new economy and trad

new economy and trad

Volatility Prediction Using Hybrid Deep Learning and GARCH Family Models: A Case Study on Bitcoin

Document Type : Research Paper

Authors
saeed sadrzadeh moghadam 1
kambiz hozhabrkiani 2
kambiz peykarjou 3
alireza moradi 4
1 Ph.D. Student, Department of Economic, Faculty of Management and Economics, Islamic Azad University, Science and Research Branch, Tehran, Iran.
2 Prof. Department of Economic, Faculty of Economics, Shahid Beheshti University, Tehran, Iran.
3 Assistant Prof, Department of Economic, Faculty of Management and Economic, Science and Research Branch, Islamic Azad University, Tehran, Iran.
4 Assistant Prof, Department of Economic, Faculty of Humanities, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran
10.30465/jnet.2025.47420.2096
Abstract
This study aims to predict the volatility of cryptocurrencies, which is a crucial and difficult task. Considering the nonlinear characteristics and time-varying features of various factors that affect the price of cryptocurrencies, this study uses a novel method that combines two powerful techniques: the GARCH model and the LSTM network. The GARCH model captures the statistical patterns of price fluctuations and provides GARCH forecasts. The second technique is machine learning models. Previous studies have shown that combining GARCH models and machine learning can improve the volatility prediction in various markets, such as energy, metals and stocks markets. This study tests this hypothesis in the cryptocurrency market by using different LSTM models to predict the volatility of a selected cryptocurrency. It also creates hybrid models that feed the outputs of different GARCH variants, with three different assumptions for the residual distribution, to the LSTM network. In other words, the GARCH models act as feature extractors and the LSTM models use the extracted features as input to generate future volatility. The results show that the LSTM models alone outperform the GARCH models with any residual distribution, and that the GARCH models as features can enhance the prediction performance of the LSTM models.
Keywords
GARCH
Machine Learning
LSTM
BitCoin
Volatility
Assaf, A. M., Haron, H., Abdull Hamed, H. N., Ghaleb, F. A., Qasem, S. N., & Albarrak, A. M. (2023). A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting. Applied Sciences, 13(14), 8332.
Banik, S., Sharma, N., Mangla, M., Mohanty, S. N., & Shitharth, S. (2022). LSTM-based decision support system for swing trading in the stock market. Knowledge-Based Systems, 239, 107994.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327.
Chu, J., Chan, S., Nadarajah, S., & Osterrieder, J. (2017). GARCH modeling of cryptocurrencies. Journal of Risk and Financial Management, 10(4), 17.
Engle, R. (2001). GARCH 101: The use of ARCH/GARCH models in applied econometrics. Journal of Economic Perspectives, 15(4), 157–168.
Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics, 20(3), 339–350.
Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654–669.
García-Medina, A., & Luu Duc Huynh, T. (2021). What drives Bitcoin? An approach from continuous local transfer entropy and deep learning classification models. Entropy, 23(12), 1582.
Hajizadeh, E., Seifi, A., Fazel Zarandi, M. H., & Turksen, I. B. (2012). A hybrid modeling approach for forecasting the volatility of the S&P 500 index return. Expert Systems with Applications, 39(1), 431–436.
Khaldi, R., el Afia, A., & Chiheb, R. (2019). Forecasting BTC volatility: A comparative study between parametric and nonparametric models. Progress in Artificial Intelligence, 8(4), 511–523.
Kim, H. Y., & Won, C. H. (2018). Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models. Expert Systems with Applications, 103, 25–37.
Kristjanpoller, W., Fadic, A., Minutolo, M. C. (2014). Volatility forecast using hybrid neural network models. Expert Systems with Applications, 41(5), 2437–2442.
Kristjanpoller, W., Minutolo, M. C. (2016). Forecasting volatility of oil price using an artificial neural network-GARCH model. Expert Systems with Applications, 65, 233–241.
Kristjanpoller, W., Minutolo, M. C. (2018). A hybrid volatility forecasting framework integrating GARCH, artificial neural network, technical analysis, and principal components analysis. Expert Systems with Applications, 109, 1–11.
Lamoureux, C. G., & Lastrapes, W. D. (1990). Heteroskedasticity in stock return data: Volume versus GARCH effects. The Journal of Finance, 45(1), 221–229.
Liu, Y. (2019). Novel volatility forecasting using deep learning–Long ShortTerm Memory Recurrent Neural Networks. Expert Systems with Applications, 132, 99–109.
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and machine learning forecasting methods: Concerns and ways forward. PloS One, 13(3), 0194889.
Peng, Y., Albuquerque, P. H. M., Camboim de Sá, J. M., Padula, A. J. A., & Montenegro, M. R. (2018). The best of two worlds: Forecasting high-frequency volatility for cryptocurrencies and traditional currencies with Support Vector Regression. Expert Systems with Applications, 97, 177–192.
Quintero Valencia, D. E., et al. (2019). Pronóstico de volatilidad de la TRM mediante un modelo híbrido LSTM–GARCH. PhD thesis, Universidad del Rosario.
Ramos-Pérez, E., Alonso-González, P. J., & Núñez Velázquez, J. J. (2019). Forecasting volatility with a stacked model based on a hybridized Artificial Neural Network. Expert Systems with Applications, 129, 1–9.
Rayadurgam, Vikram Chandramouli, & Mangalagiri, Jayasree. (2023). Does inclusion of GARCH variance in deep learning models improve financial contagion prediction? Finance Research Letters, 54.
Seo, M., & Kim, G. (2020). Hybrid forecasting models based on neural networks for the volatility of Bitcoin. Applied Sciences (Switzerland), 10(14), 00.
Vidal, A., & Kristjanpoller, W. (2020). Gold volatility prediction using a CNN-LSTM approach. Expert Systems with Applications, 157(00).
Vijayalakshmi, V. (2024). Implementation of sentiment analysis in stock market prediction using variants of GARCH models. In Computational Intelligence Methods for Sentiment Analysis in Natural Language Processing Applications (pp. 227–249). Morgan Kaufmann.
Zeng, H., Shao, B., Dai, H., Yan, Y., & Tian, N. (2023). Prediction of fluctuation loads based on GARCH family-CatBoost-CNNLSTM. Energy, 126125.
Zolfaghari, M., Sahabi, B., & Bakhtyaran, M. (2020). Designing a model for forecasting the stock exchange total index returns (Emphasizing on combined deep learning network models and GARCH family models). Financial Engineering and Securities Management (Portfolio Management), 11(42), 138–171.

  • Receive Date 14 November 2023
  • Revise Date 18 January 2025
  • Accept Date 16 February 2025