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Time-Varying Efficiency of Developed and Emerging Bond Markets: Evidence from Long-Spans of Historical Data

Author

Listed:
  • Lanouar Charfeddine

    (College of Business and Economics, Qatar University)

  • Karim Ben Khediri

    (CEROS, Université Paris Nanterre, France and FSEG Nabeul, University of Carthage, Tunisia)

  • Goodness C. Aye

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

  • Rangan Gupta

    (University of Pretoria, Pretoria, South Africa)

Abstract

Bonds have become an important part of investment portfolios for individuals as well as for institutions, particularly after the recent financial crisis. This paper empirically investigates the Adaptive Market Hypothesis (AMH) in two of the most established bond markets in the world: the US and UK and two emerging markets: South Africa and India, using monthly data series spanning very long time periods. We examine the long memory properties of the series using GPH, ELW and FELW and multiple structural breaks technique to examine possibility of structural breaks. We then examine the weak-form efficiency of government bond markets, using a time varying approaches namely the state-space generalized autoregressive conditional heteroscedasticity in mean (GARCH-M) to date the time varying behavior of bond market efficiency. Results show that efficiency of these markets has been changing over time, depending on the prevailing economic, political and market conditions. Further, we observe that the degree of the weak-form efficiency of these markets has been gradually improving recently. In particular, the US government bond market has been highly efficient, showing the highest degree of market efficiency among the four bond markets. Overall, our results suggest that the AMH provides a better description of the behavior of government bond returns than the Efficient Market Hypothesis (EMH).

Suggested Citation

  • Lanouar Charfeddine & Karim Ben Khediri & Goodness C. Aye & Rangan Gupta, 2017. "Time-Varying Efficiency of Developed and Emerging Bond Markets: Evidence from Long-Spans of Historical Data," Working Papers 201771, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:201771
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    3. Aviral Kumar Tiwari & Rangan Gupta & Juncal Cunado & Xin Sheng, 2020. "Testing the white noise hypothesis in high-frequency housing returns of the United States," Economics and Business Letters, Oviedo University Press, vol. 9(3), pages 178-188.
    4. Adefemi A. OBALADE & Akona TSHUTSHA & Lungelo MVUYANA & Nothando NDLOVU & Paul-Francois MUZINDUTSI, 2022. "Are Frontier African Markets Inefficient or Adaptive? Application of Rolling GARCH Models," Journal of Economics and Financial Analysis, Tripal Publishing House, vol. 6(1), pages 19-35.
    5. Sikhosana, Ayanda & Aye, Goodness C., 2018. "Asymmetric volatility transmission between the real exchange rate and stock returns in South Africa," Economic Analysis and Policy, Elsevier, vol. 60(C), pages 1-8.
    6. Subhamitra Patra & Gourishankar S. Hiremath, 2022. "An Entropy Approach to Measure the Dynamic Stock Market Efficiency," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(2), pages 337-377, June.
    7. Rangan Gupta & Vasilios Plakandaras, 2019. "Efficiency in BRICS Currency Markets Using Long-Spans of Data: Evidence from Model-Free Tests of Directional Predictability," Journal of Economics and Behavioral Studies, AMH International, vol. 11(1), pages 152-165.
    8. Ben Moews & Gbenga Ibikunle, 2020. "Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning," Papers 2002.10385, arXiv.org.
    9. Moews, Ben & Ibikunle, Gbenga, 2020. "Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    10. Charfeddine, Lanouar & Khediri, Karim Ben & Mrabet, Zouhair, 2019. "The forward premium anomaly in the energy futures markets: A time-varying approach," Research in International Business and Finance, Elsevier, vol. 47(C), pages 600-615.
    11. Adefemi A. Obalade & Paul-Francois Muzindutsi, 2021. "Are African Stock Markets Inefficient or Adaptive? Empirical Literature," Chapters, in: Vito Bobek & Chee-Heong Quah (ed.), Emerging Markets, IntechOpen.

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    More about this item

    Keywords

    Adaptive market hypothesis; Bond Market; GARCH-M; Long memory; Market Efficiency; State-space Model; Time-varying;
    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
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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