Exchange-Rates Forecasting: Exponential Smoothing Techniques And Arima Models

The exchange rate reflects the ratio at which one currency can be exchanged with another currency, namely the ratio of currency prices. The relevant literature implies, by the purchasing power parity theory, that in the long-run exchange rates converge to an equilibrium level. The question that arises is related to the behavior in the short-term of the exchange rates, and how these fluctuations might affect the financial market players, the investors as well as those directly influenced by changes in the exchange rate In this study we want to highlight the methods for exchange rate forecasting, using the exchange rates of Romanian Leu versus the most important currencies in terms of international trade, namely the Euro, United States Dollar, British Pound, Japanese Yen, Chinese Renminbi and the Russian Ruble. The relevant literature on currency forecasting issues includes a wide range of methods, the majority concentrating on models that are based on the random walk hypothesis in forecasting exchange rates, respectively those based on macroeconomic indicators. (Chinn and Meese:1995), (Mark:1995) (Hwang: 2001) consider the last ones more efficient, while (Meese and Rogof: 1983), (Goldberg and Frydman: 1996) indicate that the models based on the random walk hypothesis are superior. Others, like (Marsh and Power:1996), (Kilian and Taylor: 2001), (Zhang, Simoff and Debenham: 2006) use hybrid models. In the literature both types of models are used, but in the long-term the models based on macroeconomic indicators outperform those based on random walk, while in the short-term a more efficient predictability is achieved by the models based on random walk. To forecast the exchange rates the single exponential smoothing technique, double exponential smoothing technique, Holt -Winters simple exponential smoothing technique, Holt -Winters multiplicative exponential smoothing technique, Holt -Winters additive exponential smoothing technique namely the the autoregressive integrated moving average models were used. The forecasting results were measured by the indicators: Sum of squared errors, Root mean squared error, Mean absolute error, Bias Proportion, Variance Proportion, Covariance Proportion and the Theil Inequality Coefficient. All the results indicate the apreciation of the Romanian Leu against the other currencies. In the case of the first five forecast techniques the results are similar, from the point of view of the forecast coefficients, which points out that the optimal models were found. The exponential smoothing techniques in some cases outperform the ARIMA models, because of the speed eith which they addapt to the smallest changes to the market conditions. In addition, the ARIMA models present some difficulties in estimating and validating the model, are more effective in rendering the medium-term trend, in our case 4 months. So these models show the changes in trend, while the forecasting models based on exponential smoothing techniques are an effective tool for those interested in the evolution of the exchange rate. This work was possible with the financial support of the Sectoral Operational Programme for Human Resources Development 2007-2013, co-financed by the European Social Fund, under the project number POSDRU/107/1.5/S/77946 with the title "Doctorate: an Attractive Research Career ".