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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

Durbin-Watson

Durbin-Watson statistic is related auto-correlation. After we run a regression, the error term should have no correlation, with a mean zero. Durbin-Watson statistic is defined as:

Durbin-Watson

Here, et is the error term at time t, T is the total number of error term. The Durbin-Watson statistic tests the null hypothesis that the residuals from an ordinary least-squares regression are not auto-correlated against the alternative that the residuals follow an AR1 process. The Durbin-Watson statistic ranges in value from 0 to 4. A value near 2 indicates non-autocorrelation; a value toward 0 indicates positive autocorrelation; a value toward 4 indicates negative autocorrelation, see the following table:

Durbin-Watson Test

Description

Durbin-Watson

No autocorrelation

Towards 0

Positive auto-correlation

Towards 4

Negative auto-correlation

Table 8.3 Durbin-Watson Test

The following Python program runs a CAPM first by using daily data for IBM. The S&P500 is used as the index. The time period is from...

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