3,604
Views
2
CrossRef citations to date
0
Altmetric
Research Article

Forecasting cryptocurrencies’ price with the financial stress index: a graph neural network prediction strategy

, , &

References

  • Ahelegbey, D. F., M. Billio, and R. Casarin. 2016. “Bayesian Graphical Models for Structural Vector Autoregressive Processes.” Journal of Applied Econometrics 31 (2): 357–386. doi:10.1002/jae.2443.
  • Ahelegbey, D. F., P. Giudici, and F. Mojtahedi. 2021. “Tail Risk Measurement in Crypto Assets Markets.” International Review of Financial Analysis 73: 101604. doi:10.1016/j.irfa.2020.101604.
  • Billio, M., M. Getmansky, A. W. Lo, and L. Pelizzon. 2012. “Econometric Measures of Connectedness and Systemic Risk in the Finance and Insurance Sectors.” Journal of Financial Economics 104 (3): 535–559. doi:10.1016/j.jfineco.2011.12.010.
  • Bouri, E., R. Gupta, C. K. M. Lau, and D. Roubaud. 2021. “Risk Aversion and Bitcoin Returns in Extreme Quantiles.” Economics Bulletin 41 (3): 1374–1386.
  • Bouri, E., R. Gupta, C. K. M. Lau, D. Roubaud, and S. Wang. 2018. “Bitcoin and Global Financial Stress: A Copula-Based Approach to Dependence and Causality in the Quantiles.” The Quarterly Review of Economics and Finance 69: 297–307. doi:10.1016/j.qref.2018.04.003.
  • Cheng, D. W., F. Z. Yang, S. Xiang, and J. Liu. 2022. “Financial Time Series Forecasting with Multi-Modality Graph Neural Network.” Pattern Recognition 121: 108218. doi:10.1016/j.patcog.2021.108218.
  • Diebold, F. X., and K. Yilmaz. 2009. “Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets.” The Economic Journal 119 (534): 158–171. doi:10.1111/j.1468-0297.2008.02208.x.
  • Giudici, P., and I. Abu-Hashish. 2019. “What Determines Bitcoin Exchange Prices? A Network VAR Approach.” Finance Research Letters 28: 309–318. doi:10.1016/j.frl.2018.05.013.
  • Giudici, P., and P. Pagnottoni. 2019. “High Frequency Price Change Spillovers in Bitcoin Exchange Markets.” Risks 7 (4): 111. doi:10.3390/risks7040111.
  • Giudici, P., and P. Pagnottoni. 2020. “Vector Error Correction Models to Measure Connectedness of Bitcoin Exchange Markets.” Applied Stochastic Models in Business and Industry 36 (1): 95–109. doi:10.1002/asmb.2478.
  • Giudici, P., and G. Polinesi. 2021. “Crypto Price Discovery Through Correlation Networks.” Annals of Operations Research 299 (1–2): 443–457. doi:10.1007/s10479-019-03282-3.
  • Kim, T., and H. Y. Kim. 2019. “Forecasting Stock Prices with a Feature Fusion LSTM-CNN Model Using Different Representations of the Same Data.” PLOS One 14 (2): e0212320. doi:10.1371/journal.pone.0212320.
  • Lewenfus, G., W. A. Martins, S. Chatzinotas, and B. Ottersten. 2020. “Joint Forecasting and Interpolation of Time-Varying Graph Signals Using Deep Learning.” IEEE Transactions on Signal and Information Processing Over Networks 6: 761–773. doi:10.1109/TSIPN.2020.3040042.
  • Liu, Q. F., Z. Y. Tao, Y. M. Tse, and C. J. Wang. 2021. “Stock Market Prediction with Deep Learning: The Case of China.” Financial Research Letters 46 (A): 102209. doi:10.1016/j.frl.2021.102209.
  • Lu, Z. L., W. F. Lv, Y. B. Cao, Z. P. Xie, H. Peng, and B. Du. 2020. “LSTM Variants Meet Graph Neural Networks for Road Speed Prediction.” Neurocomputing 400: 34–45. doi:10.1016/j.neucom.2020.03.031.
  • Naeem, M. A., E. Bouri, Z. Peng, S. J. H. Shahzad, and X. V. Vo. 2021. “Asymmetric Efficiency of Cryptocurrencies During COVID19.” Physica A: Statistical Mechanics and Its Applications 565: 125562. doi:10.1016/j.physa.2020.125562.
  • Smyl, S. 2020. “A Hybrid Method of Exponential Smoothing and Recurrent Neural Networks for Time Series Forecasting.” International Journal of Forecasting 36 (1): 75–85. doi:10.1016/j.ijforecast.2019.03.017.
  • Wang, L., P. K. Sarker, and E. Bouri. 2022. ”Short- and Long-Term Interactions Between Bitcoin and Economic Variables: Evidence from the US.” Computational Economics, forthcoming. doi:10.1007/s10614-022-10247-5.
  • Wei, W. Q., Q. Zhang, and L. Liu. 2021. “Bitcoin Transaction Forecasting with Deep Network Representation Learning.” IEEE Transactions on Emerging Topics in Computing 9 (3): 1359–1371. doi:10.1109/TETC.2020.3010464.
  • Wen, Z., E. Bouri, Y. Xu, and Y. Zhao. 2022. “Intraday Return Predictability in the Cryptocurrency Markets: Momentum, Reversal, or Both.” North American Journal of Economics and Finance 62: 101733. doi:10.1016/j.najef.2022.101733.
  • Yu, B., H. Yin, and Z. Zhang. 2018. ”Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting.” In: Twenty-Seventh International Joint Conference on Artificial Intelligence IJCAI-18. doi: 10.24963/ijcai.2018/505.