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.
This exercise is part of the course
GARCH Models in Python
Exercise instructions
- Calculate daily returns as percentage price changes and save it to the DataFrame
sp_price
in a new column calledReturn
. - View the data by printing out the last 10 rows.
- Plot the
Return
column and observe signs of volatility clustering.
Hands-on interactive exercise
Have a go at this exercise by completing this sample 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()