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The Price and Trading Volume Dynamics Relationship in the EEX Power Market: A Wavelet Modeling

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  • Foued Saâdaoui

Abstract

This paper examines the dynamic relationship between power spot prices and related trading volumes in one of the most emergent energy markets. Traditionally, investigating the bivariate stochastic processes has been dominated by linear econometrical methods that proved helpful especially in finance. However, when dealing with intradaily power data, we cannot rely on models developed for financial or other commodity markets. Therefore, wavelet transforms are applied for power markets data to search for and decode nonlinear regularities and hidden patterns existing between the variables. Given its ability to decompose the time series into their time scale components and thus to reveal structure at different time horizons, wavelets are useful in analyzing situations in which the degree of association between processes is likely to change with the time-horizon. Therefore, a wavelet-based cross-analysis is performed between prices and trading volume time series. On the same basis, causality tests and out-of-sample forecasting tasks are carried out to empirically the strong relationship between the two investigated time series. Copyright Springer Science+Business Media New York 2013

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  • Foued Saâdaoui, 2013. "The Price and Trading Volume Dynamics Relationship in the EEX Power Market: A Wavelet Modeling," Computational Economics, Springer;Society for Computational Economics, vol. 42(1), pages 47-69, June.
    • Handle: RePEc:kap:compec:v:42:y:2013:i:1:p:47-69
    DOI: 10.1007/s10614-012-9346-7
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    Cited by:

    1. Yi-Ting Chen & Wan-Ni Lai & Edward W. Sun, 2019. "Jump Detection and Noise Separation by a Singular Wavelet Method for Predictive Analytics of High-Frequency Data," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 809-844, August.
    2. Saâdaoui, Foued & Naifar, Nader & Aldohaiman, Mohamed S., 2017. "Predictability and co-movement relationships between conventional and Islamic stock market indexes: A multiscale exploration using wavelets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 552-568.
    3. Papadimitriou, Theophilos & Gogas, Periklis & Stathakis, Efthimios, 2014. "Forecasting energy markets using support vector machines," Energy Economics, Elsevier, vol. 44(C), pages 135-142.
    4. Saâdaoui, Foued & Ben Jabeur, Sami & Goodell, John W., 2023. "Geopolitical risk and the Saudi stock market: Evidence from a new wavelet packet multiresolution cross-causality," Finance Research Letters, Elsevier, vol. 53(C).
    5. Saâdaoui, Foued & Ben Jabeur, Sami, 2023. "Analyzing the influence of geopolitical risks on European power prices using a multiresolution causal neural network," Energy Economics, Elsevier, vol. 124(C).

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