Observe volatility clustering

Volatility clustering is frequently observed in financial market data, and it poses a challenge for time series modeling.

In this exercise, you will get familiar with the S&P 500 daily price dataset. You will calculate daily returns as the percentage price changes, plot the results and observe its behavior over time.

Historical S&P 500 daily price data has been preloaded in sp_price for you.

Cet exercice fait partie du cours

GARCH Models in Python

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Instructions

Calculate daily returns as percentage price changes and save it to the DataFrame sp_price in a new column called Return.
View the data by printing out the last 10 rows.
Plot the Return column and observe signs of volatility clustering.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Calculate daily returns as percentage price changes
sp_price['____'] = 100 * (sp_price['Close'].____())

# View the data
print(sp_price.____(____))

# plot the data
plt.plot(sp_price['____'], color = 'tomato', label = 'Daily Returns')
plt.legend(loc='upper right')
plt.show()

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