IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v641y2024ics0378437124002292.html
   My bibliography  Save this article

Studying the impact of fluctuations, spikes and rare events in time series through a wavelet entropy predictability measure

Author

Listed:
  • Mastroeni, Loretta
  • Mazzoccoli, Alessandro
  • Vellucci, Pierluigi

Abstract

Data has become one of the most crucial sources of human life. In particular, the ability to predict the future through data is a widely studied topic. In finance, as an instance, increased volatility, fluctuations, low-frequency events, and rare events negatively affect the predictability of data, thus increasing the level of risk. As a consequence, the inability to make accurate predictions on future events increases the uncertainty and variability of a given scenario, indicating a consequent increase in risk. In this paper, we analyze data predictability introducing a new measure based on entropy and the wavelet transform. In particular, we show that the data are less predictable than one might expect due to the mentioned fluctuations and low-frequency events. Furthermore, we apply our tool to real data, in particular to time series of commodities. As a result, thanks to this new measure, we can observe that the price time series under analysis exhibit a significant level of unpredictability due to increased volatility, fluctuations, and the influence of low-frequency events.

Suggested Citation

  • Mastroeni, Loretta & Mazzoccoli, Alessandro & Vellucci, Pierluigi, 2024. "Studying the impact of fluctuations, spikes and rare events in time series through a wavelet entropy predictability measure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 641(C).
    • Handle: RePEc:eee:phsmap:v:641:y:2024:i:c:s0378437124002292
    DOI: 10.1016/j.physa.2024.129720
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437124002292
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2024.129720?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sean J. Taylor & Benjamin Letham, 2018. "Forecasting at Scale," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 37-45, January.
    2. Alessandro Mazzoccoli & Maurizio Naldi, 2020. "Robustness of Optimal Investment Decisions in Mixed Insurance/Investment Cyber Risk Management," Risk Analysis, John Wiley & Sons, vol. 40(3), pages 550-564, March.
    3. Mastroeni, Loretta & Mazzoccoli, Alessandro & Quaresima, Greta & Vellucci, Pierluigi, 2022. "Wavelet analysis and energy-based measures for oil-food price relationship as a footprint of financialisation effect," Resources Policy, Elsevier, vol. 77(C).
    4. Martin L. Weitzman, 2009. "On Modeling and Interpreting the Economics of Catastrophic Climate Change," The Review of Economics and Statistics, MIT Press, vol. 91(1), pages 1-19, February.
    5. Benedetto, F. & Giunta, G. & Mastroeni, L., 2016. "On the predictability of energy commodity markets by an entropy-based computational method," Energy Economics, Elsevier, vol. 54(C), pages 302-312.
    6. Dimpfl, Thomas & Peter, Franziska J., 2018. "Analyzing volatility transmission using group transfer entropy," Energy Economics, Elsevier, vol. 75(C), pages 368-376.
    7. Robert Carbone & Spyros Makridakis, 1986. "Forecasting When Pattern Changes Occur Beyond the Historical Data," Management Science, INFORMS, vol. 32(3), pages 257-271, March.
    8. Blundell, Richard & Griffith, Rachel & Van Reenen, John, 1995. "Dynamic Count Data Models of Technological Innovation," Economic Journal, Royal Economic Society, vol. 105(429), pages 333-344, March.
    9. Galateia Terti & Isabelle Ruin & Jonathan J. Gourley & Pierre Kirstetter & Zachary Flamig & Juliette Blanchet & Ami Arthur & Sandrine Anquetin, 2019. "Toward Probabilistic Prediction of Flash Flood Human Impacts," Risk Analysis, John Wiley & Sons, vol. 39(1), pages 140-161, January.
    10. Zunino, L. & Pérez, D.G. & Garavaglia, M. & Rosso, O.A., 2007. "Wavelet entropy of stochastic processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 379(2), pages 503-512.
    11. Dirk Helbing, 2013. "Globally networked risks and how to respond," Nature, Nature, vol. 497(7447), pages 51-59, May.
    12. Mastroeni, Loretta & Mazzoccoli, Alessandro & Quaresima, Greta & Vellucci, Pierluigi, 2021. "Decoupling and recoupling in the crude oil price benchmarks: An investigation of similarity patterns," Energy Economics, Elsevier, vol. 94(C).
    13. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
    14. Pérez, D.G. & Zunino, L. & Garavaglia, M. & Rosso, O.A., 2006. "Wavelet entropy and fractional Brownian motion time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 365(2), pages 282-288.
    15. Loretta Mastroeni & Alessandro Mazzoccoli & Greta Quaresima & Pierluigi Vellucci, 2021. "Wavelet analysis and energy-based measures for oil-food price relationship as a footprint of financialisation effect," Papers 2104.11891, arXiv.org, revised Mar 2022.
    16. Mastroeni, Loretta & Vellucci, Pierluigi & Naldi, Maurizio, 2018. "Co-existence of stochastic and chaotic behaviour in the copper price time series," Resources Policy, Elsevier, vol. 58(C), pages 295-302.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mastroeni, Loretta & Mazzoccoli, Alessandro & Vellucci, Pierluigi, 2024. "Wavelet entropy and complexity–entropy curves approach for energy commodity price predictability amid the transition to alternative energy sources," Chaos, Solitons & Fractals, Elsevier, vol. 184(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mastroeni, Loretta & Mazzoccoli, Alessandro & Vellucci, Pierluigi, 2024. "Wavelet entropy and complexity–entropy curves approach for energy commodity price predictability amid the transition to alternative energy sources," Chaos, Solitons & Fractals, Elsevier, vol. 184(C).
    2. Loretta Mastroeni & Pierluigi Vellucci, 2022. "Construction of an SDE Model from Intraday Copper Futures Prices," Risks, MDPI, vol. 10(11), pages 1-21, November.
    3. Benedetto, Francesco & Mastroeni, Loretta & Quaresima, Greta & Vellucci, Pierluigi, 2020. "Does OVX affect WTI and Brent oil spot variance? Evidence from an entropy analysis," Energy Economics, Elsevier, vol. 89(C).
    4. Philippe Aghion & Antoine Dechezleprêtre & David Hémous & Ralf Martin & John Van Reenen, 2016. "Carbon Taxes, Path Dependency, and Directed Technical Change: Evidence from the Auto Industry," Journal of Political Economy, University of Chicago Press, vol. 124(1), pages 1-51.
    5. Loretta Mastroeni & Alessandro Mazzoccoli & Greta Quaresima & Pierluigi Vellucci, 2021. "Wavelet analysis and energy-based measures for oil-food price relationship as a footprint of financialisation effect," Papers 2104.11891, arXiv.org, revised Mar 2022.
    6. Sprangers, Olivier & Schelter, Sebastian & de Rijke, Maarten, 2023. "Parameter-efficient deep probabilistic forecasting," International Journal of Forecasting, Elsevier, vol. 39(1), pages 332-345.
    7. Aktham Maghyereh & Hussein Abdoh, 2022. "Global financial crisis versus COVID‐19: Evidence from sentiment analysis," International Finance, Wiley Blackwell, vol. 25(2), pages 218-248, August.
    8. Maghyereh, Aktham & Abdoh, Hussein & Awartani, Basel, 2022. "Have returns and volatilities for financial assets responded to implied volatility during the COVID-19 pandemic?," Journal of Commodity Markets, Elsevier, vol. 26(C).
    9. Mastroeni, Loretta & Mazzoccoli, Alessandro & Quaresima, Greta & Vellucci, Pierluigi, 2022. "Wavelet analysis and energy-based measures for oil-food price relationship as a footprint of financialisation effect," Resources Policy, Elsevier, vol. 77(C).
    10. Naqvi, Asjad & Monasterolo, Irene, 2019. "Natural Disasters, Cascading Losses, and Economic Complexity: A Multi-layer Behavioral Network Approach," Ecological Economic Papers 24, WU Vienna University of Economics and Business.
    11. Zhou, Yang & Xie, Chi & Wang, Gang-Jin & Gong, Jue & Li, Zhao-Chen & Zhu, You, 2024. "Who dominate the information flowing between innovative and traditional financial assets? A multiscale entropy-based approach," International Review of Economics & Finance, Elsevier, vol. 93(PB), pages 329-358.
    12. van den Bergh, J.C.J.M. & Botzen, W.J.W., 2015. "Monetary valuation of the social cost of CO2 emissions: A critical survey," Ecological Economics, Elsevier, vol. 114(C), pages 33-46.
    13. Richard S. J. Tol & In Chang Hwang & Frédéric Reynès, 2012. "The Effect of Learning on Climate Policy under Fat-tailed Uncertainty," Working Paper Series 5312, Department of Economics, University of Sussex Business School.
    14. Cornelia Lawson, 2013. "Academic Inventions Outside the University: Investigating Patent Ownership in the UK," Industry and Innovation, Taylor & Francis Journals, vol. 20(5), pages 385-398, July.
    15. Symeonidis, George, 2001. "Price Competition, Innovation and Profitability: Theory and UK Evidence," CEPR Discussion Papers 2816, C.E.P.R. Discussion Papers.
    16. Hille, Erik & Althammer, Wilhelm & Diederich, Henning, 2020. "Environmental regulation and innovation in renewable energy technologies: Does the policy instrument matter?," Technological Forecasting and Social Change, Elsevier, vol. 153(C).
    17. Simon Levin & Anastasios Xepapadeas, 2021. "On the Coevolution of Economic and Ecological Systems," Annual Review of Resource Economics, Annual Reviews, vol. 13(1), pages 355-377, October.
    18. Hahn Robert, 2010. "Designing Smarter Regulation with Improved Benefit-Cost Analysis," Journal of Benefit-Cost Analysis, De Gruyter, vol. 1(1), pages 1-19, July.
    19. Balint, T. & Lamperti, F. & Mandel, A. & Napoletano, M. & Roventini, A. & Sapio, A., 2017. "Complexity and the Economics of Climate Change: A Survey and a Look Forward," Ecological Economics, Elsevier, vol. 138(C), pages 252-265.
    20. Wei Dai & Yuan An & Wen Long, 2021. "Price change prediction of ultra high frequency financial data based on temporal convolutional network," Papers 2107.00261, arXiv.org.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:641:y:2024:i:c:s0378437124002292. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.