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The Relationship between Commodity Markets and Commodity Mutual Funds: A Wavelet-Based Analysis

Author

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  • Nikolaos Antonakakis

    (University of Portsmouth, Economics and Finance Group, United Kingdom ; Webster Vienna Private University, Department of Business and Management, Austria)

  • Tsangyao Chang

    (Department of Finance, College of Finance, Feng Chia University, Taiwan)

  • Juncal Cunado

    (University of Navarra, School of Economics, Spain)

  • Rangan Gupta

    (Department of Economics, University of Pretoria)

Abstract

This paper analyses the causal relationship between commodities funds and returns using monthly data for the period May 1997 to August 2015. Linear Granger causality tests fail to detect any evidence of causal relationships. However, our data reveals strong evidence of nonlinearity and structural breaks, making the results from the linear model unreliable. Given this, we use wavelets to analyse the causality between the two variables at both time and frequency domains. Wavelet coherency reveals that these two variables are primarily positively related in the short-run and over the period of 2008 to 2015. When we investigate the phase differences over this period, we observe that flows have predicted returns over the period of 2008 to 2012, with causality running in the other direction thereafter. Our results highlight the importance of using a time-varying approach across the frequency domain to draw correct inferences between commodity returns and flows, especially in the presence of nonlinearities and structural breaks, which results in a misspecified linear model of Granger causality.

Suggested Citation

  • Nikolaos Antonakakis & Tsangyao Chang & Juncal Cunado & Rangan Gupta, 2016. "The Relationship between Commodity Markets and Commodity Mutual Funds: A Wavelet-Based Analysis," Working Papers 201619, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:201619
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    2. Tule, Moses K. & Salisu, Afees A. & Chiemeke, Charles C., 2019. "Can agricultural commodity prices predict Nigeria's inflation?," Journal of Commodity Markets, Elsevier, vol. 16(C).
    3. Bernardina Algieri & Arturo Leccadito, 2020. "CARL and His POT: Measuring Risks in Commodity Markets," Risks, MDPI, vol. 8(1), pages 1-15, March.
    4. Lei Wang & Provash Kumer Sarker & Elie Bouri, 2023. "Short- and Long-Term Interactions Between Bitcoin and Economic Variables: Evidence from the US," Computational Economics, Springer;Society for Computational Economics, vol. 61(4), pages 1305-1330, April.
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    6. Elroi Hadad & Davinder Malhotra & Srinivas Nippani, 2024. "Trading commodity ETFs: Price behavior, investment insights, and performance analysis," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(7), pages 1257-1276, July.

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    More about this item

    Keywords

    Commodity returns and flows; Granger causality; Nonlinearity; Time and frequency domains; Wavelet;
    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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market

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