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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

Estimating value-at-risk using Monte Carlo

Value-at-risk is a very important financial metric that measures the risk associated with a position, portfolio, and so on. It is commonly abbreviated to VaR, not to be confused with Vector Autoregression. VaR reports the worst expected loss – at a given level of confidence – over a certain horizon under normal market conditions. The easiest way to understand it is by looking at an example. Let's say that the 1-day 95% VaR of our portfolio is $100. This means that 95% of the time (under normal market conditions), we will not lose more than $100 by holding our portfolio over one day.

It is common to present the loss given by VaR as a positive (absolute) value. That is why in this example, a VaR of $100 means losing no more than $100.

There are several ways to calculate VaR, some of which are:

  • Parametric Approach (Variance...
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