Quant Option Pricing - Exotic/Vanilla: Barrier, Asian, European, American, Parisian, Lookback, Cliquet, Variance Swap, Swing, Forward Starting, Step, Fader

Build and simulate jump equations like Gillespie simulations and jump diffusions with constant and state-dependent rates and mix with differential equations and scientific machine learning (SciML)

JumpDiff: Non-parametric estimator for Jump-diffusion processes for Python

An R Package for Monte Carlo Option Pricing Algorithm for Jump Diffusion Models with Correlational Companies

Hedging options by using Monte Carlo simulations or real data

This repository contains pricing methods for equity European and American options. Monte Carlo and tree methods have been implemented for Black Scholes extensions (standard, with discrete dividend, and with single and double Normal jumps for corporate actions). This repository also contains an implementation of a Differential Evolution algorithm…

Closed-Form Moment Derivation for Affine Jump Diffusions with State-Independent Jump Intensities

Jump Processes in JAX

Numerical integration of SDEs with variance reduction methods for Monte Carlo simulation

Comparative analysis of Value at Risk (VaR) measures using Black-Scholes pricing under different volatility models: jump diffusion, SABR and rough volatility.

Simulation of Affine Jump Diffusions Using Broadie-Kaya Method

Simulation of Affine Jump Diffusions Using Kyriakou-Brignone-Fusai Method

Implementation of a generative jump-diffusion model

A Python-based Monte Carlo pricing engine for FX derivatives, implementing the Bates Model (Stochastic Volatility + Jump-Diffusion) to account for geopolitical liquidity shocks and fragmented market architecture.

Pricing & calibration engine (15+ models; API + CLI + Streamlit UI)

Calibration Stability and Fragility of the Heston and SVJ Models in Energy Option Markets: Evidence from Hessian Spectral Analysis

MATLAB code is presented to estimate stock option prices using Monte Carlo simulations and closed-form solutions. Three models are employed to describe the dynamics of stock price movements: (1) Geometric Brownian Motion, (2) the jump-diffusion model of Merton (1976), and (3) the jump-diffusion model of Kou and Wang (2004).

A Python/C++ implementation of the Merton Jump Diffusion model using Monte Carlo simulation to generate stochastic equity price paths.