Data Science DS Concepts DS Languages Python

Volatility Check in Returns charts: FA9

 

Volatility Check in Returns charts

Hi All! In our previous tutorial, we had introduced stylized facts, what the five stylized facts are and had also covered stylized fact 1 – Distribution of returns – Is it non-Gaussian? In this tutorial, we will be covering stylized fact 2 – “Are Volatility clusters formed in returns chart?” and do Volatility Check in Returns charts using Python. New to this series? – Go to part 1 of Financial Analytics series to develop a good understanding on this.

Stylized Fact 2: Volatility Check in Returns charts

Let’s choose MSFT stock for our analysis. We’ll use yfinance to fetch stock data of MSFT.

In [1]:
# Importing the libraries
import pandas as pd
import yfinance as yf
import numpy as np
import matplotlib.pyplot as plt
In [2]:
# Downloading MSFT data from yfinance from 1st January 2010 to 31st March 2020
msftStockData = yf.download( 'MSFT',
                        start = '2010-01-01',
                        end = '2020-03-31',
                        progress = False)
In [3]:
# Checking what's in there the dataframe by loading first 5 rows
msftStockData.head()
Out[3]:
Open High Low Close Adj Close Volume
Date
2009-12-31 30.980000 30.990000 30.480000 30.480000 23.925440 31929700
2010-01-04 30.620001 31.100000 30.590000 30.950001 24.294369 38409100
2010-01-05 30.850000 31.100000 30.639999 30.959999 24.302216 49749600
2010-01-06 30.879999 31.080000 30.520000 30.770000 24.153070 58182400
2010-01-07 30.629999 30.700001 30.190001 30.450001 23.901886 50559700
In [4]:
# Checking what's in there the dataframe by loading last 5 rows
msftStockData.tail()
Out[4]:
Open High Low Close Adj Close Volume
Date
2020-03-24 143.750000 149.600006 141.270004 148.339996 148.339996 82516700
2020-03-25 148.910004 154.330002 144.440002 146.919998 146.919998 75638200
2020-03-26 148.399994 156.660004 148.369995 156.110001 156.110001 64568100
2020-03-27 151.750000 154.889999 149.199997 149.699997 149.699997 57042300
2020-03-30 152.440002 160.600006 150.009995 160.229996 160.229996 63420300
In [5]:
# Calculating log returns and obtaining column to contain it
msftStockData['Log Returns'] = np.log(msftStockData['Adj Close']/msftStockData['Adj Close'].shift(1)) 
In [6]:
# Checking what's in there the dataframe by loading first 5 rows
msftStockData.head()
Out[6]:
Open High Low Close Adj Close Volume Log Returns
Date
2009-12-31 30.980000 30.990000 30.480000 30.480000 23.925440 31929700 NaN
2010-01-04 30.620001 31.100000 30.590000 30.950001 24.294369 38409100 0.015302
2010-01-05 30.850000 31.100000 30.639999 30.959999 24.302216 49749600 0.000323
2010-01-06 30.879999 31.080000 30.520000 30.770000 24.153070 58182400 -0.006156
2010-01-07 30.629999 30.700001 30.190001 30.450001 23.901886 50559700 -0.010454
In [7]:
# Using back fill method to replace NaN values
msftStockData['Log Returns'] = msftStockData['Log Returns'].fillna(method = 'bfill')
msftStockData.head()
Out[7]:
Open High Low Close Adj Close Volume Log Returns
Date
2009-12-31 30.980000 30.990000 30.480000 30.480000 23.925440 31929700 0.015302
2010-01-04 30.620001 31.100000 30.590000 30.950001 24.294369 38409100 0.015302
2010-01-05 30.850000 31.100000 30.639999 30.959999 24.302216 49749600 0.000323
2010-01-06 30.879999 31.080000 30.520000 30.770000 24.153070 58182400 -0.006156
2010-01-07 30.629999 30.700001 30.190001 30.450001 23.901886 50559700 -0.010454
In [8]:
# Line chart of log return series
msftStockData['Log Returns'].plot(title = 'Daily log returns of MSFT', figsize = (14,10))
Out[8]:
<matplotlib.axes._subplots.AxesSubplot at 0x1fde5a46708>
download (1)

Thus, we can see that volatility clusters are formed in the line chart – there are some periods having higher returns and some periods have lower returns and they alternate forming a cycle of high-low-high. Thus, volatility doesn’t remain the same always.

So guys, we have just now explored Stylized fact 2 in this tutorial. In the next tutorial, we will explore stylized fact 3. Stay tuned! And don’t forget to subscribe to our YouTube channel.

 

 

One thought on “Volatility Check in Returns charts: FA9

Leave a Reply

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Back To Top
%d bloggers like this: