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Python for Finance

You're reading from   Python for Finance If your interest is finance and trading, then using Python to build a financial calculator makes absolute sense. As does this book which is a hands-on guide covering everything from option theory to time series.

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Product type Paperback
Published in Apr 2014
Publisher
ISBN-13 9781783284375
Length 408 pages
Edition 1st Edition
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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 (14) Chapters Close

Preface 1. Introduction and Installation of Python FREE CHAPTER 2. Using Python as an Ordinary Calculator 3. Using Python as a Financial Calculator 4. 13 Lines of Python to Price a Call Option 5. Introduction to Modules 6. Introduction to NumPy and SciPy 7. Visual Finance via Matplotlib 8. Statistical Analysis of Time Series 9. The Black-Scholes-Merton Option Model 10. Python Loops and Implied Volatility 11. Monte Carlo Simulation and Options 12. Volatility Measures and GARCH Index

Test of heteroskedasticity, Breusch, and Pagan (1979)

Breusch and Pagan (1979) designed a test to confirm or reject the null assumption that the residuals from a regression is 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 (1979)

Here, y is the independent variable, x is the independent variable, α is the intercept, β is the coefficient and Test of heteroskedasticity, Breusch, and Pagan (1979) is an error term. After we get the error term (residual), we run the second regression:

Test of heteroskedasticity, Breusch, and Pagan (1979)

Assume that the fitted values from running the previous regression is Test of heteroskedasticity, Breusch, and Pagan (1979) , 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 (1979)

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,...

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