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.

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.

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

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.

Forecasting Bitcoin daily volatility using GARCH modeling

Analysis and forecasting of Gold, Bitcoin volatility and Air quality

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.

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.

No description available

No description available

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

No description available

No description available

No description available

XGBoost-based Bitcoin volatility forecasting system

A Regime-Switching Approach to Bitcoin Volatility Forecasting

QFI Labs NH-HMM Bitcoin regime detection and volatility forecasting pipeline

Machine learning model for forecasting hourly Bitcoin volatility with live deployment.

Bitcoin Price Analysis and Forecasting: Volatility Insights, Time Series Modeling, and Visualization

Code for Bitcoin Price Behaviour subject to condition of Dynamic Volatility: A Time-series forecasting approach

Rolling GARCH volatility forecasting, VaR backtesting, and regime-based position sizing on Bitcoin daily returns (2013-2024)