References
- Apley, D. W., and J. Zhu. 2020. “Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 82 (4): 1059–1086. doi:10.1111/rssb.12377.
- Bazán-Palomino, W., and D. Winkelried. 2021. “FX Markets’ Reactions to COVID-19: Are They Different?” International Economics 167: 50–58. doi:10.1016/j.inteco.2021.05.006.
- Bianco, M. D., M. Camacho, and G. P. Quiros. 2012. “Short-run Forecasting of the euro-dollar Exchange Rate with Economic Fundamentals.” Journal of International Money and Finance 31 (2): 377–396. doi:10.1016/j.jimonfin.2011.11.018.
- Carriero, A., G. Kapetanios, and M. Marcellino. 2009. “Forecasting Exchange Rates with a Large Bayesian VAR.” International Journal of Forecasting 25 (2): 400–417. doi:10.1016/j.ijforecast.2009.01.007.
- Dupuy, P. 2021. “Risk-adjusted Return Managed Carry Trade”. Journal of Banking & Finance 129: 106172. forthcoming. doi:10.1016/j.jbankfin.2021.106172.
- Filippou, I., D. Rapach, M. P. Taylor, and G. Zhou. 2021 . “Exchange Rate Prediction with Machine Learning and a Smart Carry Portfolio.” Available at SSRN 3455713. doi:10.2139/ssrn.3455713.
- Fuertes, A., K. Phylaktis, and C. Yan. 2019. “Uncovered Equity “Disparity” in Emerging Markets.” Journal of International Money and Finance 98: 102066. doi:10.1016/j.jimonfin.2019.102066.
- Gunay, S. 2021. “Comparing COVID-19 with the GFC: A Shockwave Analysis of Currency Markets.” Research in International Business and Finance 56: 101377. doi:10.1016/j.ribaf.2020.101377.
- Kassi, D. F., G. Sun, N. Ding, D. N. Rathnayake, and, and G. R. Assamoi. 2019. “Asymmetry in Exchange Rate pass-through to Consumer Prices: Evidence from Emerging and Developing Asian Countries”. Economic Analysis and Policy 62: 357–372. doi:10.1016/j.eap.2018.09.013.
- Kilian, L., and M. P. Taylor. 2003. “Why Is It so 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.
- Liang, L., and X. Cai. 2022. “Time-sequencing European Options and Pricing with Deep learning–Analyzing Based on Interpretable ALE Method.” Expert Systems with Applications 187: 115951. doi:10.1016/j.eswa.2021.115951.
- Meese, R. A., and K. Rogoff. 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.
- Molnar, C. 2020. “Interpretable Machine Learning.” Lulu. com.
- Ranjit, S., S. Shrestha, S. Subedi, and S. Shakya. 2018. “Comparison of Algorithms in Foreign Exchange Rate Prediction.” In 2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS), Kathmandu, Nepal (pp. 9–13). doi:10.1109/CCCS.2018.8586826.
- Rossi, B. 2013. “Exchange Rate Predictability.” Journal of Economic Literature 51 (4): 1063–1119. doi:10.1257/jel.51.4.1063.
- Taylor, M. P. 2005. “Official Foreign Exchange Intervention as a Coordinating Signal in the dollar-yen Market.” Pacific Economic Review 10 (1): 73–82. doi:10.1111/j.1468-0106.2005.00261.x.
- Yilmaz, F. M., and O. Arabaci. 2021. “Should Deep Learning Models Be in High Demand, or Should They Simply Be A Very Hot Topic? A Comprehensive Study for Exchange Rate Forecasting.” Computational Economics 57 (1): 217–245. doi:10.1007/s10614-020-10047-9.
- You, Y., and X. Liu. 2020. “Forecasting short-run Exchange Rate Volatility with Monetary Fundamentals: A GARCH-MIDAS Approach.” Journal of Banking & Finance 116: 105849. doi:10.1016/j.jbankfin.2020.105849.
- Zhao, Y., and M. Khushi. 2020. “Wavelet denoised-ResNet CNN and LightGBM Method to Predict Forex Rate of Change.” In 2020 International Conference on Data Mining Workshops (ICDMW), Virtual Conference (pp. 385–391). doi:10.1109/ICDMW51313.2020.00060.