garch-models
There are 5 repositories under garch-models topic.
- bitcoin_volatility_forecasting
chibui191 / bitcoin_volatility_forecasting
GARCH and Multivariate LSTM forecasting models for Bitcoin realized volatility with potential applications in crypto options trading, hedging, portfolio management, and risk management
Language:Jupyter Notebook 211 Blue-Universe / Time-Series-Analysis-Statistical-Arbitrage
This project used GARCH type models to estimate volatility and used delta hedging method to make a profit.
Language:Jupyter Notebook 58Bturan19 / Neural-Garch-Hybrid-Model-Implementation
By combining GARCH(1,1) and LSTM model implementing predictions.
Language:Jupyter Notebook 54AlbertoAlmuinha / garchmodels
The Tidymodels Extension for GARCH models
Language:R 34- Applied-Econometric-Time-Series
andreachello / Applied-Econometric-Time-Series
A repository to explore the concepts of applied econometrics in the context of financial time-series.
Language:Jupyter Notebook 31 csatzky / forecasting-realized-volatility-using-supervised-learning
Traditionally, volatility is modeled using parametric models. This project focuses on predicting EUR/USD volatility using more flexible, machine-learning methods.
Language:R 21EmanuelSommer / portvine
Portfolio level (un)conditional risk measure estimation for backtesting using Vine Copula and ARMA-GARCH models.
Language:R 20Nyarukotep / ARMA-GARCH-Model
A stock price prediction model based on ARMA and GARCH
Language:Jupyter Notebook 17stxupengyu / time-series-analysis
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.
Language:Jupyter Notebook 15lyx66 / Value-at-Risk-VaR-Based-on-Historical-Simulation-in-Conjunction-with-GARCH-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).
Language:Jupyter Notebook 11vincent27hugh / Time_Series_ARIMA-GARCH
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.
Language:R 11GinIchimaru / VBA_Time_Series
Unit root tests, ARIMAX, GARCH models for the time being
Language:Visual Basic 7flexx3 / Stock_Analytics_Forecast_app
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
Language:Jupyter Notebook 5anthonyli01 / Neural-Network-Approach-to-Implied-Volatility-Forecasting
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.
Language:Jupyter Notebook 4sam14032000 / market_linkages_mgarch-bekk
Study on volatility transmission and protuberance among developed and developing stock markets using multivariate GARCH
Language:Jupyter Notebook 4aakashkh / Time-Series-Analysis
Learned time series analysis from Quantstart
Language:HTML 3Code for the case studies and theoretical visualizations for the master thesis 'Estimation and Backtesting of the Expected Shortfall and Value at Risk using Vine Copulas'
Language:HTML 3mahendranandi / NIFTY-Share-Market-Price-Prediction
Time series analysis on NIFTY data ( bank,oil,metal,it ) using GARCH model in R.
Language:Jupyter Notebook 3TheodorEmanuelsson / BitcoinGARCH
Project in Statistics: Timeseries analysis (STAH14) at Lund University. The project it about Bitcoin price and returns, modelled using an AR-GARCH model.
Language:R 3wywongbd / EPFL-Time-Series
MATH-342 Time Series course taken at EPFL during Spring 17-18.
Language:R 3allisterdelrosario / FINSTAT
Applied Regression and Time Series for Financial Research
danielegiulianini / inbestment-portfolio
A web-based and machine-learning fostered prototype tool to find your best financial investment portfolio
Language:Python 2hutch24 / Econometrics_with_R
R을 이용한 경제 시계열 데이터 분석 / GARCH, Legendre models
Language:R 2- Analysis-and-Forecasting-of-Financial-Time-Series-Selected-Cases
JaydipSen / Analysis-and-Forecasting-of-Financial-Time-Series-Selected-Cases
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.
cointegration-analysiscointegration-strategyegarchgarch-modelsgjr-garchhierarchicalhierarchical-clusteringlstm-modellstm-neural-networksmean-variance-optimizationoptimization-algorithmsportfolioportfolio-optimizationriskrisk-analysistime-seriestime-series-analysistime-series-forecastingtime-series-prediction jolly-io / Stock_Markets_Assymetric_Volatility_Spillover_Effects
I investigate the Asymmetric Volatility Spillover Effects within and across six major International stock markets. United States, Canada, France, Germany, Italy & Japan
Language:R 2rafa-rod / Curso-Introducao-Risco-de-Mercado-em-Python---Financial-Risk-Academy
Curso ministrado por mim na Financial Risk Academy (FRA) sobre Introdução ao Risco de Mercado com Python
AneesahG / stockreturns
Predictive analysis and GARCH model on stock returns. I demonstrate how to use the PACF (partial autocorrelation function) and ACF (autocorrelation function) on a non stationary time series.
Language:R 1benjaminweymouth / Time-Series-analysis-using-ARIMA
This repository holds 2 Jupyter notebooks and one csv file on Time Series analysis for the A Yen for the Future exercises. The purpose of this code is to demonstrate understanding of time series work in Python: ARMA, ARIMA and related concepts.
Language:Jupyter Notebook 1DavidAlexanderMoe / Financial-Time-Series-Analysis-and-Forecasting-GARCH-models
Stock/Financial Time Series Analysis, Prediction and Forecasting using advanced Statistical methods and GARCH volatility-based models in R.
Language:R 1- Language:Jupyter Notebook 1
LinhNguyen-MyLi / GARCH-model-forecast
Apply GARCH (1,1) model into forecasting S&P500. The topic is harder than though so it's still under construction but I'm working on it.
Language:Jupyter Notebook 1namazlilkin / Time-Series-Analysis
Estimating the impact of Covid-19 pandemic on the Value-at-Risk of energy commodities (R)
Avinash793 / time-series-analysis-examples
Detailed implementation of various time series analysis models and concepts on real datasets.
Language:Jupyter Notebook 0RAGHAV-N5 / UnsupervisedLearningTrading-SentimentData-GarchModel
This is a project which uses Data Science, Machine learning to predict the stock movements, minimize the risk and maximise gains of portfolio using fama-french factors and many other models.Also the sentiment towards stocks are also monitored using sentiment analysis. Garch Model is used to predict the volatility and movements for intraday trading.
Language:Jupyter Notebook 0kottoization / FinancialTimeSeriesProject
Repository for keeping the code used for project of the course Financial Econometrics / Financial Time Series.
Language:HTMLwpwwhimself / master-thesis
[PL] My master thesis from PUEB
Language:TeX
