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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

Backtesting and stress testing

In finance, a stress test could be viewed as an analysis or simulation designed to determine the ability of a given financial instrument, such as a VaR to deal with an economic crisis. Since the first method to estimate a VaR is based on the assumption that stock returns following a normal distribution, its accuracy depends how far, in the real world, stock returns deviate from this assumption. A key component to the implementation of model-based risk management is model validation. That is, we need some way to determine whether the model chosen is accurate and performs consistently. This step is quite important both to firms and their regulators. According to Lopez (2000), we have the following table:

Name

Objectives

Methods

Backtesting

Compare observed outcomes with a model's expected output

Forecast evaluation established empirical issue with a large academic literature

Stress testing

Examples a model's expected outcomes under extreme conditions...

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