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

This project used GARCH type models to estimate volatility and used delta hedging method to make a profit.

By combining GARCH(1,1) and LSTM model implementing predictions.

A repository to explore the concepts of applied econometrics in the context of financial time-series.

The Tidymodels Extension for GARCH models

Traditionally, volatility is modeled using parametric models. This project focuses on predicting EUR/USD volatility using more flexible, machine-learning methods.

Portfolio level (un)conditional risk measure estimation for backtesting using Vine Copula and ARMA-GARCH models.

A stock price prediction model based on ARMA and GARCH

使用经典的AR、MA、ARMA、ARIMA、ARCH、GARCH时间序列模型进行模型的检验和拟合。The classic AR, MA, ARMA, ARIMA, ARCH, GARCH time series models are used to test and predict the model.

Python code for rolling Value at Risk(VaR) of fiancial assets and some of economic time series, based on the procedure proposed by Hull & White(1998).

In this project, this research generally investigates the financial time series such as the price & return of NASDAQ Composite Index using ARIMA and GARCH methods.

Unit root tests, ARIMAX, GARCH models for the time being

Implied volatility is a key aspect when it comes to derivatives pricing. With the growing influence of machine learning in finance, I have investigated the use of LSTMs to forecast 1-day forward Implied Volatility.

The aim of this project is to help stocktraders determine suitable stock to enter by helping them keep track of its daily volatility and returns. The user selects a particular stock option which is automatically gotten from an API and stored in a sqlite database. using Garch(1,1) model to forecast volatility. fastapi and dash is used for deployment

Stock/Financial Time Series Analysis, Prediction and Forecasting using advanced Statistical methods and GARCH volatility-based models in R.

Study on volatility transmission and protuberance among developed and developing stock markets using multivariate GARCH

Time series analysis on NIFTY data ( bank,oil,metal,it ) using GARCH model in R.

This repository of codes includes in the R and Python programs used in the six chapters of my published book titled "Analysis and Forecasting of Financial Time Series: Selected Cases". The book is published by Cambridge Scholars Publishing, New Casle upon Tyne, United Kindoam, in 2022.

Project in Statistics: Timeseries analysis (STAH14) at Lund University. The project it about Bitcoin price and returns, modelled using an AR-GARCH model.

Learned time series analysis from Quantstart