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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

Test of heteroskedasticity, Breusch, and Pagan

Breusch and Pagan (1979) designed a test to confirm or reject the null assumption that the residuals from a regression are homogeneous, that is, with a constant volatility. The following formula represents their logic. First, we run a linear regression of y against x:

Test of heteroskedasticity, Breusch, and Pagan

Here, y is the dependent variable, x is the independent variable, α is the intercept, β is the coefficient, and Test of heteroskedasticity, Breusch, and Pagan is an error term. After we get the error term (residual), we run the second regression:

Test of heteroskedasticity, Breusch, and Pagan

Assume that the fitted values from running the previous regression is t f, then the Breusch-Pangan (1979) measure is given as follows, and it follows a χ2 distribution with a k degree of freedom:

Test of heteroskedasticity, Breusch, and Pagan

The following example is borrowed from an R package called lm.test (test linear regression), and its authors are Hothorn et al. (2014). We generate a time series of x, y1 and y2. The independent variable is x, and the dependent variables are y1 and y2. By our design, y1 is...

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