A KELM-Based Ensemble Learning Approach for Exchange Rate Forecasting

References

[1] Goldberg P K, Knetter M M. Goods prices and exchange rates: What have we learned?. Journal of Economic Literature, 1997, 35: 1243–1272.10.3386/w5862Search in Google Scholar

[2] Campa J M, Goldberg L S. Exchange rate pass-through into import prices: A macro or micro phenomenon?. National Bureau of Economic Research, 2002.10.3386/w8934Search in Google Scholar

[3] Campa J M, Goldberg L S. Exchange-rate pass-through into import prices. Review of Economics and Statistics, 2005, 87(4): 679–690.10.1162/003465305775098189Search in Google Scholar

[4] Jiang J, Kim D. Exchange rate pass-through to inflation in China. Economic Modelling, 2013, 33: 900–912.10.1016/j.econmod.2013.05.021Search in Google Scholar

[5] Abbas G, Bhowmik R, Koju L, et al. Cointegration and causality relationship between stock market, money market and foreign exchange market in Pakistan. Journal of Systems Science and Information, 2017, 5(1): 1–20.10.21078/JSSI-2017-001-20Search in Google Scholar

[6] Abdalla I S A, Murinde V. Exchange rate and stock price interactions in emerging financial markets: Evidence on India, Korea, Pakistan and the Philippines. Applied Financial Economics, 1997, 7(1): 25–35.10.1080/096031097333826Search in Google Scholar

[7] Granger C W J, Huangb B N, Yang C W. A bivariate causality between stock prices and exchange rates: Evidence from recent Asian flu. The Quarterly Review of Economics and Finance, 2000, 40(3): 337–354.10.1016/S1062-9769(00)00042-9Search in Google Scholar

[8] Aydemir O, Demirhan B. The relationship between stock prices and exchange rates: Evidence from MENA countries. Handbook of Research on Global Enterprise Operations and Opportunities. IGI Global, 2017: 171–185.10.4018/978-1-5225-2245-4.ch011Search in Google Scholar

[9] Mccarthy J. Pass-through of exchange rates and import prices to domestic inflation in some industrialized economies. Staff Reports No. 111, Federal Reserve Bank of New York, 2000.10.2139/ssrn.249576Search in Google Scholar

[10] Ito T, Sato K. Exchange rate changes and inflation in post-crisis Asian economies: Vector autoregression analysis of the exchange rate pass-through. Journal of Money, Credit and Banking, 2008, 40(7): 1407–1438.10.1111/j.1538-4616.2008.00165.xSearch in Google Scholar

[11] Lin S, Ye H. Does inflation targeting make a difference in developing countries?. Journal of Development Economics, 2009, 89(1): 118–123.10.1016/j.jdeveco.2008.04.006Search in Google Scholar

[12] Romero F P. How does the Philippine peso exchange rate respond to inflation, industrial production and trade balance? A Granger causality analysis. Advanced Science Letters, 2017, 23(1): 272–276.10.1166/asl.2017.7155Search in Google Scholar

[13] Bini-Smaghi L. Exchange rate variability and trade: Why is it so difficult to find any empirical relationship?. Applied Economics, 1991, 23(5): 927–936.10.1080/00036849100000041Search in Google Scholar

[14] Chowdhury A R. Does exchange rate volatility depress trade flows? Evidence from error-correction models. The Review of Economics and Statistics, 1993: 700–706.10.2307/2110025Search in Google Scholar

[15] Doganlar M. Estimating the impact of exchange rate volatility on exports: Evidence from Asian countries. Applied Economics Letters, 2002, 9(13): 859–863.10.1080/13504850210150906Search in Google Scholar

[16] Eichengreen M B J, Mathieson M D J. The currency composition of foreign exchange reserves: Retrospect and prospect. International Monetary Fund, 2000.10.1093/acprof:oso/9780199245314.003.0010Search in Google Scholar

[17] Jiang J, Kim D. Exchange rate pass-through to inflation in China. Economic Modelling, 2013, 33: 900–912.10.1016/j.econmod.2013.05.021Search in Google Scholar

[18] Bhattacharya R, Patnaik H, Shah A. Monetary policy transmission in an emerging market setting. IMF Working Paper No. 11/5, International Monetary Fund, Washington D.C., 2011.10.5089/9781455211838.001Search in Google Scholar

[19] Chortareas G, Jiang Y, Nankervis J C. Forecasting exchange rate volatility using high-frequency data: Is the euro different?. International Journal of Forecasting, 2011, 27(4): 1089–1107.10.1016/j.ijforecast.2010.07.003Search in Google Scholar

[20] Moosa I A, Vaz J J. Cointegration, error correction and exchange rate forecasting. Journal of International Financial Markets, Institutions and Money, 2016, 44: 21–34.10.1016/j.intfin.2016.04.007Search in Google Scholar

[21] McCrae M, Lin Y X, Pavlik D, et al. Can cointegration-based forecasting outperform univariate models? An application to Asian exchange rates. Journal of Forecasting, 2002, 21(5): 355–380.10.1002/for.824Search in Google Scholar

[22] Joseph N L. Model specification and forecasting foreign exchange rates with vector autoregressions. Journal of Forecasting, 2001, 20(7): 451–484.10.1002/for.808Search in Google Scholar

[23] Roni B, Wu C, Jewel R K, et al. A Study on the volatility of the Bangladesh stock marketbased on GARCH type models. Journal of Systems Science and Information, 2017, 5(3): 193–215.10.21078/JSSI-2017-193-23Search in Google Scholar

[24] Hu M Y, Zhang G P, Jiang C X, et al. A Cross-Validation analysis of neural network out-of-sample performance in exchange rate forecasting. Decision Sciences, 1999, 30(1): 197–216.10.1111/j.1540-5915.1999.tb01606.xSearch in Google Scholar

[25] Raji C G, Chandra S S V. Long-term forecasting the survival in liver transplantation using multilayer perceptron networks. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017, 47(8): 2318–2329.10.1109/TSMC.2017.2661996Search in Google Scholar

[26] Li X, Zhang Z, Huang C. An EPC forecasting method for stock index based on integrating empirical mode decomposition, SVM and Cuckoo search algorithm. Journal of Systems Science and Information, 2014, 2(6): 481–504.10.1515/JSSI-2014-0481Search in Google Scholar

[27] Sermpinis G, Stasinakis C, Theofilatos K, et al. Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms — Support vector regression forecast combinations. European Journal of Operational Research, 2015, 247(3): 831–846.10.1016/j.ejor.2015.06.052Search in Google Scholar

[28] Shen F, Chao J, Zhao J. Forecasting exchange rate using deep belief networks and conjugate gradient method. Neurocomputing, 2015, 167: 243–253.10.1016/j.neucom.2015.04.071Search in Google Scholar

[29] Yu L, Lai K K, Wang S. Multistage RBF neural network ensemble learning for exchange rates forecasting. Neurocomputing, 2008, 71(16): 3295–3302.10.1016/j.neucom.2008.04.029Search in Google Scholar

[30] Yu L, Wang S, Lai K K. Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm. Energy Economics, 2008, 30(5): 2623–2635.10.1016/j.eneco.2008.05.003Search in Google Scholar

[31] Sun S L, Qiao H, Wei Y J, et al. A new dynamic integrated approach for wind speed forecasting in China. Applied Energy, 2017, 197: 151–162.10.1016/j.apenergy.2017.04.008Search in Google Scholar

[32] Granger C. Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 1969, 37(3): 424–438.10.2307/1912791Search in Google Scholar

[33] Tan X, Yang Y, Deng J. Grey relational analysis factors in hypertensive with cardiac insufficiency. Journal of Grey System, 1998, 10(1): 75–80.Search in Google Scholar

[34] Kao P S, Hocheng H. Optimization of electrochemical polishing of stainless steel by grey relational analysis. Journal of Materials Processing Technology, 2003, 140(1): 255–259.10.1016/S0924-0136(03)00747-7Search in Google Scholar

[35] Huang G B. An insight into extreme learning machines: Random neurons, random features and kernels. Cognitive Computation, 2014, 6(3): 376–390.10.1007/s12559-014-9255-2Search in Google Scholar

[36] Zhou Z, Xu R, Wu D, et al. Illumination correction of dyeing products based on Grey-Edge and kernel extreme learning machine. Optik — International Journal for Light and Electron Optics, 2016, 127(19): 7978–7985.10.1016/j.ijleo.2016.05.108Search in Google Scholar

[37] Shamshirband S, Mohammadi K, Chen H L, et al. Daily global solar radiation prediction from air temperatures using kernel extreme learning machine: A case study for Iran. Journal of Atmospheric and Solar-Terrestrial Physics, 2015, 134: 109–117.10.1016/j.jastp.2015.09.014Search in Google Scholar

[38] Sun S L, Wang S Y, Wei Y J, et al. A clustering-based nonlinear ensemble approach for exchange rates forecasting. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018, 10.1109/TSMC.2018.2799869.Search in Google Scholar

[39] Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: Theory and applications. Neurocomputing, 2006, 70(1–3): 489–501.10.1016/j.neucom.2005.12.126Search in Google Scholar