Book Image

Hands-On Simulation Modeling with Python

By : Giuseppe Ciaburro
Book Image

Hands-On Simulation Modeling with Python

By: Giuseppe Ciaburro

Overview of this book

Simulation modeling helps you to create digital prototypes of physical models to analyze how they work and predict their performance in the real world. With this comprehensive guide, you'll understand various computational statistical simulations using Python. Starting with the fundamentals of simulation modeling, you'll understand concepts such as randomness and explore data generating processes, resampling methods, and bootstrapping techniques. You'll then cover key algorithms such as Monte Carlo simulations and Markov decision processes, which are used to develop numerical simulation models, and discover how they can be used to solve real-world problems. As you advance, you'll develop simulation models to help you get accurate results and enhance decision-making processes. Using optimization techniques, you'll learn to modify the performance of a model to improve results and make optimal use of resources. The book will guide you in creating a digital prototype using practical use cases for financial engineering, prototyping project management to improve planning, and simulating physical phenomena using neural networks. By the end of this book, you'll have learned how to construct and deploy simulation models of your own to overcome real-world challenges.
Table of Contents (16 chapters)
Section 1: Getting Started with Numerical Simulation
Section 2: Simulation Modeling Algorithms and Techniques
Section 3: Real-World Applications

Chapter 8: Using Simulation Models for Financial Engineering

The explosive entry of systems based on artificial intelligence and machine learning has opened up new scenarios for the financial sector. These methods can bring benefits such as user rights protections, as well as macroeconomic benefits.

Monte Carlo methods find a natural application in finance for the numerical resolution of pricing and problems in covered call options. Essentially, these methods consist of simulating a given process or phenomenon using a given mathematical law and a sufficiently large set of data, created randomly from distributions that adequately represent real variables. The idea is that, if an analytical study is not possible, or adequate experimental sampling is not possible or convenient, the numerical simulation of the phenomenon is used. In this chapter, we will look at practical cases of using simulation methods in a financial context. You will learn how to use Monte Carlo methods to predict...