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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

Changing frequency

The general rule of thumb for changing frequency can be broken down into the following:

  • Multiply/divide the log returns by the number of time periods.
  • Multiply/divide the volatility by the square root of the number of time periods.

In this recipe, we present an example of how to calculate the monthly realized volatilities for Apple using daily returns and then annualize the values.

The formula for realized volatility is as follows:

Realized volatility is frequently used for daily volatility using the intraday returns.

The steps we need to take are as follows:

  • Download the data and calculate the log returns.
  • Calculate the realized volatility over the months.
  • Annualize the values by multiplying by , as we are converting from monthly values.

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