Stochastic Financial Models

Journey through the world of stochastic finance from learning theory, underlying models, and derivations of financial models (stocks, options, portfolios) to the almost production-ready Python components under cover of stochastic finance. This book will show you the techniques to estimate potential financial outcomes using stochastic processes implemented with Python.

The aim of this book is twofold. The first goal is to summarize elementary and advanced topics on modern option pricing: from the basic models of the Black & Scholes theory to the more sophisticated approach based on Lévy processes and other jump processes.

This book provides a hands-on guide to how financial models are actually implemented and used in practice, on a daily basis, for pricing and risk-management purposes. It shows how to put these models into use in production while minimizing the cost of implementation and maximizing robustness and control. Addressing some of the most important and cutting-edge issues, it describes how to build the necessary models in order to risk manage all the costs involved in options fabrication within the world of equity derivatives and hybrids.