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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

Exercises

  1. What is the assumption behind don't put all your eggs in one basket?
  2. What are the measures of risk?
  3. How do you measure the co-moment between two stock returns?
  4. Why it is argued that correlation is a better measure than covariance when we evaluate the co-movements between two stocks?
  5. For two stocks A and B, with two pairs of (σA, σB) and (βA,βB), which pair is important when comparing their expected returns?
  6. Is it true that variance and correlation of historical returns possess the same sign?
  7. Find some inefficiency with the following code:
    import scipy as sp
    sigma1=0.02
    sigma2=0.05
    rho=-1
    n=1000
    portVar=10   # assign a big number
    tiny=1.0/n
    for i in sp.arange(n):
        w1=i*tiny
        w2=1-w1
        var=w1**2*sigma1**2 +w2**2*sigma2**2+2*w1*w2*rho*sigma1*sigma2
        if(var<portVar):
            portVar=var
            finalW1=w1
        #print(vol)
    print("min vol=",sp.sqrt(portVar), "w1=",finalW1)
  8. For a given set of σA, σB, and correlation...
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