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Visualizing risk factor correlation

Investment banks heavily invested in mortgage-backed securities (MBS) before and during the financial crisis. This makes MBS a likely risk factor for the investment bank portfolio. You'll assess this using scatterplots between portfolio returns and an MBS risk measure, the 90-day mortgage delinquency rate mort_del.

mort_del is only available as quarterly data. So portfolio_returns first needs to be transformed from daily to quarterly frequency using the DataFrame .resample() method.

Your workspace contains both portfolio_returns for an equal-weighted portfolio and the delinquency rate mort_del variable. For the scatterplots, plot_average and plot_min are plot axes in your workspace--you'll add your scatterplots to them using the .scatter() method.

Cet exercice fait partie du cours

Quantitative Risk Management in Python

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Instructions

  • Transform the daily portfolio_returns data into average quarterly data using the .resample() and .mean() methods.
  • Add a scatterplot between mort_del and portfolio_q_average to plot_average. Is there a strong correlation?
  • Now create minimum quarterly data using .min() instead of .mean().
  • Add a scatterplot between mort_del and portfolio_q_min to plot_min.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Transform the daily portfolio_returns into quarterly average returns
portfolio_q_average = portfolio_returns.____('Q').____.dropna()

# Create a scatterplot between delinquency and quarterly average returns
plot_average.____(____, portfolio_q_average)

# Transform daily portfolio_returns returns into quarterly minimum returns
portfolio_q_min = ____.resample('____').____.dropna()

# Create a scatterplot between delinquency and quarterly minimum returns
plot_min.scatter(____, ____)
plt.show()
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