medjr0

medjr0's Stars

• albuzenet/Longstaff-Schwartz

  American option pricing using Longstaff Schwartz Algorithm under the Heston model

  Language:Jupyter Notebook71

• luphord/longstaff_schwartz

  A Python implementation of the Longstaff-Schwartz linear regression algorithm for the evaluation of call rights an American options.

  Language:Jupyter Notebook3913

• deepintomlf/mlfbook

  Language:Jupyter Notebook6635

• omartinsky/AmericanMonteCarlo

  Implementation of the Longstaff-Schwartz (American Monte Carlo) algorithm for pricing options and other derivatives with early-exercise features.

  Language:Jupyter Notebook255

• lanteignel93/longstaff_schwartz_pricer

  Solving Bermudan Put Option via Longstaff-Schwartz Method

  Language:Jupyter Notebook3

• davidalbertonogueira/MLP

  Simple multilayer perceptron c++ implementation.

  Language:C++7621

• jaungiers/LSTM-Neural-Network-for-Time-Series-Prediction

  LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data

  Language:Python4.8k2k

• aldodec/Black-Scholes-Option-Pricing-with-Monte-Carlo-

  The purpose of this notebook is to explore different methods for the valuation of options within the framework of the Black-Scholes pricing model with the use of Python. In particular, we will rely on Monte Carlo methods for the pricing of european call options, and compare the results with those obtained through the exact Black-Scholes solution.

  Language:Jupyter Notebook1812

• boyac/pyOptionPricing

  Option pricing based on Black-Scholes processes, Monte-Carlo simulations with Geometric Brownian Motion, historical volatility, implied volatility, Greeks hedging

  Language:Python28729

• arunp77/MonteCarlo-simulation

  Application to finance

  Language:HTML198

• BirdiD/Portfolio-Management

  In this repository, you will find a credit portfolio modeling with Monte Carlo simulation based method for the computation of credit-portfolio loss-distributions and for the estimation of various risk measures. Four principal risk measures are taken into account : Value At Risk (VaR), Expected Shortfall (ES), Expected Loss (EL) and Unexpected Loss (UL). The notebook also includes credit derivatives which are synthetic contracts to buy or sell protection against credit-related losses. There's also an interactive dashboard allowing you to choose parameters for your credit portfolio modeling

  Language:Jupyter Notebook61

• yineme/Credit_Risk_modelling

  Language:Jupyter Notebook1314

• duffau/RNN_GARCH

  Estimating Value-at-Risk with a recurrent neural network (Jordan type) GARCH model

  Language:Python6529