Modeling Exchange Rate Volatility Using ARMA-GARCH Aproach with Non-Gaussian Distributions

Date

2023-9

Author

Girgin, Yeşim

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Modeling exchange rate volatility is a major concern for researchers, investors, and policymakers since it has a wide-ranging impact on the country’s economy, includ- ing inflation, interest, investment, production, and foreign commerce [44]. Therefore, the primary goal of this research is to model the volatility of the exchange rate. For this purpose, the generalized autoregressive conditional heteroscedastic techniques comprising of symmetrical (GARCH) and asymmetrical (EGARCH, TGARCH, and APARCH) models are used in this study. Furthermore, aside from the studies con- ducted in the Turkish literature on that matter regarding models’ distribution, various distributions which consist of skew normal, skew student t, and skew GED along with normal, student t, GED distributions are utilized for the error distribution in GARCH models.The data is taken from CBRT’s closing prices in US dollars consisting of the period of June 2001 to June 2023, and it is divided into 4 sub-periods according to Chow Test results. Therefore, not only the whole data but also its sub-period are ana- lyzed in this thesis. The sub-periods as follows: from June 2001 to July 2013 (Period 1) , from July 2013 to October 2016 (Period 2), from October 2016 to February 2020 (Period 3), and from February 2020 to June 2023 (Period 4). Convenient models for these periods are put forward based on model selection criteria such as Akaike (AIC), Schwarz (SC), and Log-Likelihood. In the end of the study, the results concluded that asymmetric GARCH models provide the best fitting for the time intervals. Added to that, compared to the whole period the sub-periods have low order in the their mean model, and some period has different distribution than the whole data.

Subject Keywords

Volatility, EGARCH, TGARCH, APARCH, Skew Distributions

URI

https://hdl.handle.net/11511/105456

Collections

Graduate School of Applied Mathematics, Thesis