Long-Tailed Distributions¶

In [1]:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import probplot

import pandas as pd import numpy as np import matplotlib.pyplot as plt from scipy.stats import probplot

In [2]:

nflx = pd.read_csv("../data/sp500_data.csv").NFLX
nflx = np.diff(np.log(nflx[nflx>0]))
fig, ax = plt.subplots(figsize=(4,4))
probplot(nflx, plot=ax)

nflx = pd.read_csv("../data/sp500_data.csv").NFLX nflx = np.diff(np.log(nflx[nflx>0])) fig, ax = plt.subplots(figsize=(4,4)) probplot(nflx, plot=ax)

Out[2]:

((array([-3.33278964, -3.07756454, -2.93575124, ...,  2.93575124,
          3.07756454,  3.33278964], shape=(1612,)),
  array([-6.60529819, -6.09667121, -5.40220961, ...,  5.12547859,
          5.87072271,  6.03787   ], shape=(1612,))),
 (np.float64(1.4823778663727092),
  np.float64(0.0015561067366313861),
  np.float64(0.9941828221936319)))

In [3]:

import seaborn as sns

plt.hist(nflx, density=True, bins=30, alpha=0.5, edgecolor='black')
sns.kdeplot(nflx, color='orange', linewidth=2)

import seaborn as sns plt.hist(nflx, density=True, bins=30, alpha=0.5, edgecolor='black') sns.kdeplot(nflx, color='orange', linewidth=2)

Out[3]:

<Axes: ylabel='Density'>