OIPD computes the market's expectations about the probable future prices of an asset, based on information contained in options data.

Predicting stock prices using Geometric Brownian Motion and the Monte Carlo method

The Breeden-Litzenberger formula, proposed by Douglas T. Breeden and Robert H. Litzenberger in 1978, is a method used to extract the implied risk-neutral probability density function from observed option prices

Sieve estimation of state price density implied by option prices

The main focus of this repository is to analysis the fair price and the risk of the Auto-callable Reverse Convertible issued by Credit Suisse AG on 24/10/2017

Computes implied measures for inflation expectations derived from inflation option data

Auxiliary material course Quantitative Finance (Tilburg University)

Pricing European options using explicit Black-Scholes solution and Monte-Carlo method. Producing heat maps which display the variability in option price for varying volatility and spot price.

A Python library for pricing options under various risk-neutral density assumptions, computing option-implied densities, and extracting model parameters from market data.

Inferring subjective probabilities from option prices via time-varying stochastic discount functions. Resolves the monotonicity puzzle while achieving superior return prediction through non-local pricing kernel estimation.