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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

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 Ï€ value by using Monte Carlo simulation
  • Simulate stock price...
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