1. Home
  2. Vol 6, No 3 (2022)
  3. Khoa

Predicting Exchange Rate under UIRP Framework with Support Vector Regression

Bui Thanh Khoa, Tran Trong Huynh

Abstract


This study aimed to forecast the exchange rate between the Vietnamese dong and the US dollar for the following month in the context of the COVID-19 pandemic. It used the Support Vector Regression (SVR) algorithm under the Uncovered Interest Rate Parity (UIRP) theoretical framework; the results are compared with the Ordinary Least Square (OLS) regression model and the Random Walk (RW) model under the rolling window method. The data included the VND/USD exchange rate, the bank interest rate for the 1-month term, and the 1-month T-bill from January 01, 2020, to September 11, 2021. The research discovered a linear link between the two nations' exchange rates and interest rate differentials. Interest rate differentials are input variables to forecast interest rate differentials. Furthermore, the connection between the exchange rate and interest rate differentials during this era does not support the UIRP hypothesis; hence, the error for OLS predictions remains large. The study provided a model to forecast future exchange rates by combining the UIRP theoretical framework and the SVR algorithm. The UIRP theoretical framework can anticipate exchange rate differentials using the input variable and the interest rates between two nations. Meanwhile, the SVR algorithm is a robust machine learning technique that enhances prediction accuracy.

 

Doi: 10.28991/ESJ-2022-06-03-014

Full Text: PDF


Keywords


Exchange Rate; UIRP; SVR; Predicting; Machine Learning.

References


Ismailov, A., & Rossi, B. (2018). Uncertainty and deviations from uncovered interest rate parity. Journal of International Money and Finance, 88, 242–259. doi:10.1016/j.jimonfin.2017.07.012.

Firoj, M., & Khanom, S. (2018). Efficient market hypothesis: Foreign exchange market of Bangladesh. International Journal of Economics and Financial Issues, 8(6), 99.

Engel, C., Lee, D., Liu, C., Liu, C., & Wu, S. P. Y. (2019). The uncovered interest parity puzzle, exchange rate forecasting, and Taylor rules. Journal of International Money and Finance, 95, 317–331. doi:10.1016/j.jimonfin.2018.03.008.

Huynh, T. T. (2020). Application of Machine Learning in CAPM. Master Degree, Faculty of Finance, University of Economics Ho Chi Minh City, Ho Chi Minh City, Vietnam.

Vuong, N. B., Suzuki, Y., & To, A. T. (2021). The local impact on the concurrent sentiment-return nexus: Asian versus European markets. Emerging Science Journal, 5(6), 894–905. doi:10.28991/esj-2021-01318.

Fernandes, M. (1998). Non-linearity and exchange rates. Journal of Forecasting, 17(7), 497–514. doi:10.1002/(SICI)1099-131X(199812)17:7<497::AID-FOR677>3.0.CO;2-N.

Kilian, L., & Taylor, M. P. (2003). Why is it difficult to beat the random walk forecast of exchange rates? Journal of International Economics, 60(1), 85–107. doi:10.1016/S0022-1996(02)00060-0.

Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4), 987. doi:10.2307/1912773.

Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. doi:10.1016/0304-4076(86)90063-1.

Ding, Z., & Granger, C. W. J. (1996). Modeling volatility persistence of speculative returns: A new approach. Journal of Econometrics, 73(1), 185–215. doi:10.1016/0304-4076(95)01737-2.

Ding, Z., Granger, C. W. J., & Engle, R. F. (1993). A long memory property of stock market returns and a new model. Journal of Empirical Finance, 1(1), 83–106. doi:10.1016/0927-5398(93)90006-D.

Cheung, Y. W., Chinn, M. D., & Pascual, A. G. (2005). Empirical exchange rate models of the nineties: Are any fit to survive? Journal of International Money and Finance, 24(7), 1150–1175. doi:10.1016/j.jimonfin.2005.08.002.

Chen, A. S., & Leung, M. T. (2004). Regression neural network for error correction in foreign exchange forecasting and trading. Computers and Operations Research, 31(7), 1049–1068. doi:10.1016/S0305-0548(03)00064-9.

Vojinovic, Z., Kecman, V., & Seidel, R. (2001). A data mining approach to financial time series modelling and forecasting. International Journal of Intelligent Systems in Accounting, Finance & Management, 10(4), 225–239. doi:10.1002/isaf.207.

Khoa, B. T., & Huynh, T. T. (2021). Is It Possible to Earn Abnormal Return in an Inefficient Market? An Approach Based on Machine Learning in Stock Trading. Computational Intelligence and Neuroscience 2021, 1–14. doi:10.1155/2021/2917577.

Lisi, F., & Schiavo, R. A. (1999). A comparison between neural networks and chaotic models for exchange rate prediction. Computational Statistics and Data Analysis, 30(1), 87–102. doi:10.1016/S0167-9473(98)00067-X.

Nag, A. K., & Mitra, A. (2002). Forecasting daily foreign exchange rates using genetically optimized neural networks. Journal of Forecasting, 21(7), 501–511. doi:10.1002/for.838.

Vapnik, V. (2013).The Nature of Statistical Learning Theory. Statistics for engineering and Information Science, Springer, New York, United States. doi:10.1007/978-1-4757-3264-1.

Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297. doi:10.1007/BF00994018.

Ince, H., & Trafalis, T. B. (2008). Short term forecasting with support vector machines and application to stock price prediction. International Journal of General Systems, 37(6), 677–687. doi:10.1080/03081070601068595.

Trafalis, T. B., & Ince, H. (2000, July). Support vector machine for regression and applications to financial forecasting. In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000, Como, Italy. Neural Computing: New Challenges and Perspectives for the New Millennium 6, 348-353. doi:10.1109/ijcnn.2000.859420.

Kim, D. (2021). On the invalidity of the ordinary least squares estimate of the equilibrium climate sensitivity. Theoretical and Applied Climatology, 146(1–2), 21–27. doi:10.1007/s00704-021-03719-5.

Bočková, K., Škrabánková, J., & Hanák, M. (2021). Theory and practice of neuromarketing: Analyzing human behavior in relation to markets. Emerging Science Journal, 5(1), 44–56. doi:10.28991/esj-2021-01256.

Fang, M., & Taylor, S. (2021). A machine learning based asset pricing factor model comparison on anomaly portfolios. Economics Letters, 204, 109919. doi:10.1016/j.econlet.2021.109919.

Khoa, B. T., Son, P. T., & Huynh, T. T. (2021). The Relationship between the Rate of Return and Risk in Fama-French Five-Factor Model: A Machine Learning Algorithms Approach. Journal of System and Management Sciences, 11(4), 47–64. doi:10.33168/jsms.2021.0403.

Rossi, B. (2013). Exchange rate predictability. Journal of economic literature, 51(4), 1063-1119. doi:10.1257/jel.51.4.1063.

Meese, R. A., & Rogoff, K. (1983). Empirical exchange rate models of the seventies. Do they fit out of sample? Journal of International Economics, 14(1–2), 3–24. doi:10.1016/0022-1996(83)90017-X.

Alquist, R., & Chinn, M. D. (2008). Conventional and unconventional approaches to exchange rate modelling and assessment. International Journal of Finance and Economics, 13(1), 2–13. doi:10.1002/ijfe.354.

Chinn, M. D., & Quayyum, S. (2012). Long horizon uncovered interest parity re-assessed, Working paper 18482. National Bureau of economic research, Massachusetts, United States. doi: 10.3386/w18482.

Clark, T. E., & West, K. D. (2006). Using out-of-sample mean squared prediction errors to test the martingale difference hypothesis. Journal of Econometrics, 135(1–2), 155–186. doi:10.1016/j.jeconom.2005.07.014.

Wolfe, P. (1961). A duality theorem for non-linear programming. Quarterly of Applied Mathematics, 19(3), 239–244. doi:10.1090/qam/135625.

Khoa, B. T., & Huynh, T. T. (2021). Support Vector Regression Algorithm under in the CAPM Framework. 2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021, Sakheer, Bahrain, 186–190. doi:10.1109/ICDABI53623.2021.9655797.

Qi, M., & Wu, Y. (2006). Technical Trading-Rule Profitability, Data Snooping, and Reality Check: Evidence from the Foreign Exchange Market. Journal of Money, Credit, and Banking, 38(8), 2135–2158. doi:10.1353/mcb.2007.0006.

Ahmed, S., Liu, X., & Valente, G. (2016). Can currency-based risk factors help forecast exchange rates? International Journal of Forecasting, 32(1), 75–97. doi:10.1016/j.ijforecast.2015.01.010.

Beckmann, J., & Schüssler, R. (2016). Forecasting exchange rates under parameter and model uncertainty. Journal of International Money and Finance, 60, 267–288. doi:10.1016/j.jimonfin.2015.07.001.

Arman, K. N., Teh, Y. W., & David, N. C. L. (2013). A novel FOREX prediction methodology based on fundamental data. African Journal of Business Management, 5(20), 8322–8330. doi:10.5897/ajbm11.798.

Mark, N. C. (1995). Exchange Rates and Fundamentals: Evidence on Long-Horizon Predictability. American Economic Review, 201–218.

Balke, N. S., Ma, J., & Wohar, M. E. (2013). The contribution of economic fundamentals to movements in exchange rates. Journal of International Economics, 90(1), 1–16. doi:10.1016/j.jinteco.2012.10.003.

Engel, C., Mark, N. C., & West, K. D. (2015). Factor Model Forecasts of Exchange Rates. Econometric Reviews, 34(1–2), 32–55. doi:10.1080/07474938.2014.944467.

Dabrowski, M. A., Papiez, M., & Śmiech, S. (2014). Exchange rates and monetary fundamentals in CEE countries: Evidence from a panel approach. Journal of Macroeconomics, 41, 148–159. doi:10.1016/j.jmacro.2014.05.005.

Hsu, M. W., Lessmann, S., Sung, M. C., Ma, T., & Johnson, J. E. V. (2016). Bridging the divide in financial market forecasting: machine learners vs. financial economists. Expert Systems with Applications, 61, 215–234. doi:10.1016/j.eswa.2016.05.033.

Cavalcante, R. C., Brasileiro, R. C., Souza, V. L. F., Nobrega, J. P., & Oliveira, A. L. I. (2016). Computational Intelligence and Financial Markets: A Survey and Future Directions. Expert Systems with Applications, 55, 194–211. doi:10.1016/j.eswa.2016.02.006.

Barboza, F., Kimura, H., & Altman, E. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83, 405–417. doi:10.1016/j.eswa.2017.04.006.

Sermpinis, G., Stasinakis, C., Theofilatos, K., & Karathanasopoulos, A. (2015). Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms - Support vector regression forecast combinations. European Journal of Operational Research, 247(3), 831–846. doi:10.1016/j.ejor.2015.06.052.

Jubert de Almeida, B., Ferreira Neves, R., & Horta, N. (2018). Combining Support Vector Machine with Genetic Algorithms to optimize investments in Forex markets with high leverage. Applied Soft Computing Journal, 64, 596–613. doi:10.1016/j.asoc.2017.12.047.

White, H. (2000). A reality check for data snooping. Econometrica, 68(5), 1097–1126. doi:10.1111/1468-0262.00152.

Caginalp, G., & Desantis, M. (2011). Nonlinearity in the dynamics of financial markets. Nonlinear Analysis: Real World Applications, 12(2), 1140–1151. doi:10.1016/j.nonrwa.2010.09.008.

Engle, R. F., & Granger, C. W. (1987). Co-integration and error correction: representation, estimation, and testing. Econometrica: Journal of the Econometric Society, 251-276. doi:10.2307/1913236.

Ince, H., & Trafalis, T. B. (2006). A hybrid model for exchange rate prediction. Decision Support Systems, 42(2), 1054–1062. doi:10.1016/j.dss.2005.09.001.

Lu, C. J., Lee, T. S., & Chiu, C. C. (2009). Financial time series forecasting using independent component analysis and support vector regression. Decision Support Systems, 47(2), 115–125. doi:10.1016/j.dss.2009.02.001.

Yaohao, P., & Albuquerque, P. H. M. (2019). Non-Linear Interactions and Exchange Rate Prediction: Empirical Evidence Using Support Vector Regression. Applied Mathematical Finance, 26(1), 69–100. doi:10.1080/1350486X.2019.1593866.


Full Text: PDF

DOI: 10.28991/ESJ-2022-06-03-014

Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 Bui Thanh Khoa