Abstract
The aim of this paper is to investigate the predictive capacity of cryptocurrency on exchange rate returns. The train-test split technique is applied in estimating the ARIMA and ARIMAX models. We also use an ensemble technique to improve our prediction accuracy. The results indicate there exists only a linkage between cryptocurrencies and US dollar. There found no evidence that cryptocurrencies affect the returns of Chinese Yuan and Japanese Yen. According to the RMSE criterion, the ARIMAX with cryptocurrency forecasting model produces superior results over the ARIMA model. This indicates that cryptocurrency can improve forecasting accuracy for exchange rate returns. Furthermore, we found that the ensemble model outperforms the conventional ARIMA and ARIMAX forecasting models.
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References
Wong, H.T.: Real exchange rate returns and real stock price returns. Int. Rev. Econ. Finance 49, 340–352 (2017)
Xie, Z., Chen, S.W., Wu, A.C.: The foreign exchange and stock market nexus: new international evidence. Int. Rev. Econ. Finance 67, 240–266 (2020)
Ding, L.: Conditional correlation between exchange rates and stock prices. Q. Rev. Econ. Finance 80, 452–463 (2021)
Dai, X., Wang, Q., Zha, D., Zhou, D.: Multi-scale dependence structure and risk contagion between oil, gold, and US exchange rate: a wavelet-based vine-copula approach. Energy Econ. 88, 104774 (2020)
Chkir, I., Guesmi, K., Brayek, A.B., Naoui, K.: Modelling the nonlinear relationship between oil prices, stock markets, and exchange rates in oil-exporting and oil-importing countries. Res. Int. Bus. Financ. 54, 101274 (2020)
Wu, T., An, F., Gao, X., Wang, Z.: Hidden causality between oil prices and exchange rates. Resour. Policy 82, 103512 (2023)
Ferraro, D., Rogoff, K., Rossi, B.: Can oil prices forecast exchange rates? An empirical analysis of the relationship between commodity prices and exchange rates. J. Int. Money Financ. 54, 116–141 (2015)
Zhang, H.J., Dufour, J.M., Galbraith, J.W.: Exchange rates and commodity prices: measuring causality at multiple horizons. J. Empir. Financ. 36, 100–120 (2016)
Liu, L., Tan, S., Wang, Y.: Can commodity prices forecast exchange rates? Energy Econ. 87, 104719 (2020)
Apergis, N.: Can gold prices forecast the Australian dollar movements? Int. Rev. Econ. Finance 29, 75–82 (2014)
Rehman, M.U., Apergis, N.: Determining the predictive power between cryptocurrencies and real time commodity futures: evidence from quantile causality tests. Resour. Policy 61, 603–616 (2019)
Isah, K.O., Raheem, I.D.: The hidden predictive power of cryptocurrencies and QE: evidence from US stock market. Phys. A 536, 121032 (2019)
Shang, Y., Wei, Y., Chen, Y.: Cryptocurrency policy uncertainty and gold return forecasting: a dynamic Occam’s window approach. Financ. Res. Lett. 50, 103251 (2022)
Wei, Y., Wang, Y., Lucey, B.M., Vigne, S.A.: Cryptocurrency uncertainty and volatility forecasting of precious metal futures markets. J. Commod. Mark. 29, 100305 (2023)
Feng, W., Zhang, Z.: Currency exchange rate predictability: the new power of Bitcoin prices. J. Int. Money Financ. 132, 102811 (2023)
Baur, D.G., Dimpfl, T., Kuck, K.: Bitcoin, gold and the US dollar-A replication and extension. Financ. Res. Lett. 25, 103–110 (2018)
Mokni, K., Ajmi, A.N.: Cryptocurrencies vs. US dollar: evidence from causality in quantiles analysis. Econ. Anal. Policy 69, 238–252 (2021)
Wang, P., Liu, X., Wu, S.: Dynamic linkage between bitcoin and traditional financial assets: a comparative analysis of different time frequencies. Entropy 24(11), 1565 (2022)
He, K., Yang, Q., Ji, L., Pan, J., Zou, Y.: Financial time series forecasting with the deep learning ensemble model. Mathematics 11(4), 1054 (2023)
Hajek, P., Hikkerova, L., Sahut, J.M.: How well do investor sentiment and ensemble learning predict Bitcoin prices? Res. Int. Bus. Financ. 64, 101836 (2023)
Wang, G., Tao, T., Ma, J., Li, H., Fu, H., Chu, Y.: An improved ensemble learning method for exchange rate forecasting based on complementary effect of shallow and deep features. Expert Syst. Appl. 184, 115569 (2021)
Sun, S., Wang, S., Wei, Y.: A new ensemble deep learning approach for exchange rates forecasting and trading. Adv. Eng. Inform. 46, 101160 (2020)
Young, J.: No Direct Correlation Between Chinese Yuan & Bitcoin Price, Here’s Why. Cointelegraph, 17 April 2017. https://cointelegraph.com/news/no-direct-correlation-between-chinese-yuan-bitcoin-price-heres-why. Accessed 31 May 2023
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This research work was partially supported by Chiang Mai University and Center of Excellence in Econometrics, Faculty of Economics, Chiang Mai University
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Khiewngamdee, C., Chanaim, S. (2023). Does Cryptocurrency Improve Forecasting Performance of Exchange Rate Returns?. In: Huynh, VN., Le, B., Honda, K., Inuiguchi, M., Kohda, Y. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2023. Lecture Notes in Computer Science(), vol 14375. Springer, Cham. https://doi.org/10.1007/978-3-031-46775-2_25
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