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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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Table of Contents (14) Chapters Close

Preface 1. Introduction and Installation of Python FREE CHAPTER 2. Using Python as an Ordinary Calculator 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

Summary

In this chapter, we discussed several types of distributions: normal, standard normal, lognormal, and Poisson. Since the assumption that stocks follow a lognormal distribution and returns follow a normal distribution is the cornerstone for option theory, the Monte Carlo simulation is used to price European options. Under certain scenarios, Asian options might be more effective in terms of hedging. Exotic options are more complex than the vanilla options since the former have no closed-form solution, while the latter could be priced by the Black-Scholes-Merton option model. One way to price these exotic options is to use the Monte Carlo simulation. The Python programs to price an Asian option and lookback options are discussed in detail.

In the next chapter, we will discuss various volatility measures, such as our conventional standard deviation, Lower Partial Standard Deviation (LPSD). Using the standard deviation of returns as a risk measure is based on a critical assumption that...

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