#Volatility-modeling
Showing 13 of 13 repositories tagged #volatility-modeling, ranked by stars
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.
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 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.
Python wrappers around QuantLib and Pandas to easily generate volatility surfaces
Measure market risk by CAViaR model
Neural network framework for volatility surface approximation and calibration. Supports rough Heston/Bergomi, random grids, multi-regime architectures.
Executive Programme in Algorithmic Trading by QuantInsti
Q-Variance Challenge: Can any continuous-time stochastic-volatility model reproduce q-variance?
Quantitative Finance Library & Option Trading Tool