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Python for Finance

You're reading from   Python for Finance If your interest is finance and trading, then using Python to build a financial calculator makes absolute sense. As does this book which is a hands-on guide covering everything from option theory to time series.

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Product type Paperback
Published in Apr 2014
Publisher
ISBN-13 9781783284375
Length 408 pages
Edition 1st Edition
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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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Toc

Table of Contents (14) Chapters Close

Preface 1. Introduction and Installation of Python 2. Using Python as an Ordinary Calculator FREE CHAPTER 3. Using Python as a Financial Calculator 4. 13 Lines of Python to Price a Call Option 5. Introduction to Modules 6. Introduction to NumPy and SciPy 7. Visual Finance via Matplotlib 8. Statistical Analysis of Time Series 9. The Black-Scholes-Merton Option Model 10. Python Loops and Implied Volatility 11. Monte Carlo Simulation and Options 12. Volatility Measures and GARCH Index

Chapter 11. Monte Carlo Simulation and Options

In finance, we study the trade-off between risk and return. The common definition of risk is uncertainty. For example, when evaluating a potential profitable project, we have to predict many factors in the life of the project, such as the annual sales, price of the final product, prices of raw materials, salary increase of employees, inflation rate, cost of borrowing, cost of new equity, and economic status. For those cases, the Monte Carlo simulation could be used to simulate many possible future outcomes, events, and their various combinations. In this chapter, we focus on the applications of the Monte Carlo simulation to price various options.

In this chapter, we will cover the following topics:

  • Generating random numbers from standard normal distribution and normal distribution
  • Generating random numbers from a uniform distribution
  • A simple application: estimate pi by the Monte Carlo simulation
  • Generating random numbers from a Poisson...
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