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Impact of COVID-19 on Exchange rate volatility of Bangladesh: Evidence through GARCH model

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  • Rizwanul Karim

Abstract

This study uses the GARCH (1,1) model to examine the impact of COVID-19 cases (log value) on the volatility of the Exchange rate return of Bangladeshi taka (BDT) over the US dollar (USD), Japanese Yen (JPY), and Swedish Krona (SEK). The result shows that an increase in the number of COVID-19-affected cases in Bangladesh has a significant and positive impact on the volatility of exchange rates BDT/USD, BDT/JPY, and BDT/SEK.

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  • Rizwanul Karim, 2024. "Impact of COVID-19 on Exchange rate volatility of Bangladesh: Evidence through GARCH model," Papers 2403.02560, arXiv.org.
    • Handle: RePEc:arx:papers:2403.02560
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    References listed on IDEAS

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    1. Claudiu Tiberiu Albulescu, 2020. "Coronavirus and financial volatility: 40 days of fasting and fear," Working Papers hal-02501814, HAL.
    2. Goodell, John W., 2020. "COVID-19 and finance: Agendas for future research," Finance Research Letters, Elsevier, vol. 35(C).
    3. Al-Awadhi, Abdullah M. & Alsaifi, Khaled & Al-Awadhi, Ahmad & Alhammadi, Salah, 2020. "Death and contagious infectious diseases: Impact of the COVID-19 virus on stock market returns," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. S. M. Abdullah & Salina Siddiqua & Muhammad Shahadat Hossain Siddiquee & Nazmul Hossain, 2017. "Modeling and forecasting exchange rate volatility in Bangladesh using GARCH models: a comparison based on normal and Student’s t-error distribution," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 3(1), pages 1-19, December.
    6. Sadorsky, Perry, 2006. "Modeling and forecasting petroleum futures volatility," Energy Economics, Elsevier, vol. 28(4), pages 467-488, July.
    7. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    8. Bollerslev, Tim, 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 542-547, August.
    9. 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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