A Case on Volatility and Volatility Spillover in the Turkey’s Foreign Exchange Market

Akkaya, Murat

doi:10.29023/alanyaakademik.1088204

Cited by 1 publication

(2 citation statements)

References 31 publications

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“…The foreign exchange rate volatility in Türkiye is modelled in another study where GARCH(1,1) and EGARCH(1,1) models are employed and it is found out that various policies have similar effects on the foreign exchange rate volatilities (Ayhan, 2006). In another work, the USD/TRY and USD/DM exchange rates in Türkiye are modelled for the 1988-1995 period uaing GARCH models and it is exposed that foreign exchange rate volatilities increase during the economic crisis periods (Aysoy, 1996). Asymmetric Power ARCH (APARCH) models are utilized to represent the foreign exchange (Demirgil and Kesekler, 2019).…”

Section: Literature Surveymentioning

confidence: 99%

“…The volatility of the MYR/GBP is modelled employing GARCH, EGARCH, GARCH-M and EGARCH-M models and it is shown that GARCH-M model provides the best goodness of fit metric (Chong et al, 2011). In another work, the USD/TRY and DM/TRY exchange rates are modelled employing ARCH and GARCH models and it is concluded that GARCH(1,1) provides the best accuracy (Aysoy et al, 1996). Similarly, the exchange rate volatility in Türkiye is investigated using ARCH, GARCH and SWARCH methods where it is observed that the SWARCH model has high accuracy (Guloglu and Akman, 2007).…”

Section: Literature Surveymentioning

confidence: 99%

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Modelling the Exchange Rates in Türkiye Employing Machine Learning Methods

Yamaçli¹

2023

CJMS

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Exchange rates affect several macroeconomic parameters in open economies. Considering their importance, the USD/TRY and EUR/TRY exchange rates in Türkiye are modelled employing machine learning methods in this study. The exchange rate data are gathered from the official sources for the period of 2003-2023 and then their nonlinearities are inspected in EViews software. As the next step, a machine learning model, namely an autoregressive deep learning model is developed in Python programming language. The developed model is trained separately for the USD/TRY and EUR/TRY exchange rate data. The loss curves regarding the training phases show that the developed model is trained effectively for the exchange rate data. Then, the actual exchange rate data and the results of the developed autoregressive deep learning model are plotted in the same axes showing overlap in a wide range. The performance metrics of the developed model for the USD/TRY and EUR/TRY modelling such as the coefficient of determination, mean absolute error, mean absolute percentage error and the root mean square error are also calculating further verifying the accuracy of the developed autoregressive deep learning model. It is argued that the developed model can also be applied for the modelling of the nonlinear econometric data for other cases.

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Section: Literature Surveymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%