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Machine learning approaches to forecasting cryptocurrency volatility: Considering internal and external determinants. (2023). Wang, Yijun ; Andreeva, Galina ; Martin-Barragan, Belen.
In: International Review of Financial Analysis.
RePEc:eee:finana:v:90:y:2023:i:c:s1057521923004301.

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  2. Dynamics in Realized Volatility Forecasting: Evaluating GARCH Models and Deep Learning Algorithms Across Parameter Variations. (2025). Gulay, Emrah ; Akgun, Omer Burak.
    In: Computational Economics.
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  3. A hybrid deep learning model for cryptocurrency returns forecasting: Comparison of the performance of financial markets and impact of external variables. (2025). Jirou, Ismail ; Jebabli, Ikram ; Lahiani, Amine.
    In: Research in International Business and Finance.
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  4. Does multi-scale GARCH information enhance volatility prediction?. (2025). Yu, Rentian ; Xiao, Haotian ; Zhu, Yukun ; Zhang, Gongqiu.
    In: Finance Research Letters.
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  5. Explainable-machine-learning-based online transaction analysis of China property rights exchange capital market. (2025). Zhou, YU ; Guo, Zitong ; Zhang, Zihe.
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  6. Modeling climate policy uncertainty into cryptocurrency volatilities. (2025). Cui, Tianxiang ; Wu, Xiangling ; Ding, Shusheng ; Goodell, John W ; Du, Anna Min.
    In: International Review of Financial Analysis.
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  7. Forecasting Bitcoin volatility using machine learning techniques. (2024). Urquhart, Andrew ; Sangiorgi, Ivan ; Huang, Zih-Chun.
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  8. Do design features explain the volatility of cryptocurrencies?. (2024). Shi, Yanghua ; Uhrig-Homburg, Marliese ; Eska, Fabian E ; Theissen, Erik.
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  9. The interdependence structure of cryptocurrencies and Chinese financial assets. (2024). Gao, Ting ; Wang, Huaiming ; Du, Dongying.
    In: Finance Research Letters.
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  10. Enhancing cryptocurrency market volatility forecasting with daily dynamic tuning strategy. (2024). lucey, brian ; Feng, Lingbing ; Qi, Jiajun.
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  12. Review of deep learning models for crypto price prediction: implementation and evaluation. (2024). Zhang, Xinyi ; Chandra, Rohtiash ; Wu, Jingyang ; Zhou, Haochen ; Huang, Fangyixuan.
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References

References cited by this document

  1. Aggarwal, A. ; Gupta, I. ; Garg, N. ; Goel, A. Deep learning approach to determine the impact of socio economic factors on bitcoin price prediction. 2019 En : 2019 Twelfth international conference on contemporary computing. IEEE:
    Paper not yet in RePEc: Add citation now
  2. Agnolucci, P. Volatility in crude oil futures: A comparison of the predictive ability of GARCH and implied volatility models. 2009 Energy Economics. 31 316-321

  3. Akyildirim, E. ; Corbet, S. ; Lucey, B. ; Sensoy, A. ; Yarovaya, L. The relationship between implied volatility and cryptocurrency returns. 2020 Finance Research Letters. 33 -

  4. Alessandretti, L. ; ElBahrawy, A. ; Aiello, L.M. ; Baronchelli, A. Machine learning the cryptocurrency market. 2018 Complexity. 2018 -
    Paper not yet in RePEc: Add citation now
  5. Alexander, C. ; Dakos, M. A critical investigation of cryptocurrency data and analysis. 2020 Quantitative Finance. 20 173-188

  6. Babaei, G. ; Giudici, P. ; Raffinetti, E. Explainable artificial intelligence for crypto asset allocation. 2022 Finance Research Letters. -

  7. Baker, S.R. ; Bloom, N. ; Davis, S.J. Measuring economic policy uncertainty. 2016 Quarterly Journal of Economics. 131 1593-1636

  8. Bengio, Y. ; Goodfellow, I. ; Courville, A. . 2017 MIT press Cambridge: MA, USA
    Paper not yet in RePEc: Add citation now
  9. Bianchi, D. ; Büchner, M. ; Tamoni, A. Bond risk premiums with machine learning. 2021 The Review of Financial Studies. 34 1046-1089

  10. Bianchi, D. ; Guidolin, M. ; Pedio, M. The dynamics of returns predictability in cryptocurrency markets. 2022 The European Journal of Finance. 1-29
    Paper not yet in RePEc: Add citation now
  11. Bollerslev, T. Generalized autoregressive conditional heteroskedasticity. 1986 Journal of Econometrics. 31 307-327

  12. Bouktif, S. ; Fiaz, A. ; Ouni, A. ; Serhani, M.A. Optimal deep learning lstm model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches. 2018 Energies. 11 1636-

  13. Breiman, L. Random forests. 2001 Machine Learning. 45 5-32
    Paper not yet in RePEc: Add citation now
  14. Catania, L. ; Grassi, S. Forecasting cryptocurrency volatility. 2022 International Journal of Forecasting. 38 878-894
    Paper not yet in RePEc: Add citation now
  15. Chen, W. ; Xu, H. ; Jia, L. ; Gao, Y. Machine learning model for bitcoin exchange rate prediction using economic and technology determinants. 2021 International Journal of Forecasting. 37 28-43

  16. Chen, Z. ; Li, C. ; Sun, W. Bitcoin price prediction using machine learning: An approach to sample dimension engineering. 2020 Journal of Computational and Applied Mathematics. 365 -
    Paper not yet in RePEc: Add citation now
  17. Cheng, H.-P. ; Yen, K.-C. The relationship between the economic policy uncertainty and the cryptocurrency market. 2020 Finance Research Letters. 35 -

  18. Chung, H. ; Shin, K.-s. Genetic algorithm-optimized long short-term memory network for stock market prediction. 2018 Sustainability. 10 3765-

  19. Conrad, C. ; Custovic, A. ; Ghysels, E. Long-and short-term cryptocurrency volatility components: A GARCH-MIDAS analysis. 2018 Journal of Risk and Financial Management. 11 23-

  20. Corbet, S. ; Lucey, B. ; Yarovaya, L. Datestamping the bitcoin and ethereum bubbles. 2018 Finance Research Letters. 26 81-88

  21. Fior, J. ; Cagliero, L. ; Garza, P. Leveraging explainable AI to support cryptocurrency investors. 2022 Future Internet. 14 251-

  22. Fischer, T. ; Krauss, C. Deep learning with long short-term memory networks for financial market predictions. 2018 European Journal of Operational Research. 270 654-669

  23. Gökbulut, R.I. ; Pekkaya, M. Estimating and forecasting volatility of financial markets using asymmetric GARCH models: An application on Turkish financial markets. 2014 International Journal of Economics and Finance. 6 23-35
    Paper not yet in RePEc: Add citation now
  24. Goodfellow, I. ; Bengio, Y. ; Courville, A. Deep learning. 2016 MIT Press:
    Paper not yet in RePEc: Add citation now
  25. Gradojevic, N. ; Kukolj, D. ; Adcock, R. ; Djakovic, V. Forecasting bitcoin with technical analysis: A not-so-random forest?. 2023 International Journal of Forecasting. 39 1-17

  26. Hochreiter, S. ; Schmidhuber, J. Long short-term memory. 1997 Neural Computation. 9 1735-1780
    Paper not yet in RePEc: Add citation now
  27. Holland, J.H. Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. 1992 MIT Press:
    Paper not yet in RePEc: Add citation now
  28. Jalan, A. ; Matkovskyy, R. ; Urquhart, A. ; Yarovaya, L. The role of interpersonal trust in cryptocurrency adoption. 2022 :
    Paper not yet in RePEc: Add citation now
  29. Karaboga, D. An idea based on honey bee swarm for numerical optimization. 2005 Erciyes university, engineering faculty, computer engineering department:
    Paper not yet in RePEc: Add citation now
  30. Kumar, R. ; Kumar, P. ; Kumar, Y. Integrating big data driven sentiments polarity and ABC-optimized LSTM for time series forecasting. 2021 Multimedia Tools and Applications. 1-20
    Paper not yet in RePEc: Add citation now
  31. Li, Z. ; Li, Z. ; Li, Z. ; Li, Y. Application of GA-LSTM model in cable joint temperature prediction. 2020 En : 2020 7th International forum on electrical engineering and automation. IEEE:
    Paper not yet in RePEc: Add citation now
  32. Lim, B. ; Zohren, S. Time-series forecasting with deep learning: A survey. 2021 Philosophical Transactions of the Royal Society, Series A. 379 -
    Paper not yet in RePEc: Add citation now
  33. Liu, Y. ; Tsyvinski, A. Risks and returns of cryptocurrency. 2021 The Review of Financial Studies. 34 2689-2727

  34. Liu, Y. ; Tsyvinski, A. ; Wu, X. Common risk factors in cryptocurrency. 2022 The Journal of Finance. 77 1133-1177

  35. Lundberg, S. M., & Lee, S. -I. (2017). A unified approach to interpreting model predictions. In Proceedings of the 31st international conference on neural information processing systems (pp. 4768–4777).
    Paper not yet in RePEc: Add citation now
  36. Masini, R.P. ; Medeiros, M.C. ; Mendes, E.F. Machine learning advances for time series forecasting. 2023 Journal of Economic Surveys. 37 76-111

  37. McNally, S. ; Roche, J. ; Caton, S. Predicting the price of bitcoin using machine learning. 2018 En : 2018 26th Euromicro international conference on parallel, distributed and network-based processing. IEEE:
    Paper not yet in RePEc: Add citation now
  38. Nti, I.K. ; Adekoya, A.F. ; Weyori, B.A. A systematic review of fundamental and technical analysis of stock market predictions. 2020 Artificial Intelligence Review. 53 3007-3057
    Paper not yet in RePEc: Add citation now
  39. Ozbayoglu, A.M. ; Gudelek, M.U. ; Sezer, O.B. Deep learning for financial applications: A survey. 2020 Applied Soft Computing. 93 -

  40. Patro, S. ; Sahu, K.K. Normalization: A preprocessing stage. 2015 :
    Paper not yet in RePEc: Add citation now
  41. Peng, Y. ; Albuquerque, P.H.M. ; de Sá, J.M.C. ; Padula, A.J.A. ; Montenegro, M.R. The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with support vector regression. 2018 Expert Systems with Applications. 97 177-192
    Paper not yet in RePEc: Add citation now
  42. Probst, P. ; Wright, M.N. ; Boulesteix, A.-L. Hyperparameters and tuning strategies for random forest. 2019 Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 9 -
    Paper not yet in RePEc: Add citation now
  43. Shen, D. ; Urquhart, A. ; Wang, P. Forecasting the volatility of bitcoin: The importance of jumps and structural breaks. 2020 European Financial Management. 26 1294-1323

  44. Shrikumar, A. ; Greenside, P. ; Kundaje, A. Learning important features through propagating activation differences. 2017 En : International conference on machine learning. PMLR:
    Paper not yet in RePEc: Add citation now
  45. Siami-Namini, S. ; Namin, A.S. Forecasting economics and financial time series: ARIMA vs. LSTM. 2018 :

  46. Sigaki, H.Y.D. ; Perc, M. ; Ribeiro, H.V. Clustering patterns in efficiency and the coming-of-age of the cryptocurrency market. 2019 Scientific Reports. 9 1-9

  47. Sirignano, J. ; Cont, R. Universal features of price formation in financial markets: Perspectives from deep learning. 2019 Quantitative Finance. 19 1449-1459

  48. Smales, L.A. Investor attention in cryptocurrency markets. 2022 International Review of Financial Analysis. 79 -
    Paper not yet in RePEc: Add citation now
  49. Trucíos, C. Forecasting bitcoin risk measures: A robust approach. 2019 International Journal of Forecasting. 35 836-847

  50. Urquhart, A. ; Lucey, B. Crypto and digital currencies—nine research priorities. 2022 :
    Paper not yet in RePEc: Add citation now
  51. van Binsbergen, J.H. ; Han, X. ; Lopez-Lira, A. Man vs. machine learning: The term structure of earnings expectations and conditional biases. 2022 The Review of Financial Studies. -
    Paper not yet in RePEc: Add citation now
  52. Wang, L. ; Ma, F. ; Liu, J. ; Yang, L. Forecasting stock price volatility: New evidence from the GARCH-MIDAS model. 2020 International Journal of Forecasting. 36 684-694

  53. Yen, K.-C. ; Cheng, H.-P. Economic policy uncertainty and cryptocurrency volatility. 2021 Finance Research Letters. 38 -

  54. Yuliyono, A.D. ; Girsang, A.S. Artificial bee colony-optimized LSTM for bitcoin price prediction. 2019 Advances in Science, Technology and Engineering Systems Journal. 4 375-383
    Paper not yet in RePEc: Add citation now
  55. Yun, K.K. ; Yoon, S.W. ; Won, D. Prediction of stock price direction using a hybrid GA-XGBoost algorithm with a three-stage feature engineering process. 2021 Expert Systems with Applications. 186 -
    Paper not yet in RePEc: Add citation now

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  44. Forecasting petroleum futures markets volatility: The role of regimes and market conditions. (2011). Pouliasis, Panos ; Nomikos, Nikos K..
    In: Energy Economics.
    RePEc:eee:eneeco:v:33:y:2011:i:2:p:321-337.

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  45. A Characterization of Oil Price Behavior - Evidence from Jump Models. (2011). Gronwald, Marc.
    In: CESifo Working Paper Series.
    RePEc:ces:ceswps:_3644.

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  46. An empirical model of daily highs and lows of West Texas Intermediate crude oil prices. (2010). Wan, Alan ; Wan, Alan T. K., ; Kwok, Jerry T. K., ; He, Angela W. W., .
    In: Energy Economics.
    RePEc:eee:eneeco:v:32:y:2010:i:6:p:1499-1506.

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  47. Forecasting crude oil market volatility: Further evidence using GARCH-class models. (2010). Wang, Yudong ; Wei, YU ; Huang, Dengshi.
    In: Energy Economics.
    RePEc:eee:eneeco:v:32:y:2010:i:6:p:1477-1484.

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  48. International evidence on crude oil price dynamics: Applications of ARIMA-GARCH models. (2010). Mohammadi, Hassan ; Su, Lixian.
    In: Energy Economics.
    RePEc:eee:eneeco:v:32:y:2010:i:5:p:1001-1008.

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  49. Nonlinearity and intraday efficiency tests on energy futures markets. (2010). Yang, Jian ; Wang, Tao.
    In: Energy Economics.
    RePEc:eee:eneeco:v:32:y:2010:i:2:p:496-503.

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  50. Jumps in Oil Prices- Evidence and Implications. (2009). Gronwald, Marc.
    In: ifo Working Paper Series.
    RePEc:ces:ifowps:_75.

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