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Testing Price Pressure, Information, Feedback Trading, and Smoothing Effects for Energy Exchange Traded Funds

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  • Chang, Chia-Lin
  • Ke, Yu-Pei

Abstract

This paper examines the relationships between flows and returns for five Exchange Traded Funds (ETF) in the U.S. energy sector. Four alternative hypotheses are tested, including the price pressure hypothesis, information (or price release) hypothesis, feedback trading hypothesis, and smoothing hypothesis. The five ETF are the Energy Select Sector SPDR Fund (XLE), iShares U.S. Energy ETF (IYE), iShares Global Energy ETF (IXC), Vanguard Energy ETF (VDE), and PowerShares Dynamic Energy Exploration & Production Portfolio (PXE). A Vector Autoregressive (VAR) model is used to analyze the relationships between energy flows and returns. The empirical results show that energy returns and subsequent energy ETF flows have a negative relationship, thereby supporting the smoothing hypothesis. Moreover, the smoothing effect exists for XLE and IYE during the global financial crisis. Regardless of whether the whole sample period or the sub-samples before, during and after the global financial crisis are used, no evidence is found in support of the price pressure hypothesis, information hypothesis, or feedback trading hypothesis.

Suggested Citation

  • Chang, Chia-Lin & Ke, Yu-Pei, 2014. "Testing Price Pressure, Information, Feedback Trading, and Smoothing Effects for Energy Exchange Traded Funds," MPRA Paper 57625, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:57625
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    References listed on IDEAS

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    Cited by:

    1. Chia-Lin Chang & Shing-Yang Hu & Shih-Ti Yu, 2014. "Recent Developments In Quantitative Finance: An Overview," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 9(02), pages 1-7.
    2. Jędrzej Białkowski & Huong Dieu Dang & Xiaopeng Wei, 2017. "Does the Tail Wag the Dog? Evidence from Fund Flow to VIX ETFs and ETNs," Working Papers in Economics 17/17, University of Canterbury, Department of Economics and Finance.
    3. Chia-Lin Chang & Michael McAleer & Chien-Hsun Wang, 2017. "An Econometric Analysis of ETF and ETF Futures in Financial and Energy Markets Using Generated Regressors," IJFS, MDPI, vol. 6(1), pages 1-24, December.
    4. Chang, Chia-Lin & McAleer, Michael & Wang, Yanghuiting, 2018. "Testing Co-Volatility spillovers for natural gas spot, futures and ETF spot using dynamic conditional covariances," Energy, Elsevier, vol. 151(C), pages 984-997.
    5. Caroline Michere Ndei & Stephen Muchina & Kennedy Waweru, 2019. "Equity Unit Trust Funds Flow and Stock Market Returns: Evidence from Kenya," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 8(1), pages 21-36, January.
    6. Caner Ozdurak & Veysel Ulusoy, 2020. "Price Discovery in Crude Oil Markets: Intraday Volatility Interactions between Crude Oil Futures and Energy Exchange Traded Funds," International Journal of Energy Economics and Policy, Econjournals, vol. 10(3), pages 402-413.
    7. Anton Lisin & Tomonobu Senjyu, 2021. "Renewable Energy Transition: Evidence from Spillover Effects in Exchange-Traded Funds," International Journal of Energy Economics and Policy, Econjournals, vol. 11(3), pages 184-190.

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    More about this item

    Keywords

    Energy Exchange Traded Funds (ETF); Price pressure hypothesis; Information hypothesis; Feedback trading hypothesis; Smoothing hypothesis.;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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