Historical expected shortfall
Expected Shortfall, otherwise known as CVaR, or conditional value at risk, is simply the expected loss of the worst case scenarios of returns.
For example, if your portfolio has a VaR(95) of -3%, then the CVaR(95) would be the average value of all losses exceeding -3%.
Returns data is available (in percent) in the variable StockReturns_perc. var_95 from the previous exercise is also available in your workspace.
This is a part of the course
“Introduction to Portfolio Risk Management in Python”
Exercise instructions
- Calculate the average of returns in
StockReturns_percwhereStockReturns_percis less than or equal tovar_95and assign it tocvar_95. - Plot the histogram of sorted returns (
sorted_rets) using theplt.hist()function.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Historical CVaR 95
cvar_95 = ____
print(cvar_95)
# Sort the returns for plotting
sorted_rets = sorted(StockReturns_perc)
# Plot the probability of each return quantile
____(____, density=True, stacked=True)
# Denote the VaR 95 and CVaR 95 quantiles
plt.axvline(x=var_95, color="r", linestyle="-", label='VaR 95: {0:.2f}%'.format(var_95))
plt.axvline(x=cvar_95, color='b', linestyle='-', label='CVaR 95: {0:.2f}%'.format(cvar_95))
plt.show()This exercise is part of the course
Introduction to Portfolio Risk Management in Python
Evaluate portfolio risk and returns, construct market-cap weighted equity portfolios and learn how to forecast and hedge market risk via scenario generation.
In this chapter, you will learn two different methods to estimate the probability of sustaining losses and the expected values of those losses for a given asset or portfolio of assets.
Exercise 1: Estimating tail riskExercise 2: Historical drawdownExercise 3: Historical value at riskExercise 4: Historical expected shortfallExercise 5: VaR extensionsExercise 6: Changing VaR and CVaR quantilesExercise 7: Parametric VaRExercise 8: Scaling risk estimatesExercise 9: Random walksExercise 10: A random walk simulationExercise 11: Monte Carlo simulationsExercise 12: Monte Carlo VaRExercise 13: Understanding riskWhat is DataCamp?
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