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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

Introduction to VaR

Up to now, we have several ways to evaluate risk for an individual stock or a portfolio, such as variance, standard deviation of returns to measure the total risk, or beta to measure the market risk of a portfolio or individual stocks. On the other hand, many CEOs prefer a simple measure called Value at Risk (VaR), which has the simple definition given here:

"The maximum loss with a confidence level over a predetermined period."

From the preceding definition, it has three explicit factors plus one implied one. The implied factor or variable is our current position, or the value of our current portfolio or individual stock(s). The preceding statement offers the maximum possible loss in the future and this is the first factor. The second one is over a specific time period. Those two factors are quite common. However, the last factor is quite unique: with a confidence level or probability. Here are a few examples:

  • Example #1: On February 7, 2017, we own 300 shares...
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