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Python for Finance

You're reading from   Python for Finance Apply powerful finance models and quantitative analysis with Python

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Product type Paperback
Published in Jun 2017
Publisher
ISBN-13 9781787125698
Length 586 pages
Edition 2nd Edition
Languages
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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 (17) Chapters Close

Preface 1. Python Basics FREE CHAPTER 2. Introduction to Python Modules 3. Time Value of Money 4. Sources of Data 5. Bond and Stock Valuation 6. Capital Asset Pricing Model 7. Multifactor Models and Performance Measures 8. Time-Series Analysis 9. Portfolio Theory 10. Options and Futures 11. Value at Risk 12. Monte Carlo Simulation 13. Credit Risk Analysis 14. Exotic Options 15. Volatility, Implied Volatility, ARCH, and GARCH Index

VaR for portfolios

In Chapter 9, Portfolio Theory, it was shown that when putting many stocks in our portfolio, we could reduce or eliminate firm-specific risk. The formula to estimate an n-stock portfolio return is given here:

VaR for portfolios

Here Rp,t is the portfolio return at time t, wi is the weight for stock i, and Ri, t is the return at time t for stock i. When talking about the expected return or mean, we have a quite similar formula:

VaR for portfolios

Here, VaR for portfolios is the mean or expected portfolio return, VaR for portfolios is the mean or expected return for stock i. The variance of such an n-stock portfolio is given here:

VaR for portfolios

Here, VaR for portfolios is the portfolio variance, σi,j is covariance between stocks i and j; see the following formula:

VaR for portfolios

The correlation between stocks i and j, ρi,j, is defined here:

VaR for portfolios

When stocks are not positively perfectively correlated, combining stocks would reduce our portfolio risk. The following program shows that the VaR of the portfolio is not simply the summation or weighted VaR of individual stocks within the portfolio...

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