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Investor Sentiment and Volatility Prediction of Currencies and Commodities During the COVID-19 Pandemic

Author

Listed:
  • Thi Hong Van Hoang
  • Qasim Raza Syed

    (Montpellier Business School, 2300 avenue des Moulins, Montpellier, France)

Abstract

In this note, we examine whether the volatility predictive power of investor sentiment for currencies and commodities is sensitive to the COVID-19 pandemic. The Credit Suisse Fear Barometer (CSFB) and the VIX are used to measure investor sentiment. The volatility of seven major currencies, gold, and oil is investigated. Using daily data from 2005 to 2020, we show that VIX is a better predictor than CSFB. However, they have no predictive power during the COVID-19 pandemic period. This may be attributed to the different nature of fear sentiment during the crisis.

Suggested Citation

  • Thi Hong Van Hoang & Qasim Raza Syed, 2021. "Investor Sentiment and Volatility Prediction of Currencies and Commodities During the COVID-19 Pandemic," Asian Economics Letters, Asia-Pacific Applied Economics Association, vol. 1(4), pages 1-6.
    • Handle: RePEc:ayb:jrnael:25
    DOI: 2021/08/10
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    References listed on IDEAS

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    1. Dirk G. Baur & Brian M. Lucey, 2010. "Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold," The Financial Review, Eastern Finance Association, vol. 45(2), pages 217-229, May.
    2. Corbet, Shaen & Dowling, Michael & Gao, Xiangyun & Huang, Shupei & Lucey, Brian & Vigne, Samuel A., 2019. "An analysis of the intellectual structure of research on the financial economics of precious metals," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    3. Bouteska, Ahmed, 2019. "The effect of investor sentiment on market reactions to financial earnings restatements: Lessons from the United States," Journal of Behavioral and Experimental Finance, Elsevier, vol. 24(C).
    4. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    5. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    6. Jushan Bai & Serena Ng, 2005. "Tests for Skewness, Kurtosis, and Normality for Time Series Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 49-60, January.
    7. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Cited by:

    1. Rao, Yonghui & Hu, Zijiang & Sharma, Susan Sunila, 2021. "Do managers hedge disaster risk? Extreme earthquake shock and firm innovations," Pacific-Basin Finance Journal, Elsevier, vol. 70(C).
    2. Bing, Tao & Ma, Hongkun, 2021. "COVID-19 pandemic effect on trading and returns: Evidence from the Chinese stock market," Economic Analysis and Policy, Elsevier, vol. 71(C), pages 384-396.
    3. Chen, Xinxin & Guo, Yanhong & Song, Yingying, 2024. "Multiple time scales investor sentiment impact the stock market index fluctuation: From margin trading business perspective," The North American Journal of Economics and Finance, Elsevier, vol. 69(PA).
    4. Siyi Liu & Xin Liu & Chuancai Zhang & Lingli Zhang, 2023. "Institutional and individual investors' short‐term reactions to the COVID‐19 crisis in China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(4), pages 4333-4355, December.
    5. Paresh Kumar Narayan, 2022. "Introduction of the special issue on COVID-19 and the financial and economic systems," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-4, December.
    6. Devpura, Neluka & Narayan, Paresh Kumar & Sharma, Susan Sunila, 2021. "Bond return predictability: Evidence from 25 OECD countries," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    7. Li, Sufang & Xu, Qiufan & Lv, Yixue & Yuan, Di, 2022. "Public attention, oil and gold markets during the COVID-19: Evidence from time-frequency analysis," Resources Policy, Elsevier, vol. 78(C).
    8. Shashank Kathpal & Asif Akhtar & Asma Zaheer & Mohd Naved Khan, 2021. "Covid-19 and heuristic biases: evidence from India," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 26(4), pages 305-316, December.
    9. Narayan, Paresh Kumar & Narayan, Seema, 2021. "Do opinion polls on government preference influence stock returns?," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).
    10. Paresh Kumar Narayan & Solikin M. Juhro, 2022. "Stimulating Economic Recovery, Promoting Sustainable- Inclusive Growth: Challenges And Opportunities," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 25(Special I), pages 1-1, March.

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    More about this item

    Keywords

    gfc; commodities; currencies; volatility prediction; investor sentiment; covid-19;
    All these keywords.

    JEL classification:

    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market
    • I1 - Health, Education, and Welfare - - Health

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