Derivative Pricing Models implemented in Python
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Updated
Oct 13, 2020 - Jupyter Notebook
Derivative Pricing Models implemented in Python
Financial Engineering
An interactive app on the Black Scholes Option Pricing Model, Option Greeks.
A kdb library to call Quantlib C++ library via embedpy with a simple interface (similar to R Quantlib interface)
Derivatives Pricing
The binomial tree model is a commonly used approach for pricing derivatives, such as options. The basic idea behind the model is to create a tree of possible stock prices over time, based on a set of input parameters
A GUI Based executable app which takes input values from the end user and return CALL/PUT option prices. Currently working on scaling the app to take datasets as input and store resultant option pricing in a file.
A method for generating n random values, sampled from a Levy alpha stable distribution. It is useful for Levy adjusted random walks and financial risk modelling.
Black-Scholes-Merton European Options Pricing
University Project: simulation techniques to price derivatives. It will involve Monte-Carlo, variance-reduction techniques, and advanced simulation methods.
This repository holds my journey thought derivatives, pricing, maths and volatility associated.
The repository for The Derivative Pricing in Practice Subject's Assignments
Monte Carlo Simulation Option Pricing application in various programming languages and Excel
The repository contains various models for pricing options, including the popular Black-Scholes model, as well as more advanced models that take into account stochastic volatility, jumps and other factors.
In this section we will explore several contributions on Financial Derivatives valuation
This repository provides TensorFlow compatible code for some stochastic volatility models widely used in derivatives pricing.
Black-Scholes derivatives pricing model implementation in Google Sheets.
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