Modeling and Forecasting of British Pound/US Dollar Exchange Rate: An Empirical Analysis

  • Chaido Dritsaki
Conference paper
Part of the Springer Proceedings in Business and Economics book series (SPBE)


The aim of this paper is to develop and examine the characteristics of volatility of exchange rate on British pound/US dollar, using symmetric and asymmetric GARCH(p,q) models. Given that there are ARCH effects on exchange rate returns, we estimated ARCH(q), GARCH(p,q), and EGARCH(p,q) including these effects on mean equation. These models were estimated with maximum likelihood method using the following distributions: normal, t-Student, and generalized error distribution. The log-likelihood function was maximized using Marquardt’s algorithm (1963) in order to search for optimal parameters. The results showed that ARIMA(0,0,1)-EGARCH(1,1) model with t-Student distribution is the best in order to describe exchange rate returns and also captures the leverage effect. Finally, for the forecasting of ARIMA(0,0,1)-EGARCH(1,1) model, both the dynamic and static procedures are used. The static procedure provides better results on the forecasting rather than the dynamic.


Exchange rate Volatility GARCH models Forecasting 


  1. Abdalla, S. Z. S. (2012). Modelling exchange rate volatility using GARCH models: Empirical evidence from Arab countries. International Journal of Economics and Finance, 4(3), 216–229. Scholar
  2. Antonakakis, N., & Darby, J. (2012). Forecasting volatility in developing countries’ nominal exchange returns. MPRA Paper No.40875.Google Scholar
  3. Black, F. (1976). Studies of stock price volatility changes. In Proceedings of the 1976 Business Meeting of the Business and Economics Statistics Section. American Statistical Association, Washington, DC (pp. 177–181).Google Scholar
  4. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. Scholar
  5. Dickey, D. A., & Fuller, W. A. (1979). Distributions of the estimators for autoregressive time series with a unit root. Journal of American Statistical Association, 74(366), 427–431. Scholar
  6. Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49(4), 1057–1072. CrossRefGoogle Scholar
  7. Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. inflation. Econometrica, 50(4), 987–1008. Scholar
  8. Epaphra, M. (2017). Modeling exchange rate volatility: Application of the GARCH and EGARCH models. Journal of Mathematical Finance, 7(1), 121–143.
  9. Fama, E. F. (1965). The behavior of stock market prices. Journal of Business, 38(1), 34–105. Scholar
  10. Friedman, D., & Vandersteel, S. (1982). Short-run fluctuations in foreign exchange rates: Evidence from the data 1973-1979. Journal of International Eonomics, 13(1–2), 171–186.CrossRefGoogle Scholar
  11. Jarque, C., & Bera, A. (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters, 6, 255–259.CrossRefGoogle Scholar
  12. Johnston, K., & Scott, E. (2000). GARCH models and the stochastic process underlying exchange rate price changes. Journal of Financial and Strategic Decisions, 13(2), 13–24.Google Scholar
  13. Hsieh, D. A. (1988). The statistical properties of daily foreign exchange rates: 1974–1983. Journal of International Economics, 24, 129–145.CrossRefGoogle Scholar
  14. Ljung, G. M., & Box.G.E.P. (1978). On a measure of a lack of fit in time series models. Biometrika, 65(2), 297–303. Scholar
  15. MacKinnon, J. G. (1996). Numerical distribution functions for unit root and cointegration tests. Journal of Applied Econometrics, 11(6), 601–618.<601::AID-JAE417>3.0.CO;2-T.CrossRefGoogle Scholar
  16. Mandelbrot, B. (1963). The variation of certain speculative prices. Journal of Business, 36(4), 394–414. Scholar
  17. Marquardt, D. W. (1963). An algorithm for least squares estimation of nonlinear parameters. Journal of the Society for Industrial and Applied Mathematics, 11, 431–441. CrossRefGoogle Scholar
  18. Miletić, S. (2015). Modeling and forecasting exchange rate volatility: Comparison between EEC and developed countries. Industrija, 43(1), 7–24. Scholar
  19. Mundaca, G. G. (1991). The volatility of the Norwegian currency basket. Scandinavian Journal of Economics, 93(1), 53–73. Scholar
  20. Nelson, D. B. (1991). Conditional heteroscedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. Scholar
  21. Newey, W. K., & West, K. D. (1994). Automatic lag selection in covariance matrix estimation. Review of Economic Studies, 61(4), 631–654. 10.2307/2297912 (application/pdf).CrossRefGoogle Scholar
  22. Phillips, P. C. B., & Perron, P. (1998). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346. Scholar
  23. Sandoval, J. (2006). Do asymmetric GARCH models fit better exchange rate volatilities on emerging markets? Working Paper, Universidad Externado do Colombia, Odeon (pp. 99–116). Retrieved from
  24. Taylor, S. J. (1986). Modelling Financial Time Series. New York: Wiley.Google Scholar
  25. Theil, H. (1967). Economics and information theory. Chicago: Rand McNally and Company.Google Scholar
  26. Vee, D. N. C., Gonpot, P. N., & Sookia, N. (2011). Forecasting volatility of USD/MUR exchange rate using a GARCH (1,1) model with GED and student’s t-errors. University of Mauritius Research Journal, 17, 171–214.Google Scholar
  27. Yoon, S., & Lee, K. S. (2008). The volatility and asymmetry of won/dollar exchange rate. Journal of Social Sciences, 4(7–9), 2008. jssp.2008.7.9.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Accounting and FinanceWestern Macedonia University of Applied SciencesKozaniGreece

Personalised recommendations