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Python for Finance Cookbook

You're reading from   Python for Finance Cookbook Over 50 recipes for applying modern Python libraries to financial data analysis

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Product type Paperback
Published in Jan 2020
Publisher Packt
ISBN-13 9781789618518
Length 432 pages
Edition 1st Edition
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Author (1):
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Eryk Lewinson Eryk Lewinson
Author Profile Icon Eryk Lewinson
Eryk Lewinson
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Table of Contents (12) Chapters Close

Preface 1. Financial Data and Preprocessing 2. Technical Analysis in Python FREE CHAPTER 3. Time Series Modeling 4. Multi-Factor Models 5. Modeling Volatility with GARCH Class Models 6. Monte Carlo Simulations in Finance 7. Asset Allocation in Python 8. Identifying Credit Default with Machine Learning 9. Advanced Machine Learning Models in Finance 10. Deep Learning in Finance 11. Other Books You May Enjoy

Correcting for stationarity in time series

In this recipe, we investigate how to make a non-stationary time series stationary by using the following transformations:

  • Deflation: Accounting for inflation in monetary series using the Consumer Price Index (CPI)
  • Natural logarithm: Making the exponential trend closer to linear
  • Differencing: Taking the difference between the current observation and a lagged value (observation x time points before it)

We use the same data that we used in the Testing for stationarity in time series recipe. The conclusion from that recipe was that the time series of monthly gold prices from 2000-2011 was not stationary.

How to do it...

Execute the following steps to transform the series from non-stationary...

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