Machine learning

Machine learning in derivative pricing employs complex algorithms to predict future derivative prices, drawing from a vast dataset of historical trading data. By modeling market dynamics and identifying patterns, it provides more accurate price forecasts than traditional models. This not only reduces financial risk but also optimizes trading strategies. Furthermore, it provides insights into market behavior, assisting in the development of more resilient financial systems.

Geometric Brownian motion

We must model the underlying equities before estimating the price of derivative instruments based on their value. The geometric Brownian motion (GBM), also called the Wiener process, is the method often uses to model the stochastic process of a Brownian motion, driving the future values of an asset. It helps create trajectories that the asset price of the underlying stock may take in the future.

A stochastic or random process, here defined as the time-dependent...