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Nonlinear dynamics in crude oil benchmarks: an AMH perspective

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  • George Varghese
  • Vinodh Madhavan

Abstract

We investigate the nonlinear dynamics in global crude oil benchmarks at multiple frequencies from an Adaptive Market Hypothesis perspective. In doing so, we examine the role of time aggregation in the examination of nonlinearity. We find the evidence of neglected nonlinearity to be elusive at higher levels of time aggregation once the returns series is explicitly modeled for conditional heteroscedasticity. While bouts of transient inefficiencies are found to be more pronounced at lower levels of time aggregation, they are interspersed with protracted periods of efficiency. Further, our findings pinpoint the adequacy of appropriate AR-GARCH models in capturing most (if not all) of neglected nonlinearity in crude oil prices. Lastly, our study indicates the critical role played by AMH in elevating the level of scholarly discourse on the efficiency of crude oil benchmarks.

Suggested Citation

  • George Varghese & Vinodh Madhavan, 2019. "Nonlinear dynamics in crude oil benchmarks: an AMH perspective," Applied Economics Letters, Taylor & Francis Journals, vol. 26(21), pages 1798-1801, December.
  • Handle: RePEc:taf:apeclt:v:26:y:2019:i:21:p:1798-1801
    DOI: 10.1080/13504851.2019.1602700
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    Cited by:

    1. Du, Xiaoxu & Tang, Zhenpeng & Chen, Kaijie, 2023. "A novel crude oil futures trading strategy based on volume-price time-frequency decomposition with ensemble deep reinforcement learning," Energy, Elsevier, vol. 285(C).

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