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Mastering Pandas for Finance

You're reading from   Mastering Pandas for Finance Master pandas, an open source Python Data Analysis Library, for financial data analysis

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Product type Paperback
Published in May 2015
Publisher Packt
ISBN-13 9781783985104
Length 298 pages
Edition 1st Edition
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Author (1):
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Michael Heydt Michael Heydt
Author Profile Icon Michael Heydt
Michael Heydt
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Table of Contents (11) Chapters Close

Preface 1. Getting Started with pandas Using Wakari.io FREE CHAPTER 2. Introducing the Series and DataFrame 3. Reshaping, Reorganizing, and Aggregating 4. Time-series 5. Time-series Stock Data 6. Trading Using Google Trends 7. Algorithmic Trading 8. Working with Options 9. Portfolios and Risk Index

Value at Risk


Value at Risk (VaR) is a statistical technique used to measure the level of financial risk within an investment portfolio, over a specific timeframe. It measures in three variables—the amount of potential loss, the probability of the loss, and the timeframe.

As an example, a portfolio may have a 1-month 5 percent VaR of $1 million. This means that there is a 5 percent probability that the portfolio will fall in value by more than $1 million over a 1-month period. Likewise, it also means that a $1 million loss should be expected once every 20 months.

The most common means of measuring VaR is by calculating the volatility. There are three common means of calculating the volatility: using historical data, variance-covariance, and the Monte Carlo simulation. We will examine the variance-covariance method here, as there is a straightforward formulation for the VaR once you have historical returns.

VaR assumes that returns are normally distributed. The returns for a stock or portfolio...

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