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Health Crisis And Currency Risk: Fresh Evidence From New Data Sets

Author

Listed:
  • Afees A. Salisu

    (University of Pretoria, South Africa)

  • Dinci J. Penzin

    (Central Bank of Nigeria, Nigeria)

  • Yinka S. Hammed

    (Lagos "&" Centre for Econometrics "&" Applied Research, Ibadan, Nigeria)

Abstract

With the aid of a method of predictability analysis that involves a feasible quasigeneralized least squares estimator, we examine the predictive power associated with the newly computed COVID-19 indices, which are disaggregated into six indices for currency market risks (realized volatility of exchange rate). Our sample size covers the period between December 31, 2019 and December 28, 2021. We note mixed outcomes or the major currency markets considered. On average, while the health crisis seems to have heightened the risks associated with Pounds Sterling, Australian Dollar and Canadian Dollar against USD, it exerts a moderating effect on the Euro, Yen and Swiss Franc against USD. However, the indices consistently demonstrate predictive prowess across multiple out-of-sample forecasts, which we adduce to the richness of the new measures.

Suggested Citation

  • Afees A. Salisu & Dinci J. Penzin & Yinka S. Hammed, 2025. "Health Crisis And Currency Risk: Fresh Evidence From New Data Sets," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 28(1), pages 1-14, April.
    • Handle: RePEc:idn:journl:v:28:y:2025:i:1a:p:1-14
    DOI: https://doi.org/10.59091/2460-9196.2136
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    References listed on IDEAS

    as
    1. Salisu, Afees A. & Akanni, Lateef & Raheem, Ibrahim, 2020. "The COVID-19 global fear index and the predictability of commodity price returns," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    2. Scott R. Baker & Nicholas Bloom & Steven J. Davis & Stephen J. Terry, 2020. "COVID-Induced Economic Uncertainty," NBER Working Papers 26983, National Bureau of Economic Research, Inc.
    3. Westerlund, Joakim & Narayan, Paresh Kumar, 2012. "Does the choice of estimator matter when forecasting returns?," Journal of Banking & Finance, Elsevier, vol. 36(9), pages 2632-2640.
    4. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    5. Narayan, Paresh Kumar & Sharma, Susan Sunila & Phan, Dinh Hoang Bach & Liu, Guangqiang, 2020. "Predicting exchange rate returns," Emerging Markets Review, Elsevier, vol. 42(C).
    6. Devpura, Neluka & Narayan, Paresh Kumar & Sharma, Susan Sunila, 2018. "Is stock return predictability time-varying?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 52(C), pages 152-172.
    7. Salisu, Afees A. & Adediran, Idris, 2020. "Gold as a hedge against oil shocks: Evidence from new datasets for oil shocks," Resources Policy, Elsevier, vol. 66(C).
    8. Anasuya Haldar & Narayan Sethi, 2021. "The News Effect Of Covid-19 On Global Financial Market Volatility," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 24(Special I), pages 33-58, January.
    9. Paresh Kumar Narayan & Bernard Njindan Iyke & Susan Sunila Sharma, 2021. "New Measures of the COVID-19 Pandemic - A New Time-Series Dataset," Asian Economics Letters, Asia-Pacific Applied Economics Association, vol. 2(2), pages 1-13.
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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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