Book Image

Python for Finance - Second Edition

By : Yuxing Yan
5 (1)
Book Image

Python for Finance - Second Edition

5 (1)
By: Yuxing Yan

Overview of this book

This book uses Python as its computational tool. Since Python is free, any school or organization can download and use it. This book is organized according to various finance subjects. In other words, the first edition focuses more on Python, while the second edition is truly trying to apply Python to finance. The book starts by explaining topics exclusively related to Python. Then we deal with critical parts of Python, explaining concepts such as time value of money stock and bond evaluations, capital asset pricing model, multi-factor models, time series analysis, portfolio theory, options and futures. This book will help us to learn or review the basics of quantitative finance and apply Python to solve various problems, such as estimating IBM’s market risk, running a Fama-French 3-factor, 5-factor, or Fama-French-Carhart 4 factor model, estimating the VaR of a 5-stock portfolio, estimating the optimal portfolio, and constructing the efficient frontier for a 20-stock portfolio with real-world stock, and with Monte Carlo Simulation. Later, we will also learn how to replicate the famous Black-Scholes-Merton option model and how to price exotic options such as the average price call option.
Table of Contents (23 chapters)
Python for Finance Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Index

Chapter 12. Monte Carlo Simulation

Monte Carlo Simulation is an extremely useful tool in finance. For example, because we can simulate stock price by drawing random numbers from a lognormal distribution, the famous Black-Scholes-Merton option model can be replicated. From Chapter 9, Portfolio Theory, we have learnt that by adding more stocks into a portfolio, the firm specific risk could be reduced or eliminated. Via simulation, we can see the diversification effect much clearly since we can randomly select 50 stocks from 5,000 stocks repeatedly. For capital budgeting, we can simulate over several dozen variables with uncertain future values. For those cases, simulation can be applied to generate many possible future outcomes, events, and various types of combinations. In this chapter, the following topics will be covered:

  • Generating random numbers drawn from a normal, uniform, and Poisson distributions

  • Estimating π value by using Monte Carlo simulation

  • Simulate stock price movement with a...