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Deep Learning Models for Forecasting Cryptocurrency Volatility

Article arXiv papers · Author: Anika Tahsin Meem
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Summary

This study frames cryptocurrency risk as volatility forecasting and applies machine-learning models to calculated risk measures across twenty elements of the cryptocurrency market. It compares convolutional neural networks, long short-term memory models, bidirectional LSTMs, and gated recurrent units, then introduces a new model intended to improve on those alternatives. The assets discussed include Bitcoin, Ethereum, and Dogecoin.

The document reports root mean squared error values for the proposed and existing models, with the proposed approach spanning lower and higher errors than the comparison models. However, it gives little detail here about the data, forecast horizon, volatility construction, validation design, or how the new model is structured. The claimed improvement therefore cannot be independently assessed from this description, and forecast accuracy alone does not show that a volatility estimate improves trading decisions or controls risk in live markets.

Key ideas

  • The research treats cryptocurrency risk as a volatility prediction problem.
  • It compares CNN, LSTM, bidirectional LSTM, and GRU models on calculated risk measures.
  • A newly proposed model is evaluated against existing approaches using RMSE.
  • The summary names Bitcoin, Ethereum, and Dogecoin but provides limited information about data and validation.
  • Forecast error results do not by themselves establish practical trading or risk-management gains.

Tags

CryptoMachine learningVolatilityRisk managementStatistics
Full text
# A Deep Learning Approach to Predict the Fall [of Price] of Cryptocurrency Long Before its Actual Fall


# A Deep Learning Approach to Predict the Fall [of Price] of Cryptocurrency Long Before its Actual Fall









In modern times, the cryptocurrency market is one of the world's most rapidly rising financial markets. The cryptocurrency market is regarded to be more volatile and illiquid than traditional markets such as equities, foreign exchange, and commodities. The risk of this market creates an uncertain condition among the investors. The purpose of this research is to predict the magnitude of the risk factor of the cryptocurrency market. Risk factor is also called volatility. Our approach will assist people who invest in the cryptocurrency market by overcoming the problems and difficulties they experience. Our approach starts with calculating the risk factor of the cryptocurrency market from the existing parameters. In twenty elements of the cryptocurrency market, the risk factor has been predicted using different machine learning algorithms such as CNN, LSTM, BiLSTM, and GRU. All of the models have been applied to the calculated risk factor parameter. A new model has been developed to predict better than the existing models. Our proposed model gives the highest RMSE value of 1.3229 and the lowest RMSE value of 0.0089. Following our model, it will be easier for investors to trade in complicated and challenging financial assets like bitcoin, Ethereum, dogecoin, etc. Where the other existing models, the highest RMSE was 14.5092, and the lower was 0.02769. So, the proposed model performs much better than models with proper generalization. Using our approach, it will be easier for investors to trade in complicated and challenging financial assets like Bitcoin, Ethereum, and Dogecoin.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.