Execute the following steps to implement the CAPM in Python.

1. Import the libraries:

import pandas as pd
import yfinance as yf
import statsmodels.api as sm

2. Specify the risky asset and the time horizon:

RISKY_ASSET = 'AMZN'
MARKET_BENCHMARK = '^GSPC'
START_DATE = '2014-01-01'
END_DATE = '2018-12-31'

3. Download the necessary data from Yahoo Finance:

df = yf.download([RISKY_ASSET, MARKET_BENCHMARK],
                 start=START_DATE,
                 end=END_DATE,
                 adjusted=True,
                 progress=False)

4. Resample to monthly data and calculate the simple returns:

X = df['Adj Close'].rename(columns={RISKY_ASSET: 'asset', 
                                    MARKET_BENCHMARK: 'market'}) 
                   .resample('M') 
                   .last() 
                   .pct_change() 
                   .dropna()

5. Calculate beta using the covariance approach: 

covariance = X.cov().iloc[0,1]
benchmark_variance = X.market.var()
beta = covariance / benchmark_variance

The result of the code is beta = 1.6709.

6. Prepare the input and estimate the CAPM as a linear regression:

y = X.pop('asset')
X = sm.add_constant(X)

capm_model = sm.OLS(y, X).fit()
print(capm_model.summary())

The following image shows the results of estimating the CAPM model:

These results indicate that the beta (denoted as market here) is equal to 1.67, which means that Amazon's returns are 67% more volatile than the market (proxied by S&P 500). The value of the intercept is relatively small and statistically insignificant at the 5% significance level.