Calculating Diversifiable and Non-Diversifiable Risk of a Portolio



In [1]:

    
import numpy as np
import pandas as pd
from pandas_datareader import data as wb
import matplotlib.pyplot as plt



In [2]:

    
weights = np.array([32.94, 30.26, 11.77, 13.02, 9.00, 3.00])



In [3]:

    
tickers = ['ITSA3', 'GRND3', 'HGTX3', 'PSSA3', 'WEGE3', 'BVMF3']

sec_data = pd.DataFrame()

for t in tickers:
    sec_data[t] = wb.DataReader(t, data_source='google', start='2010-1-1')['Close']



In [8]:

    
sec_data.tail()



In [16]:

    
(sec_data / sec_data.iloc[0] * 100).plot(figsize = (15, 6))
plt.show()

Diversifiable Risk



In [10]:

    
sec_returns = np.log(sec_data / sec_data.shift(1))



In [17]:

    
ITSA3_var_a = sec_returns[['ITSA3']].var() * 250
GRDN3_var_a = sec_returns[['GRND3']].var() * 250
PSSA3_var_a = sec_returns[['PSSA3']].var() * 250
HGTX3_var_a = sec_returns[['HGTX3']].var() * 250
WEGE3_var_a = sec_returns[['WEGE3']].var() * 250
BVMF3_var_a = sec_returns[['BVMF3']].var() * 250

print(ITSA3_var_a, GRDN3_var_a, PSSA3_var_a, HGTX3_var_a, WEGE3_var_a, BVMF3_var_a)









    



ITSA3    0.199
dtype: float64 GRND3    0.095292
dtype: float64 PSSA3    0.108787
dtype: float64 HGTX3    0.215836
dtype: float64 WEGE3    0.222465
dtype: float64 BVMF3    0.193582
dtype: float64



In [ ]:

	ITSA3	GRND3	HGTX3	PSSA3	WEGE3	BVMF3
Date
2017-06-05	8.50	26.04	21.04	31.55	18.89	18.65
2017-06-06	8.64	26.05	21.38	31.06	18.76	18.55
2017-06-07	8.74	25.90	21.11	31.50	18.90	18.76
2017-06-08	8.60	25.86	20.50	31.33	18.74	18.59
2017-06-09	8.57	24.01	20.00	30.11	18.41	18.55