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.

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.

Tool for pricing exotic options in the Heston model

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

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/

My Master's Thesis Project : MC LRV and ANNs for Financial Derivative Pricing

Simulation of Affine Jump Diffusions Using Broadie-Kaya Method

This repository implements Differential Machine Learning (DML) for pricing European options using the Heston model and introduces Differential Principal Component Analysis (Diff-PCA) to enhance computational efficiency.

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

Heston and BlackScholes models for option pricing and portfolio management

Portfolio Optimization with Feedback Strategies Based on Artificial Neural Networks