Python implementation of pricing analytics and Monte Carlo simulations for stochastic volatility models including log-normal SV model, Heston

Python code of commonly used stochastic models for Monte-Carlo simulations

Pricing and Analysis of Financial Derivative by Credit Suisse using Monte Carlo, Geometric Brownian Motion, Heston Model, CIR model, estimating greeks such as delta, gamma etc, Local volatility model incorporated with variance reduction.(For MH4518 Project)

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.

This repository contains various models and techniques for pricing financial options. The focus is on implementing the Black-Scholes model and some of its extensions (e.g. Heston) , visualizing implied volatility surfaces, and utilizing Monte Carlo simulations for exotic option pricing. PYPI pckg: https://pypi.org/project/tiny-pricing-utils/1.0.3/

Full-stack Bitcoin options pricing dashboard using the Heston Stochastic Volatility Model. Features MLE parameter calibration, Monte Carlo simulation, multi-method pricing (Heston, MC, Black-Scholes), real-time Deribit data, and interactive visualization. Built with FastAPI + React.

Project that uses Monte Carlo simulations to price American put options with changing volatility and evaluates associated risk measures.

The project aims to compare the effectiveness of the Heston model and WGAN-GP in modeling financial time series data.

Volatility surface modelling library

Quantification of risk metrics (VaR, ES, Loss Distribution, Hedging Error) via Monte Carlo simulation of stochastic models (GBM, Heston) with parameter estimation (MLE) on historical data.

Calibration of Stochastic Volatility models on implied volatility smiles

This project aims to implement the Heston model (1993) and apply it to price Equity Variance & Volatility Swaps.

Portfolio Optimization with Feedback Strategies Based on Artificial Neural Networks

A high-performance neural engine for calibrating the Heston-Hull-White stochastic model. Features 50-core parallel data generation and 3D risk-sensitivity (Vega) manifolds.

Tool for pricing exotic options in the Heston model

SPX Option Implied Volatility Surface using SVI Parameterisation, its variants and the Heston Stochastic Volatiltiy Model. Implements and studies interpolation and smoothing techniques used by Bloomberg for Equity Option Vol Surface Construction.

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

Simulation of Affine Jump Diffusions Using Broadie-Kaya Method