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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 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

Chapter 12. Volatility Measures and GARCH

In finance, we know that risk is defined as uncertainty since we are unable to predict the future more accurately. Based on the assumption that prices follow a lognormal distribution and returns follow a normal distribution, we could define risk as standard deviation or variance of the returns of a security. We call this our conventional definition of volatility (uncertainty). Since a normal distribution is symmetric, it will treat a positive deviation from a mean in the same manner as it would a negative deviation. This is against our conventional wisdom since we treat them differently. To overcome this, Sortino (1983) suggests a lower partial standard deviation. Up to now, we assume that the volatility of a time series is a constant. Obviously this is not true. Another observation is volatility clustering, which means that high volatility is usually followed by a high-volatility period, and this is true for low volatility that is usually...

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