time-varying long range dependence in market returns of FEAS members

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  • Sensoy, A.

Abstract

We study the time-varying efficiency of nineteen members of the Federation of Euro-Asian Stock Exchanges (FEAS – an international organization comprising the main stock exchanges in Eastern Europe, the Middle East and Central Asia) by generalized Hurst exponent analysis of daily data with a rolling window technique. The study covers the six years of time period between January 2007 and December 2012. The results reveal that all FEAS members exhibit different degrees of long range dependence varying over time. We present an efficiency ranking of these members that provides guidance for investors and portfolio managers. Results show that the least inefficient market is Turkey followed by Romania while the most inefficient markets are Iran, Mongolia, Serbia and Macedonia. Throughout the considered time period, Turkey’s stable Hurst exponent around 0.5 differs from others and shows characteristics of a developed financial market. For the federation members, strong positive relationship between efficiency and market liquidity is revealed. In the light of this fact, alternatives are suggested to improve market efficiency.

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  • Sensoy, A., 2013. "Time-varying long range dependence in market returns of FEAS members," Chaos, Solitons & Fractals, Elsevier, vol. 53(C), pages 39-45.
    • Handle: RePEc:eee:chsofr:v:53:y:2013:i:c:p:39-45
    DOI: 10.1016/j.chaos.2013.05.004
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    2. Tamara Backović & Vesna Karadžić & Sergej Gričar & Štefan Bojnec, 2023. "Montenegrin Stock Exchange Market on a Short-Term Perspective," JRFM, MDPI, vol. 16(7), pages 1-18, June.
    3. Batten, Jonathan A. & Kinateder, Harald & Wagner, Niklas, 2014. "Multifractality and value-at-risk forecasting of exchange rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 71-81.
    4. Sukpitak, Jessada & Hengpunya, Varagorn, 2016. "The influence of trading volume on market efficiency: The DCCA approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 259-265.
    5. Sensoy, Ahmet & Tabak, Benjamin M., 2015. "Time-varying long term memory in the European Union stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 147-158.
    6. Sensoy, A., 2013. "Effects of monetary policy on the long memory in interest rates: Evidence from an emerging market," Chaos, Solitons & Fractals, Elsevier, vol. 57(C), pages 85-88.
    7. Akash P. POOJARI & Siva Kiran GUPTHA & G Raghavender RAJU, 2022. "Multifractal analysis of equities. Evidence from the emerging and frontier banking sectors," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(3(632), A), pages 61-80, Autumn.
    8. Lee, Hojin & Chang, Woojin, 2015. "Multifractal regime detecting method for financial time series," Chaos, Solitons & Fractals, Elsevier, vol. 70(C), pages 117-129.
    9. Hasan, Rashid & Mohammed Salim, M., 2017. "Power law cross-correlations between price change and volume change of Indian stocks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 620-631.
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    11. Pavel Srbek, 2018. "Odhad Hurstova exponentu v časových řadách denních výnosů akciových indexů [Estimation of the Hurst Exponent in Time Series of Daily Returns of Stock Indices]," Politická ekonomie, Prague University of Economics and Business, vol. 2018(4), pages 508-524.
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    13. A. Sensoy & Benjamin M. Tabak, 2013. "How much random does European Union walk? A time-varying long memory analysis," Working Papers Series 342, Central Bank of Brazil, Research Department.

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