GARCH and Multivariate LSTM forecasting models for Bitcoin realized volatility with potential applications in crypto options trading, hedging, portfolio management, and risk management

Forecasting Bitcoin Volatility Spikes from Whale Transactions and Cryptoquant Data Using Synthesizer Transformer Models

GARCH and Multivariate LSTM forecasting models for Bitcoin realized volatility with potential applications in crypto options trading, hedging, portfolio management, and risk management

Repository of the paper 'Ask "Who", Not "What": Bitcoin Volatility Forecasting with Twitter Data'

focuses on predicting cryptocurrency price movements (e.g., Bitcoin, Ethereum) and modeling their market volatility using time series forecasting techniques like LSTM and ARIMA, and financial risk models like GARCH

Price Analysis, Volatility & Forecasting of Bitcoins

Bayesian analysis and forecasting of Bitcoin volatility. Definition of GARCH and ARCH models through MCMC sampling.

Cryptocurrency markets, particularly Bitcoin, exhibit high volatility, making them challenging to predict. This project employs machine learning techniques to forecast Bitcoin prices, leveraging historical data and various predictive feature

Predicted Bitcoin Prices using ARIMA Time Series forecasting to monitor real time predicted prices, leading to improved decision-making. Used the volatility of Bitcoin Prices along with the historical data from Kaggle to predict prices in real time.

Crypto Price Prediction with LSTM uses machine learning and LSTM neural networks to forecast Bitcoin (BTC-USD) prices based on historical data. This project is for educational purposes only and should not be used for actual financial decisions due to cryptocurrency market volatility.

Forecasting Bitcoin daily volatility using GARCH modeling

This project aims to predict Bitcoin's price volatility by leveraging machine learning techniques, specifically the Random Forest algorithm. Bitcoin's highly volatile nature makes accurate forecasting crucial for investors, traders, and policymakers.

Forecasting realized volatility using GARCH-type models: estimation and prediction with R

Analysis and forecasting of Gold, Bitcoin volatility and Air quality

Implementing Bitcoin futures' strike prices and time-to-maturity to construct a volatility surface for potential profit opportunities. Utilizing time series and the GARCH model for volatility forecasting and Long Short-Term Memory (LSTM) for bitcoin futures' price forecasting in Python.

Bitcoin Price Trend and Volatility Analysis with ARIMA-GARCH This project analyzes historical Bitcoin price data using ARIMA for trend forecasting and GARCH for volatility modeling. It includes data preprocessing, time series modeling, volatility forecasting, and visualization of both price predictions and risk bands.

This project implements an advanced quantitative pipeline to forecast Bitcoin (BTC-USD) Realized Volatility for December 2025.

Bitcoin is a decentralized digital currency that has gained significant popularity over the years. Predicting its price can be challenging due to its volatility. This project leverages historical data and machine learning algorithms to forecast Bitcoin prices.

his repository contains a research project focused on forecasting Bitcoin (BTC‑USD) prices using the ARIMA (AutoRegressive Integrated Moving Average) model within a machine learning framework. The project explores the challenges of cryptocurrency volatility and demonstrates how ARIMA can be applied to time‑series data for financial forecasting.

Bitcoin price prediction using Long Short-Term Memory (LSTM) models is a popular application of deep learning in time series forecasting. Bitcoin’s price is highly volatile and influenced by various factors such as market demand, regulatory news, technological advancements, and investor sentiment. .

Developed a deep learning model using Long Short-Term Memory (LSTM) networks to predict Bitcoin’s closing price based on historical market data. The project aimed to capture the temporal dependencies and volatility of Bitcoin prices to forecast future trends with higher accuracy than traditional machine learning models.

Time-series forecasting project for Bitcoin prices using deep learning. Compares feedforward MLP and sequence-based LSTM models to highlight the advantage of temporal learning in volatile financial data. Built with PyTorch and evaluated using standard regression metrics.

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XGBoost-based Bitcoin volatility forecasting system

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This project aims to predict Bitcoin's price volatility by leveraging machine learning techniques, specifically the Random Forest algorithm. Bitcoin's highly volatile nature makes accurate forecasting crucial for investors, traders, and policymakers.

This project aims to predict Bitcoin's price volatility by leveraging machine learning techniques, specifically the Random Forest algorithm. Bitcoin's highly volatile nature makes accurate forecasting crucial for investors, traders, and policymakers.

No description available

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A Regime-Switching Approach to Bitcoin Volatility Forecasting