Özet:

Most analyses of exchange rate volatility in the economic literature are conducted by means of autoregressive conditional heteroskedasticity (ARCH) or generalized ARCH (GARCH) models. According to Humilton and Susmel such models often predict higher volatility than their actual volatility rates, and their predictive performance is considerably low. Diebold and Lamoureux and Lastapes attributed this to the structural change in the ARCH process. Moreover, Hamilton and Susmel developed the Markov-switching ARCH (MS-ARCH or SWARCH) model to overcome the reliability problem of parameter estimates that do not allow for a regime change. This method presents a nonlinear structure enabling regime changes. Therefore, the MS-ARCH method was preferred in the study. Considering the continuing massive impact of COVID-19 on the global financial system, its influence on exchange rates must also be explored. This question was addressed in the analysis. In this direction, the effect of volatility was estimated with the MS-ARCH model using the return values of USD/TRY exchange rate in the trading days between March 2020 and October 2021, the month March 2020 when the first COVID-19 case appeared in Turkey. Two volatility regimes, namely, low volatility and high volatility, were employed in the study. The findings demonstrate that the COVID-19 pandemic, along with various economic and political events in Turkey and the world, affects exchange rate volatility and that these volatility periods are permanent. It also depicts that the USD/TRY return series has high volatility and a strong regime dependency. From these results, it may be concluded that the forecasting of information on exchange rate volatility is important for asset pricing and risk management because exchange rate volatility can increase transaction costs and reduce gains in international trade. The article contributes to the existing body of literature by explaining volatility modeling in the light of the recent daily exchange rate returns during the COVID-19 pandemic.

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