Quel avenir du dinar tunisien face à l'euro ? Prévision avec le modèle ARIMA

GRITLI, Mohamed Ilyes (2018): Quel avenir du dinar tunisien face à l'euro ? Prévision avec le modèle ARIMA.

Abstract

Summary: The European Union absorbs nearly 75% of Tunisian exports and represents about 50% of Tunisian imports, which explains the important weight of the euro in the Tunisian dinar anchor basket. Thus, the purpose of this article is to predict short-term exchange rate fluctuations EUR/TND, using the ARIMA model (0,1,1). The results show that a unit of euro will be exchanged for 3.05126 dinars (model without break) and for 3.22409 dinars (model with rupture), by October 2018. This suggests that the degree of depreciation of the dinar will depend on the policy pursued by the Central Bank of Tunisia.

• Item Type: MPRA Paper
• English Title: What future of the Tunisian dinar against the euro? Prediction with the ARIMA model
• Language: French
• Keywords: Mots clés : EUR, DNT, taux de change, prévision, ARIMA
• Subjects: C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; F - International Economics > F3 - International Finance > F31 - Foreign Exchange

References

Adebiyi, A. A., Adewumi, A. O., & Ayo, C. K. (2014). Comparison of ARIMA and artificial neural networks models for stock price prediction. Journal of Applied Mathematics, 2014.

AKAIKE, H. (1973). Information Theory as an extension of the maximum likelihood principle, in: B.N. Petrov, F. Csáki (Eds.), Proceedings of the 2nd International Symposium on Information Theory.

Bassil, C. (2012). Interaction entre racines unitaires et ruptures structurelles. Revue économique, 63(1), 93-128.

Box, G. E., & Jenkins, G. M. (1976). Time series analysis: forecasting and control, revised ed. Holden-Day.

Charfi, F. M. (2009). Euro/dollar: quelle stratégie de change pour la Tunisie?. Revue de l'OFCE, (1), 85-114.

de Oliveira Neves, F., Salgado, E. G., & Beijo, L. A. (2017). Analysis of the Environmental Management System based on ISO 14001 on the American continent. Journal of Environmental Management, 199, 251-262.

Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427-431.

Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 987-1007.

International Monetary Fund IMF (2017). Washington, D.C, Country Report No. 17/203.

Lardic, S., & Priso, A. M. (1999). Une comparaison des prévisions des experts à celles issues des modèles B VAR. Économie & prévision, 140(4), 161-180.

Lahmiri, S. (2017). Modeling and predicting historical volatility in exchange rate markets. Physica A: Statistical Mechanics and its Applications, 471, 387-395.

Lasheras, F. S., de Cos Juez, F. J., Sánchez, A. S., Krzemień, A., & Fernández, P. R. (2015). Forecasting the COMEX copper spot price by means of neural networks and ARIMA models. Resources Policy, 45, 37-43.

Lee, C. K., Sehwan, Y., & Jongdae, J. (2007). Neural network model versus SARIMA model in forecasting Korean stock price index (KOSPI). Issues in Information System, 8(2), 372-378.

Ljung, G. M., & Box, G. E. (1978). On a measure of lack of fit in time series models. Biometrika, 65(2), 297-303.

MacKinnon, J. G. (1996). Numerical distribution functions for unit root and cointegration tests. Journal of applied econometrics, 601-618.

Mills, T. C., & Markellos, R. N. (2008). The econometric modelling of financial time series. Cambridge University Press.

Phillips, P. C., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335-346.

Putro, S. P., Koshio, S., & Oktaferdian, V. (2016). Implementation of ARIMA Model to Asses Seasonal Variability Macrobenthic Assemblages. Aquatic Procedia, 7, 277-284.

Ramos, P., Santos, N., & Rebelo, R. (2015). Performance of state space and ARIMA models for consumer retail sales forecasting. Robotics and computer-integrated manufacturing, 34, 151-163.

Ramsey, J. B. (1969). Tests for specification errors in classical linear least-squares regression analysis. Journal of the Royal Statistical Society. Series B (Methodological), 350-371.

Ruby-Figueroa, R., Saavedra, J., Bahamonde, N., & Cassano, A. (2017). Permeate flux prediction in the ultrafiltration of fruit juices by ARIMA models. Journal of Membrane Science, 524, 108-116.

Schwarz, G. (1978). Estimating the dimension of a model. The annals of statistics, 6(2), 461-464.

Sen, P., Roy, M., & Pal, P. (2016). Application of ARIMA for forecasting energy consumption and GHG emission: A case study of an Indian pig iron manufacturing organization. Energy, 116, 1031-1038.

Theil, H. (1966). Applied Economic Forecasting. Chicago, Rand McNally.

Theil, H. (1958). Economic forecasts and policy.

Zivot, E., & Andrews, D. W. (1992). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business & Economic Statistics, 10 (3), 251–270.