Covar = 1: Perfect (Positive) Correlation
Covar > 0: Positive Correlation (The two variables move in the same direction.)
Covar < 0: Negative Correlation (The two variables move in opposite directions.)
Covar = 0: No Correlation (The two variables are independent.)
Example:
x: house size
y: house price
import numpy as np
import pandas as pd
from pandas_datareader import data as wb
import matplotlib.pyplot as plt
tickers = ['PG', 'BEI.DE']
sec_data = pd.DataFrame()
for t in tickers:
sec_data[t] = wb.DataReader(t, data_source='yahoo', start='2007-1-1')['Adj Close']
sec_returns = np.log(sec_data / sec_data.shift(1))
PG_var = sec_returns['PG'].var()
PG_var
BEI_var = sec_returns['BEI.DE'].var()
BEI_var
# annual
PG_var_a = sec_returns['PG'].var() * 250
PG_var_a
# annual
BEI_var_a = sec_returns['BEI.DE'].var() * 250
BEI_var_a
cov_matrix = sec_returns.cov()
cov_matrix
# annual
cov_matrix_a = sec_returns.cov() * 250
cov_matrix_a
corr_matrix = sec_returns.corr()
corr_matrix