The pricing models and neural network representations used in part one of the paper "Empirical analysis of rough and classical stochastic volatility models to the SPX and VIX markets".

Implementation with a Jupyter Notebook of the VIX index modelization provided in its CBOE white paper.

I use a LSTM ( long short term memory model) model to predict the fluctuations of VIX index ( the index of 50ETF options), and trade the index of 50ETF accordingly.

A regime-aware portfolio optimization framework using Hidden Markov Models to identify market regimes from macroeconomic indicators (VIX, PMI, yield curve) and implement dynamic asset allocation strategies.

using one-day options with all strike price to calculated VIX value by using "“More than you ever wanted to know about volatility swaps” by Kresimir Demeterfi, Emanuel Derman, Michael Kamal and Joseph Zou, Goldman Sachs Quantitative Strategies Research Notes, March 1999. " approach

vixcpp/cli – Command Line Interface for vix.cpp. Scaffold new projects, run the server with hot reload, and generate models or resources.

Systematic multi-asset allocation strategy using Hidden Markov Models to identify VIX volatility regimes and dynamically rotate between TLT, GLD, and SPY

Codes used for "Joint calibration to SPX and VIX options with signature-based models" by Christa Cuchiero, Guido Gazzani, Janka Möller, Sara Svaluto-Ferro

Code for "Joint calibration to SPX and VIX options with signature-based models"

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

Prediction model for S&P 500 returns using features derived from the VIX index.

vixcpp/orm – Object-Relational Mapping (ORM) for vix.cpp. Allows defining C++ models mapped to databases and provides auto-generated CRUD operations.

Various multivariate, multistep LSTM models using SPX options data to forecast the CBOE VIX to improve future market volatility forecasts. Monte Carlo simulation and Facebook Prophet forecasts included for comparison.

No description available

Demonstrates a workflow that involves fetching, processing, storing, analyzing, and reporting on financial data using machine learning techniques within a Snowflake database environment

.NET bindings for VMware VIX API with Model Context Protocol server for AI-driven virtual machine automation

TradeAI is an intelligent trading assistant that combines machine learning with real-time market analysis to predict market movements with 90.58% accuracy. The system leverages a Random Forest model trained on 40+ global market indices, including Gold, VIX, and international bond rates.

This is a Deep Learning Model to predict price of Bank Nifty Index. Featues used are the closing price, Wavelet decomposition of the closing series , Advance Decline Ratio and India Vix

Volatility modelling project on VIX and VVIX

A two factor model to compute options and VIX derivatives

Built time-series models for forecasting the five VIX futures prices

📦 vixcpp/json – JSON serialization and deserialization for vix.cpp Efficient and lightweight JSON library for vix.cpp. Serialize C++ objects to JSON and deserialize JSON to strongly-typed C++ structures. Fully integrated with vix.cpp's runtime and model system.

Supplementary materials for paper "Improving Prediction Efficiency of Chinese Stock Index Futures Intraday Price by VIX-Lasso-GRU Model"

In this project, we implemented both Heston-Nandi GARCH and Component Heston-Nandi GARCH process for Modelling VIX futures.

Modelling S&P500 volatility with GARCH, GJR-GARCH, Beta-t-EGARCH, 2-Beta-t-EGARCH and comparison with the CBOE Volatility Index (VIX)

This project models short-term realized volatility using lagged realized volatilities, variance risk premium, and implied vol metrics (VIX, VVIX, skew) using models such as HAR-RV, LASSO, and Gradient Boosted Trees, with regime switching via a Hidden Markov Model.

Machine learning and Algorithmic trading Using NLP / Logistic Regression to Predict Future Stock Movement Program that allows a user to choose a stock from the S&P 500 or VIX run a logistic regression model to predict the price movement of this stock ‘s on the future trade based on current sentiments of Reuters news articles and social media post related to that organization. This platform performs data training using various models to provide best analysis to help traders decide whether to buy or sell the stock.

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.

We were interested in building a financial model to predict the S&P 500 (SPY), so we decided to design a Multiple Linear Regression model with a combination of common economic indicators, COVID data, and categorical data. These predictor variables included the daily volume on SPY, VIX (Volatility Index), unemployment rate, quarterly GDP, COVID cases, COVID deaths, COVID cases in China, sitting president, congress majority party, and the seasons. Using ANOVA testing, variable interaction, polynomial regression, and stepwise model selection we were able to implement a model with an R-squared value of ~0.99 over the time series of 2012 - 2020. Some interesting findings from the project: COVID deaths were not strongly correlated with the price of SPY; COVID testing and GDP were both heavily positively correlated with SPY; the VIX open and unemployment rate were both heavily negatively correlated with SPY closing price; the number of new COVID cases was observed to have a slightly positive correlation with the price of SPY even when considered in an interactive model with COVID deaths. We justify the last finding by the fact that SPY has previously experienced recessions when the US had 0 to relatively low COVID cases. Despite the fact that the number of COVID cases continues to grow at a similar or higher rate, SPY has also continued to grow. Which brings us to the question: What have been the differences between COVID-19's impact on the economy and the S&P 500?

Exploring VIX models