Do Monetary Policy Measures Affect Foreign Exchange Rates during the COVID-19 Pandemic? Evidence from Turkey

Year 2021, , 175 - 202, 31.08.2021

Mustafa Tevfik Kartal Özer Depren Serpil Kılıç Depren

https://doi.org/10.46520/bddkdergisi.987416

Cited By: 6

Abstract

Keywords

FX rates

Do Monetary Policy Measures Affect Foreign Exchange Rates during the COVID-19 Pandemic? Evidence from Turkey

Abstract

The study examines how foreign exchange (FX) rates in Turkey are affected by the pandemic considering the impacts of monetary policy responses to the pandemic. Selected FX
rates are examined by using 10 independent variables containing monetary policy indicators and the pandemic figures. In this context, daily data from February 1, 2019 to August 31,
2020 that consists of the pre-pandemic and the pandemic periods are considered and machine learning algorithms are applied. The findings reveal that the pandemic and monetary
policy indicators have a statistically significant and high effect on the FX rates, and the influence of independent factors on the FX rates vary according to the periods. According to the
results of the study, it is emphasized the importance of the pandemic and monetary policy measures on the FX rates because monetary policy indicators have a statistically significant
and high impact on the FX rates in Turkey for the pandemic period.

Keywords

FX rates, Machine learning algorthims, Monetary policy

References

• 1. Akhtaruzzaman, M., Boubaker, S., & Şensoy, A. (2020). Financial Contagion during COVID-19 Crisis. Finance Research Letters, 101604.
• 2. Apple. (2020). Mobility Trends Reports, https://covid19.apple.com/mobility, 09.05.2020.
• 3. Atmaca, S., & Karadaş, H. A. (2020). Decision Making on Financial Investment in Turkey by Using ARDL Long-Term Coefficients and AHP. Financial Innovation, 6(1), 1-22.
• 4. Bloomberg. (2020). Bloomberg Terminal, 09.05.2020.
• 5. CBRT. (2020). Electronic Data Distribution System (EVDS), https://evds2. tcmb.gov.tr/index.php?/evds/serieMarket, 09.05.2020.
• 6. CSD. (2020). Data of Foreign Investors’ Net Buying Amount. Obtained from CSD of Turkey via e-mail on 09.05.2020.
• 7. Clark, J., & Berko, E. (1997). Foreign Investment Fluctuations and Emerging Market Stock Returns: The Case of Mexico. FRB of New York, Staff Report, No. 24.
• 8. Demir, C. (2019). Macroeconomic Determinants of Stock Market Fluctuations: The Case of BIST-100. Economies, 7(1), 8.
• 9. Depren, Ö., Kartal, M. T., & Kılıç Depren, S. (2021). Changes of Gold Prices in COVID-19 Pandemic: Daily Evidence from Turkey’s Monetary Policy Measures. Technological Forecasting & Social Change, 170, 1-12.
• 10. Ertuğrul, H. M., & Öztürk, H. (2013). The Drivers of Credit Swap Prices: Evidence from Selected Emerging Market Countries. Emerging Markets Finance & Trade, 49, 228-249.
• 11. Fontana, A., & Scheicher, M. (2016). An Analysis of Euro Area Sovereign CDS and Their Relation with Government Bonds. Journal of Banking & Finance, 62, 126-140.
• 12. Gangopadhyay, K., Jangir, A., & Sensarma, R. (2016). Forecasting the Price of Gold: An Error Correction Approach. IIMB Management Review, 28(1), 6-12.
• 13. Hastie, T., Tibshirani, R., & Friedman, J. (1996). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer-Verlag.
• 14. Hasan, I., Liu, L., & Zhang, G. (2016). The Determinants of Global Bank Credit- Default-Swap Spreads. Journal of Financial Services Research, 50(3), 275- 309.
• 15. Hassan, M. K., Kayhan, S., & Bayat, T. (2017). Does Credit Default Swap Spread Affect the Value of the Turkish Lira Against the US Dollar? Borsa Istanbul Review, 17(1), 1-9.
• 16. Jebran, K., & Iqbal, A. (2016). Dynamics of Volatility Spillover between Stock Market and Foreign Exchange Market: Evidence from Asian Countries. Financial Innovation, 2(1), 3.
• 17. Karikari, J. A. (1992). Causality between Direct Foreign Investment and Economic Output in Ghana. Journal of Economic Development, 17(1), 7-17.
• 18. Kartal, M. T. (2020). The Behavior of Sovereign Credit Default Swaps (CDS) Spread: Evidence from Turkey with the Effect of Covid-19 Pandemic. Quantitative Finance and Economics, 4(3), 489-502.
• 19. Kartal, M. T., Kılıç Depren, S., & Depren, Ö. (2018). Türkiye’de Döviz Kurlarını Etkileyen Makroekonomik Göstergelerin Belirlenmesi: MARS Yöntemi İle Bir İnceleme. MANAS Sosyal Araştırmalar Dergisi, 7(1), 209-229.
• 20. Kartal, M. T., Depren, Ö., & Kılıç Depren, S. (2020). The Determinants of Main Stock Exchange Index Changes in Emerging Countries: Evidence from Turkey in Covid-19 Pandemic Age. Quantitative Finance and Economics, 4(4), 526-541.
• 21. Kartal, M. T., Kılıç Depren, S., & Depren, Ö. (2021a). How Main Stock Exchange Indices React to Covid-19 Pandemic: Daily Evidence from East Asian Countries. Global Economic Review, 50(1), 54-71.
• 22. Kartal, M. T., Depren, Ö., & Kılıç Depren, S. (2021b). The Relationship between Mobility and COVID-19 Pandemic: Daily Evidence from an Emerging Country by Causality Analysis. Transportation Research Interdisciplinary Perspectives, 10, 100366, 1-10.
• 23. Khan, M. K., Teng, J. Z., & Khan, M. I. (2019). Cointegration between Macroeconomic Factors and the Exchange Rate USD/CNY. Financial Innovation,5(1), 1-15.
• 24. MHT. (2020). COVID-19 Numbers, https://covid19.saglik.gov.tr/TR-66935/ genel-koronavirus-tablosu.html, 09.05.2020.
• 25. Nouvellet, P., Bhatia, S., Cori, A., Ainslie, K. E., Baguelin, M., Bhatt, S., ... & Donnelly, C. A. (2021). Reduction in Mobility and COVID-19 Transmission. Nature Communications, 12(1), 1-9.
• 26. Orhan, A., Kırıkkaleli, D., & Ayhan, F. (2019). Analysis of Wavelet Coherence: Service Sector Index and Economic Growth in an Emerging Market. Sustainability, (11), 6684.
• 27. Phan, D. H. B., & Narayan, P. K. (2020). Country Responses and the Reaction of the Stock Market to COVID-19-A Preliminary Exposition. Emerging Markets Finance and Trade, 56(10), 2138-2150.
• 28. Sjaastad, L. A. (2008). The Price of Gold and the Exchange Rates: Once Again. Resources Policy, 33(2), 118-124.
• 29. Towell, G.G., & Shavlik, J.W. (1993). Extracting Refined Rules from Knowledge- Based Neural Networks. Machine Learning, 13, 71-101.
• 30. Wang, H., & Yamamoto, N. (2020). Using a Partial Differential Equation with Google Mobility Data to Predict COVID-19 in Arizona. Mathematical Biosciences and Engineering, 17(5), 4891-4904.
• 31. WHO. (2020a). Coronavirus Disease (COVID-19) Pandemic, https://www. euro.who.int/en/health-topics/health-emergencies/coronavirus-covid-19/ novel-coronavirus-2019-ncov, 09.05.2020.
• 32. WHO. (2020b). COVID-19 Numbers, https://COVID19.who.int, 08.18.2020.
• 33. Wiener, M., & Liaw, A. (2002). Classification and Regression by randomForest. R News, (2)3, 18-22.
• 34. Yılmazkuday, H. (2020). COVID-19 and Welfare Costs of Reduced Mobility. Journal of Regional Science, Forthcoming.
• 35. Yılmazkuday, H. (2021). Stay-at-Home Works to Fight against COVID-19: International Evidence from Google Mobility Data. Journal of Human Behavior in the Social Environment, 31(1-4), 210-220.
• 36. Zengin, S., Yüksel, S., & Kartal, M. T. (2018). Understanding the Factors that Affect Foreign Direct Investment in Turkey by Using MARS Method. Finansal Araştırmalar ve Çalışmalar Dergisi, 10, 1309-1123.
• 37. Zhu, D., Mishra, S. R., Han, X., & Santo, K. (2020). Social Distancing in Latin America during the COVID-19 Pandemic: An Analysis Using the Stringency Index and Google Community Mobility Reports. Journal of Travel Medicine, 27(8), taaa125.