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Hands-On Simulation Modeling with Python

You're reading from   Hands-On Simulation Modeling with Python Develop simulation models to get accurate results and enhance decision-making processes

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781838985097
Length 346 pages
Edition 1st Edition
Languages
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Author (1):
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Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
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Table of Contents (16) Chapters Close

Preface 1. Section 1: Getting Started with Numerical Simulation
2. Chapter 1: Introducing Simulation Models FREE CHAPTER 3. Chapter 2: Understanding Randomness and Random Numbers 4. Chapter 3: Probability and Data Generation Processes 5. Section 2: Simulation Modeling Algorithms and Techniques
6. Chapter 4: Exploring Monte Carlo Simulations 7. Chapter 5: Simulation-Based Markov Decision Processes 8. Chapter 6: Resampling Methods 9. Chapter 7: Using Simulation to Improve and Optimize Systems 10. Section 3: Real-World Applications
11. Chapter 8: Using Simulation Models for Financial Engineering 12. Chapter 9: Simulating Physical Phenomena Using Neural Networks 13. Chapter 10: Modeling and Simulation for Project Management 14. Chapter 11: What's Next? 15. Other Books You May Enjoy

Summary

In this chapter, we applied the concepts of simulation based on Monte Carlo methods and, more generally, on the generation of random numbers to real cases related to the world of financial engineering. We started by defining the model based on Brownian motion, which describes the uninterrupted and irregular movement of small particles when immersed in a fluid. We learned how to describe the mathematical model, and then we derived a practical application that simulates a random walk as a Wiener process.

Afterward, we dealt with another practical case of considerable interest, that is, how to use Monte Carlo methods to predict the stock prices of the famous Amazon company. We started to explore the trend of Amazon sharess in the last 10 years, and we performed simple statistics to extract preliminary information on any trends that we confirmed through visual analysis. Subsequently, we learned to treat the trend of stock prices as a time series, calculating the daily return...

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