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

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

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

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

Stock markets are an essential component of the economy. Their prediction naturally arouses afascination in the academic and financial world. Indeed, financial time series, due to their widerange application fields, have seen numerous studies being published for their prediction. Some ofthese studies aim to establish whether there is a strong and predictive link between macroeconomicindicators and stock market trends and thus predict market returns. Stock market prediction howeverremains a challenging task due to uncertain noise. To what extent can macroeconomic indicatorsbe strong predictors of stock price ? Can they be used for stock trends modeling ? To answer thesequestions, we will focus on several time series forecasting models. We will on the one hand usestatistical time series models, more specifically the most commonly used time series approachesfor stock prediction : the Autoregressive Integrated Moving Average (ARIMA), the GeneralizedAutoregressive Conditional Heteroscedasticity (GARCH) and the Vector Autoregressive (VAR)approach. On the other hand, we will be using two deep learning models : the Long-Short TermMemory (LSTM) and the Gated Recurrent Unit (GRU) for our prediction task. In the final section ofthis paper, we look directly at companies to detect trends

Stock Prediction using LSTM, Linear Regression, ARIMA and GARCH models. Hyperparameter Optimization using Optuna framework for LSTM variants.

Developing hybrid deep learning models by integrating Neural networks with (s,e,t)GARCH models to predict volatility in the Indian Commodity Market. We evaluate the following DNN models: Multi-layer Perceptron (MLP), Convolutional Neural Network (CNN), and RNN with Long-Short Term Memory (LSTM-RNN) and RNN with Gated-Recurrent Unit (GRU-RNN).

Compilation of technical analysis tools (EMA, Bollinger bands), fundamental analysis, machine learning models (LSTM, Random forest, ARIMA, GARCH, Markov Regime Switching), traditional stock prediction tools (Monte Carlo), sentiment analysis (NLP) as well as portfolio optimization, with purpose to provide a better understanding regarding possible future price movements of the analyzed stock and propose an optimal asset allocation.

To create a data-web application deployed using the azure app service, which was made on Streamlit, the leading Pythonic data application service. On this website, we display candlestick plots of various stocks listed on the Nasdac, according to the option of the user; and utilize the Garch based time forecasting algorithm done using Seasonal arima model and conduct a virtual future prediction for the given stock, so as to be able to conduct non-pairs algorithmic trading using time forecasting and Garch-based deep learning.

Financial time series analysis and prediction have become an important area of re- search in today's world. Designing and pricing securities, construction of portfolios and other risk management strategies depends on the prediction of financial time se- ries. A financial time series often involve large dataset with complex interaction among themselves. A proper analysis of this data will give the investor better gains, but the existing methodologies focus on linear models (AR, MA, ARMA, ARIMA) and non- linear models (ARCH, GARCH, TAR). These models are not capable of identifying the complex interactions and latent dynamics existing within the data. Applying Deep learning methods to these types of data will give more accurate results than the existing methods. Deep learning architectures can identify the hidden patterns in the data and is also capable of exploiting the interactions existing within the data, which is, at least not possible by the existing financial models. The proposed work uses four different deep learning architectures (RNN, LSTM, CNN, and MLP) for predicting the minute wise stock price for NSE listed companies and compares the performance of the mod- els. The proposed method uses a sliding window based approach for predicting future values on a short-term basis. The performance of the models was quantified using error percentage.

Exploratory notebook . Techniques used: FFT, ARIMA, GARCH, Monte Carlo Simulations, fbprophet, LSTM, WaveNet.

This project compares the LSTM neural network and GARCH econometric models in predicting stock price volatility and estimating Value-at-Risk (VaR). The study evaluates these models' accuracy in forecasting Indonesian stock volatility using performance metrics like MAE, RMSE, and MAPE, aiming to guide investment risk assessment.

Written mainly in MATLAB, some part also written in R

The goal of this study is to predict prices for Cryptocurrencies using Time series analysis and machine learning techniques. The purpose of this project is to take a sneak peek into the future by forecasting the next 30 days' average daily Realized Volatility (RV) of ETH-BTC using 2 different approaches - the traditional econometric approach to volatility prediction of financial time series GARCH and state-of-the-art LSTM Neural Networks. Quantitative research methodology was used in this study and the The dataset Consist the historical data values of any any crypto-pair such as Open/Close/High/Low prices of any interval such as 15-minutes, Hourly, 1-day interval weekly, monthly. Dataset were obtained using the Binance API .

Hybrid LSTM-GARCH with VIX input model to predict S&P 500 volatility

DCC-GARCH written in R. ARIMAX & multivariate LSTM written 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

A Deep Learning framework for forecasting 21-day realized equity volatility. Implements a Global Hybrid LSTM architecture integrating GARCH(1,1) priors and Rogers-Satchell estimators to power an Optimized Risk Parity portfolio.

The model uses both single-layer and multi-layer perceptrons using the Hebb's algorithm. A sample dataset of 30 items is used for testing of binary classification. The accuracy is high, and the weights required will appear on executing the given code. A separate .py file spelled wrongly is added, which comprises of my own extra work comprising making a 3-layer neural network which acts as a classifier using Logistic Regression; and implements Gradient Descent for optimization. Time Dependant Neural Networks and Multilayer Perceptron Network have also been completed. Using statistic, I've also done the testing and implementation of various standard models, including Stock Forecasting, Time Series, Garch, arch and the arima model. I have also made a separate model to run a hybrid Garch-LSTM model.

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Predicting the accuracy of cryptocurrency volatility with ARMA-GARCH, Prophet and LSTM models

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

This project applies both deep learning models (LSTM and CNN-LSTM) and a classical econometric model (GARCH(1,1)) to the task of 5-day volatility forecasting using historical stock price data. It serves as a practical demonstration of how traditional and modern approaches can be integrated in financial time series analysis.

This repository contains a comprehensive analysis of NVIDIA Corporation's (NASDAQ: NVDA) stock performance using statistical methods and machine learning. Includes EDA, time series forecasting (ARIMA, LSTM), and volatility analysis (GARCH) to uncover trends and insights for market analysis and trading strategies.

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This project proposes a hybrid approach that combines an LSTM model and a GARCH(1,1) model, incorporating the VIX index as an exogenous variable, to leverage the strengths of both methodologies for improved 1-day-ahead volatility forecasts of the S&P 500.

An app that fits LSTM and GARCH model to the data.

Predict ETH-USD using ARMA-GARCH and LSTM model

Time Series Analysis following a hybrid approach using GARCH and LSTM Model

Monte Carlo, Geometric Brownian, MCMC, LSTM, BiLSTM, GARCH, Gym