FORECASTING PRICE VOLATILITY IN CRYPTO TRADING PAIRS: A MACHINE LEARNING APPROACH

In recent years, the digitization of financial markets and the growth of web-3 trading pairs have significantly impacted the landscape of asset trading. The accessibility of decentralized exchanges and the high volatility of cryptocurrency markets, compared to traditional assets, pose critical challenges for traders, especially in predicting price fluctuations and managing risks. This paper explores the application of machine learning (ML) models to forecast price volatility in cryptocurrency trading pairs. Specifically, it investigates the use of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models to predict price movements and assess market volatility. The research highlights the limitations of traditional risk management approaches, which fail to account for the unpredictable nature of price fluctuations, especially in high-frequency trading environments. By combining ML with advanced risk management techniques, this study aims to provide a more accurate prediction of volatile price movements, thus enabling traders to make more informed decisions regarding entry points, stop-loss levels, and the overall risk-reward ratio. The paper also discusses the use of Variational Autoencoders (VAE) to analyze volatility in the cryptocurrency market, providing a broader perspective on volatility modeling. The findings suggest that ML models, particularly GRU, show promising results in forecasting cryptocurrency price movements and managing volatility, offering a valuable tool for risk management in high-risk trading environments.