There are 8 repositories under volatility-modeling topic.
GARCH and Multivariate LSTM forecasting models for Bitcoin realized volatility with potential applications in crypto options trading, hedging, portfolio management, and risk management
Python implementation of pricing analytics and Monte Carlo simulations for stochastic volatility models including log-normal SV model, Heston
A vectorized implementation of py_vollib, that supports numpy arrays and pandas Series and DataFrames.
Market Data & Derivatives Pricing Tutorial based on Jupyter notebooks
Curso diseñado para proporcionar una comprensión muy profunda del Trading Cuantitativo, fusionando los principios de Ingeniería Financiera con el poder de la Inteligencia Artificial, todo implementado en Python. Desarrollarás algoritmos y estrategias avanzadas que aprovechan datos financieros y técnicas de Inteligencia Artificial.
Traditionally, volatility is modeled using parametric models. This project focuses on predicting EUR/USD volatility using more flexible, machine-learning methods.
A package for online distributional learning.
SABR Implied volatility asymptotics
Undergraduate thesis, Seoul National University Dept. of Economics — "Modeling Volatility and Risk Spillover Between the Financial Markets of US and China Using GARCH Value-at-Risk Forecasting and Granger Causality."
Implementation with a Jupyter Notebook of the VIX index modelization provided in its CBOE white paper.
C++ option pricing library on vanillas & exotics, Python volatility calibration library
Python wrappers around QuantLib and Pandas to easily generate volatility surfaces
Implementation of option pricing models using Numba that performs better. This entire project has utilized as little libraries as possible, even though certain models have their own Machine Learning Model with assessment and performance.
SEW_Trade is an #MT5 Expert Advisor using #SMA/EMA crossovers with #Waddah Attar Explosion confirmation. It features an interactive trade panel, supports up to 5 Take Profits, real-time control, and full MQL5 integration with error handling.
Measure market risk by CAViaR model
Collection of numerical methods for high frequency data, in Python notebooks
The project aims to profile stocks with similar weekly percentage returns using K-Means Clustering. The project calculates realized volatility for each stock and predicts realized volatility for each stock using classical volatility models and machine learning models and comparing their performance. This is a capstone project for CIVE 7100 Time Series and Geospatial Data Sciences.
🚀 A comprehensive project analyzing Big Tech stock prices using time series analysis, volatility modeling, and macroeconomic indicators. Featuring interactive dashboards and automated reporting! 📈💼
Neural network framework for volatility surface approximation and calibration. Supports rough Heston/Bergomi, random grids, multi-regime architectures.
IBOVESPA volatility forecasting
MSc Finance dissertation project at Newcastle University. This project focused on forecasting the volatility of exchange rates involving the Great British Pound using EWMA, GARCH-type and Implied Volatility models.
Daily Volatility trading strategies on Index Equity Options
Quantitative Finance Library & Option Trading Tool
Stress Testing Financial Portfolios using S&P 500 Stock Data from Kaggle.
项目主要构建了多混频Realized GARCH-MIDAS-X模型,结合社交媒体情绪和高频数据,与 不加社交媒体情绪指标的模型相比,加入后的模型显著提升了内地低碳市场波动预测的准确性。通过稳健 性检验,证明了研究结果的可靠性。该研究丰富了市场波动模型,并为低碳投资和宏观调控提供了参考。
Study on volatility transmission and protuberance among developed and developing stock markets using multivariate GARCH
Executive Programme in Algorithmic Trading by QuantInsti
Contains financial studies work, including capital markets, corporate finance and other topics.
computes Volatility Spillover between Cryptocurrency (BTC/USD) and S&P 500 index
In this repo you will find some tools related to pricing and risk measurement of options. You can find tools to calculate the price of an option like de Black-Scholes or Heston Model, or to get implied volatilities.
Code for the paper "Realized Semi(Co)Variation: Signs that All Volatilities are Not Created Equal"
This repository includes the scripts to replicate the results of my paper entitled "A False Discovery Rate Approach to Optimal Volatility Forecasting Model Selection".
A diffusion model for generating arbitrage-free implied volatility surface forecasts.