jump-diffusion

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

Dynamic mean-variance allocation under regime-switching jump-diffusions with wealth floors - fully analytical (SSRN 6534579)

Jump Processes in JAX

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…

European option pricing under Black-Scholes-Merton: prices, Greeks, implied volatility, and sensitivity surfaces. Typed, tested, NumPy-only — no SciPy, so it runs in a browser. The published document shows where the model breaks, generating a volatility smile and inverting it.

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

Markov Models generating trading ranges

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

A quantitative trading framework for options pricing, volatility surface calibration, and automated statistical arbitrage using Heston and Bates models.

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.

A Universal Framework for Multistable Stochastic Control.

CPPI gap risk modeling: closed-form, Monte Carlo, dynamic VaR multipliers, and put hedging under BS, Heston, and Kou

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

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

Monte Carlo Risk Engine — GARCH(1,1), Merton Jump-Diffusion & Cornish-Fisher VaR across 960+ instruments | FastAPI + Chart.js + Flutter

Personal Investment Monte Carlo Simulation