The Impact of News Related Covid-19 on Exchange Rate Volatility: A New Evidence From Generalized Autoregressive Score Model

Year 2023, Issue: 38, 105 - 126, 28.07.2023

Deniz Erer

https://doi.org/10.26650/ekoist.2023.38.1179575

Abstract

The COVID-19 pandemic causes serious problems for the economy. When considering the significant impact the COVID-19 pandemic had on capital flows and global trade, it can be stated that the outbreak of this virus has caused sharp fluctuations in exchange rate markets. From this point of view, this study examines the effect of the news regarding the COVID-19 pandemic on exchange rate volatility for 12 emerging and developed countries that were most affected by the outbreak. The data covers the period between January 1, 2019 and August 31, 2022. For this purpose, we use the Generalized Autoregressive Score (GAS) model with student-t distribution, which is a new approach to measure the volatility of a financial series and to obtain the volatility clustering and fat-tail properties of a financial series. The findings of thisstudy show that panic and fake news about the COVID-19 pandemic hasincreased the volatilites of exchange rates, while media hype news decreasesthe volatilities. These resultsindicate that the negative and speculative newsregarding COVID-19 adversely affects exchange rate volatility through increasing the uncertainty of financial markets.

Keywords

Exchange Rate Volatility, COVID-19, Generalized Autoregressive Score Model, Expected Shortfall, Tail Effect

References

• Abdullah, S. M., Siddiqua, S., Siddiquee, M. S. H., & Hossain, N. (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, 3(1), 1-19. google scholar
• Abdullah, S. M., Siddiqua, S., Siddiquee, M. S. H., & Hossain, N. (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, 3(1), 1-19. google scholar
• Alberg, D., Shalit, H., & Yosef, R. (2008). Estimating stock market volatility using asymmetric GARCH models. Applied Financial Economics, 18(15), 1201-1208. google scholar
• Andersen, T. G., Bollerslev, T:, Diebold, F.X., Vega,C. (2003). ‘Micro effects of macro announcements. Real-time price discovery in foreign exchange’. Am. Economic Review 93(1), 38-62. google scholar
• Andersen, T. G., Bollerslev, T:, Diebold, F.X., Vega,C. (2007). ‘Real-time price discovery in global stock, bond, and foreign exchange markets. Journal of International Economics, 73(2), 251-277 google scholar
• Ardia, D., Boudt, K., & Catania, L. (2016). Generalized autoregressive score models in R: The GAS package. arXiv preprint arXiv:1609.02354. google scholar
• Arı, Y. (2022). From discrete to continuous: GARCH volatility modeling of the Bitcoin. Ege Academic Review, 22(3), 353-370. google scholar
• Babatunde, O. T., Oranye, H. E., & Nwafor, C. N. (2020). Volatility of Some Selected Currencies Against the Naira Using Generalized Autoregressive Score Models. International Journal of Statistical Distributions and Applications, 6(3), 42. google scholar
• Balduzzi, P., Elton, E.J., Green, T.C., (2001). ‘Economic news and the yield curve: evidence from the US Treasury market’. J. Financ. Quant. Anal. 36 (4), 523-543. google scholar
• Barunik, J., Krehlik, T., & Vacha, L. (2016). Modeling and forecasting exchange rate volatility in time-frequency domain. European Journal of Operational Research, 251(1), 329-340. google scholar
• Bauwens, L. and Hautsch, N. (2006). ‘Stochastic Conditional Intensity Process’. Journal of Financial Econometrics 4(3), 450-493. google scholar
• Birz, G., Lott, J.R., 2013. ‘The effect of macroeconomic news on stock returns: new evidence from newspaper coverage’. J. Bank. Finance 35, 2791-2800. google scholar
• Blasques, F., Gorgi, P., & Koopman, S. J. (2019). Accelerating score-driven time series models. Journal of Econometrics, 212(2), 359-376. google scholar
• Blattberg, R. C., & Gonedes, N. J. (1974). A Comparison of the Stable and Student Distributions as Statistical Models for Stock Prices. The Journal of Business, 47(2), 244-280. http://www.jstor. org/stable/2353383 google scholar
• Blitz, Z., Huisman, R., Swinkels, L. and van Vliet, P. (2019). ‘Media Attention and the Volatility Effect’ Finance Research Letters, 101317. google scholar
• Bollerslev, T. (1986). ‘Generalized Autoregressive Conditional Heteroskedasticity’, Journal of Econometrics 31(3), 307-327. google scholar
• Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327. google scholar
• Bollerslev, T. (2010) Glossary to ARCH (GARCH*), in Volatility and Time Series Econometrics: Essays in Honor of Robert Engle, Bollerslev, T., Russell, J. and Watson, M. (Eds). doi:10.1093/ acprof:oso/ 9780199549498.001.0001 google scholar
• Branson, W. H. (1977), “Asset Markets and Relative Prices in Exchange Rate Determination, Sozialwissenschafiliche Annalen, 1(1), 69-89. google scholar
• Branson, W. H. (1981), “Macroeconomic Determinants of Real Exchange Rates,’ NBER Working Paper, No. 801, Cambridge, MA: NBER. google scholar
• Branson, W. H. (1983), “A Model of Exchange Rate Determination with Policy Reaction: Evidence from Monthly Data,’ NBER Working Paper, No. 1135, Cambridge, MA: NBER. google scholar
• Campbell, J.Y., Grossman, S.J., Wang, J., (1993). ‘Trading volume and serial correlation in stock returns. Q. J. Econ. 108, 905-939. google scholar
• Caporale, G. M., Spagnolo, F., Spagnolo, N. (2018). ‘Exchange rates and macro news in emerging markets. Research in International Business and Finance, 46, 516-527. google scholar
• Cepoi, C.O. (2020). ‘Asymmetric Dependence Between Stock Market Returns and News During COVID-19 Financial Turmoil’, Finance Research Letters, 1-5. google scholar
• Cerqueti, R., Giacalone, M., & Mattera, R. (2020). Skewed non-Gaussian GARCH models for cryptocurrencies volatility modelling. Information Sciences, 527, 1-26. google scholar
• Christoffersen, P. F. (1998). Evaluating interval forecasts. International Economic Review, 841862. google scholar
• Chu, J., Chan, S., Nadarajah, S., & Osterrieder, J. (2017). GARCH modelling of cryptocurrencies. Journal of Risk and Financial Management, 10(4), 17. google scholar
• Clark, P. (1973) . A Subordinate Stochastic Process Model With Finite Variance for Spreculative Prices. Econometrica, 50, 987-1008. google scholar
• Cox, D.R. (1981). ‘Statistical Analysis of Time Series: Some Recent Developments’, Scandinavian Journal of Statistics 8, 93-115. google scholar
• Creal, D., Koopman, J. and Lucas, A. (2013). ‘Generalized Autoregressive Score Models With Applications’, Journal of Applied Econometrics 28(5), 777-795. google scholar
• Creal, D., Koopman, J., and Lucas, A. (2011), “A Dynamic Multivariate Heavy- Tailed Model for Time-Varying Volatilities and Correlations,” Journal of Business & Economic Statistics, 29 (4), 552-563. google scholar
• Dai, L., Parwasa, J.T. and Zhang, B. (2015). ‘The Governance Effect of the Media’s News Dissemination Role: Evidence From Insider Trading’, Journal of Accounting Research 53, 331366. google scholar
• De Long, Shleifer, A., Summers, L.H., Waldmann, R.J., (1990). ‘Noise trader risk in financial markets’. J. Polit. Econ 98-703-738. google scholar
• Donkor, R. A., Mensah, L., & Sarpong-Kumankoma, E. (2022). Oil price volatility and US dollar exchange rate volatility of some oil-dependent economies. The Journal of International Trade & Economic Development, 31(4), 581-597. google scholar
• Dornbusch, R. (1976). ‘Expectations and exchange rate dynamics. Journal of Political Economy, 84, 1161-1176. google scholar
• Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the econometric society, 987-1007. google scholar
• Engle, R.F. (1982). ‘Autoregressive Conditional Heteroskedasticity with Estimates of the Variance oof UKInflation’, Econometrica 50, 987-1008. google scholar
• Engle, R.F. (2002). ‘Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models. Journal of Business and Economic Statistics 20(3), 339-350. google scholar
• Engle, R.F. and Bollerslev, T. (1986). ‘Modelling the Persistence of Conditional Variances’. Econometric Reviews 5(1), 1-50. google scholar
• Engle, R.F. and Russell, J.R. (1998). ‘Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data’, Econometrica 66(5), 1127-1162. google scholar
• Erer, E. and Erer, D. (2018) “Volatility Spillover Effect with Time-Varying Parameters Between BIST100 and Dow-Jones Under Different Regimes”. Empirical Economic Letters, 17 (3): 339- 348 google scholar
• Fama, E. F. (1965). The behavior of stock-market prices. The journal of Business, 38(1), 34-105. google scholar
• Fama, E.F. (1970), ‘Efficient Capital Markets: A Review of Theory and Empirical Work’, Journal of Finance, 25, s. 383-417 google scholar
• Fama, E.F., (1970). ‘Efficient capital markets: a review of theory and empirical work’. J. Finance 25 (2), 383-417. google scholar
• Frenkel, J. A. (1976). ‘A monetary approach to the exchange rate: Doctrinal aspects and empirical evidence. Scandinavian Journal of Economics, 78, 200-224. google scholar
• Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The journal of finance, 48(5), 1779-1801. google scholar
• Harvey, A., & Luati, A. (2014). Filtering with heavy tails. Journal of the American Statistical Association, 109(507), 1112-1122. google scholar
• Harvey, A., & Sucarrat, G. (2014). EGARCH models with fat tails, skewness, and leverage. Computational Statistics & Data Analysis, 76, 320-338. google scholar
• Harvey, A.C. (2013). ‘Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series, Cambridge University Press 52. google scholar
• Ho, K.Y., Shi, Y. and Zhang, Z. (2017). ‘Does News Matter in China’s Foreign Exchange Market: Chinese RMB Volatility and Public Information Arrivals’, International Review of Economics and Finance 52, 302-321. google scholar
• Hsieh, D. A. (1988). The statistical properties of daily foreign exchange rates: 1974-1983. Journal of international economics, 24(1-2), 129-145. google scholar
• Jabeen, M., Rashid, A., & Ihsan, H. (2020). The news effects on exchange rate returns and volatility: Evidence from Pakistan. International Journal of Finance & Economics, 27(1), 745-769. google scholar
• Jeribi, A., & Ghorbel, A. (2021). Forecasting developed and BRICS stock markets with cryptocurrencies and gold: generalized orthogonal generalized autoregressive conditional heteroskedasticity and generalized autoregressive score analysis. International Journal of Emerging Markets. google scholar
• Koopman, S.J., Lucas, A. and Monteiro, A. (2008). ‘The Multi-State Latent Factor Intensity Model for Credit Rating Transitions’, Journal of Econometrics 142(1), 399-424. google scholar
• Kupiec, P. H. (1995). Techniques for verifying the accuracy of risk measurement models (Vol. 95, No. 24). Division of Research and Statistics, Division of Monetary Affairs, Federal Reserve Board. google scholar
• Laakkonen, H. (2007). The Impact of Macroeconomic News on Exchange Rate Volatility, SSRN Electronic Journal, 20(1), 23-40 google scholar
• Laakkonen, H. (2007). The impact of macroeconomic news on exchange rate volatility. Finnish Economic Papers, 20(1), 23-40. google scholar
• Lazar, E., & Xue, X. (2020). Forecasting risk measures using intraday data in a generalized autoregressive score framework. International Journal of Forecasting, 36(3), 1057-1072. google scholar
• Lim, C. M., & Sek, S. K. (2013). Comparing the performances of GARCH-type models in capturing the stock market volatility in Malaysia. Procedia Economics and Finance, 5, 478-487. google scholar
• Lin, Z. (2018). Modelling and forecasting the stock market volatility of SSE Composite Index using GARCH models. Future Generation Computer Systems, 79, 960-972. google scholar
• Liu, Y., Han, L., Yin, L. (2019). News Implied Volatility and Long-term Foreign Exchange Market Volatility. International Review of Financial Analysis, 61, 126-142 google scholar
• Makatjane, K. D., & Kalebe, K. M. (2018). Modeling Conditional Volatility of Saving Rate by a Time-Varying Parameter Model. International Journal of Economics and Management Engineering, 12(9), 1171-1174. google scholar
• Makatjane, K.D., Xaba, D.L., and Moroke, N.D. (2017), “Application of Generalized Autoregressive Score Model to Stock Returns,” World Economy of Science, Engineering and Technology, International Journal of Economics and Management Engineering, 11 (11), 2017. google scholar
• Mandelbrot, B., 1963. The variation of certain speculative prices. Journal of Business 36 (4), 394419. google scholar
• McFarland, J. W., Pettit, R. R., & Sung, S. K. (1982). The distribution of foreign exchange price changes: trading day effects and risk measurement. the Journal of Finance, 37(3), 693-715. google scholar
• Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the econometric society, 347-370. google scholar
• Ogutu, C., Canhanga, B. and Biganda, P. (2018), “Modeling Exchange Rate Volatility Using APARCH Models”, Journal of the Institute of Engineering, 14(1): 96-106. google scholar
• Omrane, W. B., Savaşe, T. (2017). Exchange Rate Volatility Response to Macroeconomic News During the Global Financial Crisis. International Reviews of Financial Analysis, 52, 130-143 google scholar
• Pearce, D.K., Solakoglu, M.N., (2007). ‘Macroeconomic news and exchange rates. J. Financ. Mark. Inst. Money 17 (4), 307-325. google scholar
• Peng, Q., Li, J., Zhao, Y., & Wu, H. (2021). The informational content of implied volatility: Application to the USD/JPY exchange rates. Journal of Asian Economics, 76, 101363. google scholar
• Praetz, P. D. (1972). The distribution of share price changes. Journal of business, 49-55. google scholar
• Rapach, D. E., & Strauss, J. K. (2008). Structural breaks and GARCH models of exchange rate volatility. Journal of Applied Econometrics, 23(1), 65-90. google scholar
• Robert F. Engle and Simone Manganelli CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles Journal of Business & Economic Statistics Vol. 22, No. 4 (Oct., 2004), pp. 367-381. google scholar
• Rogalski, R. J., & Vinso, J. D. (1978). Empirical properties of foreign exchange rates. Journal of International Business Studies, 9(2), 69-79. google scholar
• Rognone, L., Hyde, S. and Zhang, S. (2020). ‘News Sentiment in the Cryptocurrency Market: An Empirical Comparison with Forex’, International Review of Financial Analysis 69, 1-17. google scholar
• Russell, J.R. (2001). ‘Econometric Modeling of Multivariate Irregularly-Spaced High-Frequency Data’, University of Chicago, Graduate School of Business. google scholar
• Shephard, N. (2005). ‘Stochastic Volatility: Selected Readings, Oxford UUniversityPress, Oxford. google scholar
• Smales, L.A. (2014). ‘News sentiment and the Investor Fear Gauge’. Finance Research Letters 11, 122-130. google scholar
• Thorlie, M.A., Song, L., Wang, X. and Amin, M. (2014), “Modelling Exchange Rate Volatility Using Asymmetric GARCH Models: Evidence From Sierra Leone”, International Journal of Science and Research, 3(11): 1206-1214. google scholar
• Troster, V., Tiwari, A. K., Shahbaz, M., & Macedo, D. N. (2019). Bitcoin returns and risk: A general GARCH and GAS analysis. Finance Research Letters, 30, 187-193. google scholar
• Xu, Y., & Lien, D. (2020). Dynamic exchange rate dependences: the effect of the US-China trade war. Journal of International Financial Markets, Institutions and Money, 68, 101238. google scholar
• Zakoian, J. M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955. google scholar