Graphical presentation of volatility clustering

One of the observations is labeled as volatility clustering, which means that high volatility is usually followed by a high-volatility period, while low volatility is usually followed by a low-volatility period. The following program shows this phenomenon by using S&P500 daily returns from 1988 to 2006. Note that, in the following code, in order to show 1988 on the x axis, we add a few months before 1988:

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.finance import quotes_historical_yahoo_ochl as getData
#
ticker='^GSPC'
begdate=(1987,11,1)
enddate=(2006,12,31)
#
p = getData(ticker, begdate, enddate,asobject=True, adjusted=True)
x=p.date[1:] 
ret = p.aclose[1:]/p.aclose[:-1]-1
#
plt.title('Illustration of volatility clustering (S&P500)') 
plt.ylabel('Daily returns')
plt.xlabel('Date') 
plt.plot(x,ret)
plt.show()

This program is inspired by the graph drawn by M.P. Visser; refer...