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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 11. Value at Risk

In finance, implicitly or explicitly, rational investors always consider a trade-off between risk and returns. Usually, there is no ambiguity to measure returns. However, in terms of risk, we have numerous different measures such as using variance and standard deviation of returns to measure the total risk, individual stocks' beta, or portfolio beta to measure market risk. In the previous chapters, we know that the total risk has two components: market risk and firm-specific risks. To balance between the benefit of return and the cost of risk, many measures can be applied, such as the Sharpe ratio, Treynor ratio, Sortino ratio, and M2 performance measure (Modigliani and Modigliani performance measure). All of those risk measures or ratios have a common format: a trade-off between benefits expressed as risk-premium and risk expressed as a standard deviation, or beta, or Lower Partial Standard Deviation (LPSD). On the other hand, those measures do...

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