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

Forecasting Bitcoin daily volatility using GARCH modeling

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.

No description available

Forecasting Bitcoin volatility using econometric models (GARCH, GJR-GARCH, TARCH) and deep learning (LSTM), with a focus on risk modeling and out-of-sample performance evaluation.

A time series forecasting project analyzing Bitcoin prices using ARIMA, SARIMA, and GARCH models to predict short-term trends and volatility.

A hybrid XGBoost-GARCH framework for forecasting Bitcoin Implied Volatility using on-chain metrics and wavelet analysis. Doctoral research proposal code.