Modeling Exchange Rate Volatility: Application of the GARCH and EGARCH Models

Abstract

Policy makers need accurate forecasts about future values of exchange rates. This is due to the fact that exchange rate volatility is a useful measure of uncertainty about the economic environment of a country. This paper applies univariate nonlinear time series analysis to the daily (TZS/USD) exchange rate data spanning from January 4, 2009 to July 27, 2015 to examine the behavior of exchange rate in Tanzania. To capture the symmetry effect in exchange rate data, the paper applies both ARCH and GARCH models. Also, the paper employs exponential GARCH (EGARCH) model to capture the asymmetry in volatility clustering and the leverage effect in exchange rate. The paper reveals that exchange rate series exhibits the empirical regularities such as clustering volatility, nonstationarity, non-normality and serial correlation that justify the application of the ARCH methodology. The results also suggest that exchange rate behavior is generally influenced by previous information about exchange rate. This also implies that previous day's volatility in exchange rate can affect current volatility of exchange rate. In addition, the estimate for asymmetric volatility suggests that positive shocks imply a higher next period conditional variance than negative shocks of the same sign. The main policy implication of these results is that since exchange rate volatility (exchange-rate risk) may increase transaction costs and reduce the gains to international trade, knowledge of exchange rate volatility estimation and forecasting is important for asset pricing and risk management.

The Symptoms of Illness: Does Israel Suffer from “Dutch Disease”?

“…Recent studies confirmed that the GARCH(1,1) model is the most appropriate measure of exchange-rate volatility [34,35]. Additionally, research by [36] revealed that the exchange-rate series exhibits empirical regularities such as clustering volatility, non-stationarity, non-normality, and serial correlation, which justify the application of the GARCH methodology. Another recent study by [37] that used GARCH(1,1) found that exchange rate volatility affects both international trade and foreign direct investment (FDI) significantly but negatively in countries engaged in OBOR (One Belt One Road is a global development strategy adopted by the Chinese government involving infrastructure development and investments in 152 countries and international organizations in Asia, Europe, Africa, the Middle East, and the Americas).…”

Dövi̇z Kuru Volati̇li̇tesi̇ni̇n Doğrusal Ve Doğrusal Olmayan Yöntemler İle İncelenmesi̇

“…Ayrıca bu çalışmalar, döviz kurundaki değişimin oynaklık kümelenmesi (volatility clustering) sergilediğini yani kur getirilerinde meydana gelen büyük değişimleri büyük, küçük değişimleri ise yine küçük miktarlı değişimlerin izlediğini ortaya koymaktadır. Finansal piyasaların hareketli bir enstrümanı olan döviz kurlarının bu dinamik yapısının anlaşılması ve zaman içerisinde değişkenlik gösteren volatilitesinin modellenmesine yönelik birçok çalışma yapılmıştır (Hsieh, 1988;McKenzie ve Mitchell, 2002;Beine ve diğerleri, 2003;Mapa 2004;Sandoval, 2006;Vee ve diğerleri, 2011;Antonakakis ve Darby, 2013;Miletić, 2015;Epaphra, 2017;Stillwagon ve Sullivan, 2019).…”

Unravelling the Cipher of Indian Rupee’s Volatility: Testing the Forecasting Efficacy of the Rolling Symmetric and Asymmetric GARCH Models

“…The study found that negative shocks had a larger impact than the positive shocks in the market under the study. Univariate nonlinear time series analysis was applied by [13] to the daily (TZS/USD) exchange rate data spanning from January 4, 2009 to July 27, 2015 to examine the behavior of the exchange rate in Tanzania. Both ARCH and GARCH models were used.…”

“…Mean Absolute Error (MAE) has also been calculated to evaluate the efficacy of forecasting. Both these measures have been used by many previous studies including [6] and [13] amongst others. Thereafter, the Diebold-Mariano [14] test, henceforth DM, is used to compare the predictive accuracy of both the models over the entire sample period.…”