Abstract
The objective of this paper is to see whether non-linear MSBVAR provides a better framework than the linear BVAR model when applied to the forecasting problem. In this study, we propose to forecast the monthly exchange rate of five currencies in the main basket, consisting of USD EUR JPY GBP and CNY, with these two models and compare the performance of the models along the forecast horizons. Moreover, three different prior distributions, namely Normal-Wishart, Normal-Uniform, and Uniform-Uniform, are considered in the Bayesian framework, thus two models with three priors are compared in our empirical forecasting problem. The out-of-sample forecast is conducted and the RMSE and MAE are used as the criteria. We find that linear BVAR with Uniform-Uniform provides the better forecasts of all currencies than do the MSBVAR model for both short and long forecast horizons.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abbate, A., Massimiliano, M.: Modelling and forecasting exchange rates with time-varying parameter models. J. Econ. Lit. (2014)
Andersson, M.K., Karlsson, S.: Bayesian forecast combination for VAR models. In: Bayesian Econometrics, pp. 501–524. Emerald Group Publishing Limited (2008)
Boothe, P., Glassman, D.: Comparing exchange rate forecasting models: accuracy versus profitability. Int. J. Forecast. 3(1), 65–79 (1987)
Ghalayini, L.: Modeling and forecasting the US dollar/euro exchange rate. Int. J. Econ. Financ. 6(1), 194 (2013)
Goodman, S.N.: Toward evidence-based medical statistics. 1: The P value fallacy. Ann. Internal Med. 130(12), 995–1004 (1999)
Gupta, R., Kotze, K.: The role of oil prices in the forecasts of South African interest rates: a Bayesian approach. Energy Econ. 61, 270–278 (2017)
Hamilton, J.D.: A new approach to the economic analysis of nonstationary time series and the business cycle. Econom. J. Econom. Soc. 57, 357–384 (1989)
Karlsson, S.: Forecasting with Bayesian vector autoregression. In: Handbook of Economic Forecasting, vol. 2, pp. 791–897. Elsevier (2013)
Litterman, R.B.: Forecasting with Bayesian vector autoregressions-five years of experience. J. Bus. Econ. Stat. 4(1), 25–38 (1986)
Meese, R.A., Rogoff, K.: Empirical exchange rate models of the seventies: do they fit out of sample? J. Int. Econ. 14(1–2), 3–24 (1983)
Osathanunkul, R., Khiewngamdee, C., Yamaka, W., Sriboonchitta, S.: The role of oil price in the forecasts of agricultural commodity prices. In: International Conference of the Thailand Econometrics Society, pp. 422-429. Springer, Cham (2018)
Pastpipatkul, P., Yamaka, W., Wiboonpongse, A., Sriboonchitta, S.: Spillovers of quantitative easing on financial markets of Thailand, Indonesia, and the Philippines. In: International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, pp. 374–388. Springer (2015)
Sims, C.: Macroeconomics and reality. Econometrica 48, 1–48 (1980). Mathematical Reviews (MathSciNet): MR559536 Digital Object Identifier: https://doi.org/10.1080/00031305.1980.10482711
Sims, C.A., Zha, T.: Bayesian methods for dynamic multivariate models. Int. Econ. Rev. 39, 949–968 (1998)
Sims, C.A., Waggoner, D.F., Zha, T.: Methods for inference in large multiple-equation Markov-switching models. J. Econom. 146(2), 255–274 (2008)
Spencer, D.E.: Developing a Bayesian vector autoregression forecasting model. Int. J. Forecast. 9(3), 407–421 (1993)
Tungtrakul, T., Maneejuk, P., Sriboonchitta, S.: Macroeconomic factors affecting exchange rate fluctuation: Markov switching Bayesian quantile approach. Thai J. Math. 117–132 (2016)
Zivot, E., Wang, J.: Modeling financial time series with S-Plus®, vol. 191. Springer (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Phadkantha, R., Yamaka, W., Sriboonchitta, S. (2019). Forecasting Exchange Rate with Linear and Non-linear Vector Autoregressive. In: Kreinovich, V., Sriboonchitta, S. (eds) Structural Changes and their Econometric Modeling. TES 2019. Studies in Computational Intelligence, vol 808. Springer, Cham. https://doi.org/10.1007/978-3-030-04263-9_42
Download citation
DOI: https://doi.org/10.1007/978-3-030-04263-9_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04262-2
Online ISBN: 978-3-030-04263-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)