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Python for Finance

You're reading from   Python for Finance Apply powerful finance models and quantitative analysis with Python

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Product type Paperback
Published in Jun 2017
Publisher
ISBN-13 9781787125698
Length 586 pages
Edition 2nd Edition
Languages
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Author (1):
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Yuxing Yan Yuxing Yan
Author Profile Icon Yuxing Yan
Yuxing Yan
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Table of Contents (17) Chapters Close

Preface 1. Python Basics FREE CHAPTER 2. Introduction to Python Modules 3. Time Value of Money 4. Sources of Data 5. Bond and Stock Valuation 6. Capital Asset Pricing Model 7. Multifactor Models and Performance Measures 8. Time-Series Analysis 9. Portfolio Theory 10. Options and Futures 11. Value at Risk 12. Monte Carlo Simulation 13. Credit Risk Analysis 14. Exotic Options 15. Volatility, Implied Volatility, ARCH, and GARCH Index

Importance of Monte Carlo Simulation

Monte Carlo Simulation, or simulation, plays a quite important role in finance with many applications. Assume that we intend to estimate Net Present Value (NPV) of a project. There are many uncertainties in the future, such as borrowing cost, price of our final products, raw materials, and so on. For just a few variables, we still could manage the task easily. However, if we face two dozen variables with uncertain future values, it is a headache to find a solution. Fortunately, Monte Carlo Simulation can be applied here. In Chapter 10, Options and Futures, we have learnt that the logic behind the Black-Scholes-Merton option models is the normality assumption for stock returns. Because of this, their closed-firm solution could be replicated by simulation. Another example is to randomly choose 50 stocks from 4,500 available stocks. Unlike vanilla options, such as the Black-Scholes-Merton model, there are no closed-form solutions for exotic options. Fortunately...

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