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Economic Policy Uncertainty, Energy and Sustainable Cryptocurrencies: Investigating Dynamic Connectedness during the COVID-19 Pandemic

Author

Listed:
  • Inzamam Ul Haq

    (Business School, Liaoning University, Shenyang 110036, China)

  • Paulo Ferreira

    (VALORIZA—Research Center for Endogenous Resource Valorization, 7300-555 Portalegre, Portugal
    Department of Economic Sciences and Organizations, Polytechnic Institute of Portalegre, 7300-555 Portalegre, Portugal
    Center for Advanced Studies in Management and Economics, Palácio do Vimioso, Largo Marquês de Marialva, 8, 7000-809 Évora, Portugal)

  • Derick David Quintino

    (Independent Researcher, Anchieta Street, 697, Nova Odessa, São Paulo 13380-009, Brazil)

  • Nhan Huynh

    (Department of Applied Finance, Macquarie Business School, Macquarie University, Sydney 2109, Australia)

  • Saowanee Samantreeporn

    (Faculty of Business Administration, South Asia University, 19/1 Perchkasem Road, Nong Khaem, Bangkok 10160, Thailand)

Abstract

The purpose of the research is to explore the dynamic multiscale linkage between economic policy uncertainty, equity market volatility, energy and sustainable cryptocurrencies during the COVID-19 period. We use a multiscale TVP-VAR model considering level (EPUs and IDEMV) and returns series (cryptocurrencies) from 1 December 2019 to 30 September 2022. The data are then decomposed into six wavelet components, based on the wavelet MODWT method. The TVP-VAR connectedness approach is used to uncover the dynamic connectedness among EPUs, energy and sustainable cryptocurrency returns. Our findings reveal that CNEPU (USEPU) is the strongest (weakest) NET volatility transmitter. IDEMV is the most consistent volatility NET transmitter among all uncertainty indices across the original returns and wavelet scales (D1~D6). Energy cryptocurrencies, i.e., GRID, POW and SNC, are more likely to receive volatility spillovers than sustainable cryptocurrencies during a turbulent period (COVID-19). XLM (XNO) is least (most) affected by volatility spillover in system-wide connectedness, and XLM (ADA and MIOTA) showed a consistent (heterogeneous) non-recipient behavior across the six wavelet (D1~D6) scales and original return series. This study uncovers the dynamic connectedness across multiscale, which will support investors considering different investment horizons (D1~D6).

Suggested Citation

  • Inzamam Ul Haq & Paulo Ferreira & Derick David Quintino & Nhan Huynh & Saowanee Samantreeporn, 2023. "Economic Policy Uncertainty, Energy and Sustainable Cryptocurrencies: Investigating Dynamic Connectedness during the COVID-19 Pandemic," Economies, MDPI, vol. 11(3), pages 1-23, February.
    • Handle: RePEc:gam:jecomi:v:11:y:2023:i:3:p:76-:d:1079985
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    References listed on IDEAS

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    1. Adekoya, Oluwasegun B. & Oliyide, Johnson A., 2021. "How COVID-19 drives connectedness among commodity and financial markets: Evidence from TVP-VAR and causality-in-quantiles techniques," Resources Policy, Elsevier, vol. 70(C).
    2. Pham, Linh & Karim, Sitara & Naeem, Muhammad Abubakr & Long, Cheng, 2022. "A tale of two tails among carbon prices, green and non-green cryptocurrencies," International Review of Financial Analysis, Elsevier, vol. 82(C).
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    Cited by:

    1. Alexey Mikhaylov & N. B. A. Yousif & Hasan Dincer & Serhat Yüksel & Zuleima Karpyn, 2025. "RETRACTED ARTICLE: Analysis of the knowledge and innovation-based customer expectations for the green crypto assets in investment strategies using artificial intelligence and facial expression-based fuzzy modelling," Quality & Quantity: International Journal of Methodology, Springer, vol. 59(3), pages 2861-2890, June.
    2. Ioannis Zervas & Emmanouil Stiakakis & Ioannis Athanasiadis & Georgios Tsekouropoulos, 2024. "A Holistic Approach to Define Important Digital Skills for the Digital Society," Societies, MDPI, vol. 14(7), pages 1-22, July.
    3. Wafa Masmoudi Kammoun, 2026. "Return and volatility spillover drivers among conventional cryptocurrencies," Digital Finance, Springer, vol. 8(1), pages 1-39, March.
    4. Taha Zaghdoudi & Kais Tissaoui & Mohamed Hédi Maâloul & Younès Bahou & Niazi Kammoun, 2024. "Can Economic, Geopolitical and Energy Uncertainty Indices Predict Bitcoin Energy Consumption? New Evidence from a Machine Learning Approach," Energies, MDPI, vol. 17(13), pages 1-15, July.
    5. Alshammari, Saad & Abid, Ilyes & Ayadi, Rim & Bouazizi, Tarek & Soliman, Alain, 2025. "Exploring the impact of climate change on traditional and energy cryptocurrencies: A cross-sectional approach," International Review of Financial Analysis, Elsevier, vol. 108(PB).
    6. You, Wanhai & Chen, Jianyong & Xie, Haoqi & Ren, Yinghua, 2025. "Which uncertainty measure better predicts gold prices? New evidence from a CNN-LSTM approach," The North American Journal of Economics and Finance, Elsevier, vol. 76(C).
    7. Huynh, Nhan & Phan, Hoa, 2023. "Emotions in the crypto market: Do photos really speak?," Finance Research Letters, Elsevier, vol. 55(PB).
    8. Haq, Inzamam Ul & Naeem, Muhammad Abubakr & Huo, Chunhui & Bakry, Walid, 2025. "Unveiling time-frequency linkages among diverse cryptocurrency classes and climate change concerns," International Review of Economics & Finance, Elsevier, vol. 99(C).

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