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Time–frequency return co-movement among asset classes around the COVID-19 outbreak: portfolio implications

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  • Seyed Alireza Athari

    (Cyprus International University)

  • Ngo Thai Hung

    (University of Finance-Marketing)

Abstract

This study explores the time–frequency return connectedness of the four most relevant asset classes namely, equity, digital assets, commodity, and fixed income. To do so, we use the novel proxies of the S&P500 Index for equity, the S&P Cryptocurrency MegaCAP Index for digital assets, the S&P Goldman Sachs Commodity Index for commodity, and the S&P Global Developed Sovereign Bond Index for fixed income, and also employ the wavelet analysis for daily data over the period 2017: M02 to 2021: M09. In contrast to the pre-COVID-19 period, our findings indicate that the interdependence between the selected asset classes has intensified across all time scales and frequency bands during the COVID-19 crisis, proving the lack of hedging opportunities. Besides, the findings reveal that there is a significant lead-lag relationship between time series at medium and low frequencies during the research period, and the directional connectedness among asset classes is sensitive to frequencies. Especially, the co-movements among the pairs are pronounced during the COVID-19 outbreak. Remarkably, the wavelet-based Granger causality test corroborates the wavelet results and underscores there is a significant causal link between the variables during COVID compared to pre-COVID. Moreover, the results of the portfolio risk analysis by employing the value at risk (VaR) measure indicate that portfolio diversity advantages vary among frequency and across time. The results of the present study provide insight and might help foreign portfolio investors diversify their portfolios across different asset classes.

Suggested Citation

  • Seyed Alireza Athari & Ngo Thai Hung, 2022. "Time–frequency return co-movement among asset classes around the COVID-19 outbreak: portfolio implications," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 46(4), pages 736-756, October.
  • Handle: RePEc:spr:jecfin:v:46:y:2022:i:4:d:10.1007_s12197-022-09594-8
    DOI: 10.1007/s12197-022-09594-8
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    References listed on IDEAS

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    Citations

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    Cited by:

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    2. Takashi Kanamura, 2023. "An impact assessment of the COVID-19 pandemic on Japanese and US hotel stocks," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-51, December.
    3. Roman Mestre, 2023. "Stock profiling using time–frequency-varying systematic risk measure," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-29, December.
    4. Georgia Zournatzidou & Christos Floros, 2023. "Hurst Exponent Analysis: Evidence from Volatility Indices and the Volatility of Volatility Indices," JRFM, MDPI, vol. 16(5), pages 1-15, May.
    5. Kočenda, Evžen & Moravcová, Michala, 2024. "Frequency volatility connectedness and portfolio hedging of U.S. energy commodities," Research in International Business and Finance, Elsevier, vol. 69(C).
    6. José Antonio Núñez-Mora & Mario Iván Contreras-Valdez & Roberto Joaquín Santillán-Salgado, 2023. "Risk Premium of Bitcoin and Ethereum during the COVID-19 and Non-COVID-19 Periods: A High-Frequency Approach," Mathematics, MDPI, vol. 11(20), pages 1-20, October.
    7. Gimede Gigante & Emiliano Sironi & Caterina Tridenti, 2023. "At the Frontier of Sustainable Finance: Impact Investing and the Financial Tradeoff; Evidence from Private Portfolio Companies in the United Kingdom," Sustainability, MDPI, vol. 15(5), pages 1-21, February.
    8. Shaen Corbet & Les Oxley, 2023. "Investigating the Academic Response to Cryptocurrencies: Insights from Research Diversification as Separated by Journal Ranking," Review of Corporate Finance, now publishers, vol. 3(4), pages 487-528, September.

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    More about this item

    Keywords

    Asset classes; Commodity; Digital assets; Equity; COVID-19; 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
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • F3 - International Economics - - International Finance
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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