Main Article Content
Abstract
Rate changes can occur hourly, daily, or in large incremental shifts. These changes may impact firms by changing the cost of commodities imported from other countries and the demand for their goods among foreign consumers. Therefore, it is essential to forecast exchange rates to manage this business effect. This study aims to determine the best model for predicting volatility in the exchange rate between USD and GBP. In particular, we analyze exchange rates using the Autoregressive Integrated Moving Average (ARIMA) model and the volatility or variance model by Generalized Autoregressive Conditional Heteroscedasticity (GARCH). To determine the best model, the performance of each model is evaluated with several criteria, namely Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results show that EGARCH(1,1) has the best forecasting performance in the out-sample section because it can better capture out-sample data patterns with minimum RMSE, MAE, and MAPE.
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References
- A. O’Sullivan, S. Sheffrin, and S. Perez, Microeconomics: Principles, Applications and Tools, Student Value Edition, 9th ed. London, United Kingdom: Pearson, 2016.
- “How Exchange Rates Affect Your Business | Harvard Business Services,” The HBS Blog. https://www.delawareinc.com/blog/exchange-rates/ (accessed Dec. 28, 2022).
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- E. Zivot, “Practical Issues in the Analysis of Univariate GARCH Models,” in Handbook of Financial Time Series, T. Mikosch, J.-P. Kreiß, R.A. Davis, and T.G. Andersen, Eds. Berlin, Germany: Springer, 2009, pp. 113–155, doi: 10.1007/978-3-540-71297-8_5.
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References
A. O’Sullivan, S. Sheffrin, and S. Perez, Microeconomics: Principles, Applications and Tools, Student Value Edition, 9th ed. London, United Kingdom: Pearson, 2016.
“How Exchange Rates Affect Your Business | Harvard Business Services,” The HBS Blog. https://www.delawareinc.com/blog/exchange-rates/ (accessed Dec. 28, 2022).
R.A. Meese and K. Rogoff, “Empirical Exchange Rate Models of the Seventies,” Journal of International Economics, vol. 14, no. 1–2, pp. 3–24, 1983, doi: 10.1016/0022-1996(83)90017-x.
I.M. Ghani and H.A. Rahim, “Modeling and Forecasting of Volatility using ARMA-GARCH: Case Study on Malaysia Natural Rubber Prices,” IOP Conference Series: Materials Science and Engineering, vol. 548, no. 1, pp. 1–13, 2019, doi: 10.1088/1757-899X/548/1/012023.
H.H. Goh, K.L. Tan, C.Y. Khor, and S.L. Ng, “Volatility and Market Risk of Rubber Price in Malaysia: Pre- and Post-Global Financial Crisis,” Journal of Quantitative Economics, vol. 14, no. 2, pp. 323–344, 2016, doi: 10.1007/s40953-016-0037-4.
D.N. Gujarati, Basic Econometrics. New York: Gary Burke, 1972.
P.J. Brockwell and R.A. Davis, Time Series: Theory and Methods. New York, NY, USA: Springer, 1991.
G.M. Ljung and G.E.P. Box, “On a Measure of a Lack of Fit in Time Series Models,” Biometrika, vol. 65, no. 2, pp. 297–303, Aug. 1978, doi: 10.1093/biomet/65.2.297.
E. Zivot, “Practical Issues in the Analysis of Univariate GARCH Models,” in Handbook of Financial Time Series, T. Mikosch, J.-P. Kreiß, R.A. Davis, and T.G. Andersen, Eds. Berlin, Germany: Springer, 2009, pp. 113–155, doi: 10.1007/978-3-540-71297-8_5.
C. Brooks and S.P. Burke, “Information Criteria for GARCH Model Selection,” The European Journal of Finance, vol. 9, no. 6, pp. 557–580, 2003, doi: 10.1080/1351847021000029188.
F. Aliyev, R. Ajayi, and N. Gasim, “Modelling Asymmetric Market Volatility with Univariate GARCH Models: Evidence from Nasdaq-100,” The Journal of Economic Asymmetries, vol. 22, pp. 1–10, Nov. 2020, doi: 10.1016/j.jeca.2020.e00167.
D.B. Nelson, “Conditional Heteroscedasticity in Asset Returns : A New Approach,” Econometrica, vol. 59, no. 2, pp. 347–370, Mar. 1991, doi: 10.2307/2938260.