Comparative Analysis of Econometric and Machine Learning Approaches for Forecasting Bitcoin Return Volatility
(2024) NEKN02 20241Department of Economics
- Abstract (Swedish)
- The fundamental principle of investing, applicable at both systematic and individual levels, is the art of balancing the portfolio between expected returns and acceptable risk exposure. Risk management involves predicting the direction of the underlying asset’s movement and taking appropriate actions accordingly. Traditionally, statistical models such as GARCH have been used to achieve this objective. However, with advances in machine learning, new algorithms may prove more effective by identifying more complex patterns. This paper compares the accuracy of GARCH and LSTM models in predicting future Bitcoin return volatility using the root mean squared error (RMSE) on out-of-sample data. The results indicate that the LSTM model outperforms... (More)
- The fundamental principle of investing, applicable at both systematic and individual levels, is the art of balancing the portfolio between expected returns and acceptable risk exposure. Risk management involves predicting the direction of the underlying asset’s movement and taking appropriate actions accordingly. Traditionally, statistical models such as GARCH have been used to achieve this objective. However, with advances in machine learning, new algorithms may prove more effective by identifying more complex patterns. This paper compares the accuracy of GARCH and LSTM models in predicting future Bitcoin return volatility using the root mean squared error (RMSE) on out-of-sample data. The results indicate that the LSTM model outperforms the GARCH model, suggesting significant potential for machine learning applications in enhancing risk management and forecasting practices. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9156818
- author
- Persson Podvorec, Filip LU
- supervisor
- organization
- course
- NEKN02 20241
- year
- 2024
- type
- H1 - Master's Degree (One Year)
- subject
- keywords
- Bitcoin, Forecast, LSTM, GARCH, Risk Management, Machine Learning
- language
- English
- id
- 9156818
- date added to LUP
- 2024-08-12 15:59:09
- date last changed
- 2024-08-12 15:59:09
@misc{9156818, abstract = {{The fundamental principle of investing, applicable at both systematic and individual levels, is the art of balancing the portfolio between expected returns and acceptable risk exposure. Risk management involves predicting the direction of the underlying asset’s movement and taking appropriate actions accordingly. Traditionally, statistical models such as GARCH have been used to achieve this objective. However, with advances in machine learning, new algorithms may prove more effective by identifying more complex patterns. This paper compares the accuracy of GARCH and LSTM models in predicting future Bitcoin return volatility using the root mean squared error (RMSE) on out-of-sample data. The results indicate that the LSTM model outperforms the GARCH model, suggesting significant potential for machine learning applications in enhancing risk management and forecasting practices.}}, author = {{Persson Podvorec, Filip}}, language = {{eng}}, note = {{Student Paper}}, title = {{Comparative Analysis of Econometric and Machine Learning Approaches for Forecasting Bitcoin Return Volatility}}, year = {{2024}}, }