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

Machine learning for financial risk management

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.

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.

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 …

🚀 A comprehensive project analyzing Big Tech stock prices using time series analysis, volatility modeling, and macroeconomic indicators. Featuring interactive dashboards and automated reporting! 📈💼