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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 15. Volatility, Implied Volatility, ARCH, 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. Most of the time, it is assumed 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...

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