ARCH models in Python

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

DCC GARCH modeling in Python

ARMA-GARCH

A Julia package for estimating ARMA-GARCH models.

Financial research data services for academics.

MSGARCH R Package

An R package for using mixed-frequency GARCH models

Estimating Value-at-Risk with a recurrent neural network (Jordan type) GARCH model

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

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DCC-GARCH(1,1) for multivariate normal distribution.

I perform time series analysis of data from scratch. I also implement The Autoregressive (AR) Model, The Moving Average (MA) Model, The Autoregressive Moving Average (ARMA) Model, The Autoregressive Integrated Moving Average (ARIMA) Model, The ARCH Model, The GARCH model, Auto ARIMA, forecasting and exploring a business case.

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

Open souce quantitative finance models and algorithms with tutorials

A Python implementation of a Hybrid LSTM-GARCH model for volatility forecasting

Winter 2020 Course description: Econometric and statistical techniques commonly used in quantitative finance. Use of estimation application software in exercises to estimate volatility, correlations, stability, regressions, and statistical inference using financial time series. Topic 1: Time series properties of stock market returns and prices  Class intro: Forecasting and Finance  The random walk hypothesis  Stationarity  Time-varying volatility and General Least Squares  Robust standard errors and OLS Topic 2: Time-dependence and predictability  ARMA models  The likelihood function, exact and conditional likelihood estimation  Predictive regressions, autocorrelation robust standard errors  The Campbell-Shiller decomposition  Present value restrictions  Multivariate analysis: Vector Autoregression (VAR) models, the Kalman Filter Topic 3: Heteroscedasticity  Time-varying volatility in the data  Realized Variance  ARCH and GARCH models, application to Value-at-Risk Topic 4: Time series properties of the cross-section of stock returns  Single- and multifactor models  Economic factors: Models and data exploration  Statistical factors: Principal Components Analysis  Fama-MacBeth regressions and characteristics-based factors

R package for GARCH-MIDAS

Retrieve data from Binance and simulate high-frequency trading on them using the GARCH model

Replication of key GARCH model papers

The Tidymodels Extension for GARCH models

Univariate GARCH models in R

Repository for GARCH tutorial paper in RAC

The S&P 500 Market Index is analysed using popular statistical models such as SARIMA, ETS and GARCH. Additionally, a powerful open source forecasting package from Facebook, called Prophet, is also used.

BSc Thesis on the Garch-Midas model

:exclamation: This is a read-only mirror of the CRAN R package repository. rugarch — Univariate GARCH Models. Homepage: https://github.com/alexiosg/rugarch

Comparison of Markov-Switching GARCH models, namely symmetric GARCH, EGARCH, GJR-GARCH, performances in Value-at-Risk forecasting.

ARMA-GARCH Mixture Copula Mean-CVaR portfolio optimization project.

A hybrid model to predict the volatility of stock index with LSTM and GARCH-type input parameters

OxiDiviner: A production-ready, open-source Rust library for time series analysis and forecasting, especially for financial markets. Features a wide array of models including ARIMA, GARCH, ETS, Kalman Filters, Markov Regime-Switching, and more. Offers multiple API layers for all expertise levels.