okama.AssetList.kurtosis

property AssetList.kurtosis

Calculate expanding Fisher (normalized) kurtosis of the return time series for each asset.

Kurtosis is the fourth central moment divided by the square of the variance. It is a measure of the “tailedness” of the probability distribution of a real-valued random variable.

Kurtosis should be close to zero for normal distribution.

Returns:

DataFrame: Expanding kurtosis time series for each asset.

See also

skewness: Compute skewness.
skewness_rolling: Compute rolling skewness.
kurtosis_rolling: Calculate rolling Fisher (normalized) kurtosis.
jarque_bera: Perform Jarque-Bera test for normality.
kstest: Perform Kolmogorov-Smirnov test for different types of distributions.

Examples

>>> import matplotlib.pyplot as plt
>>> al = ok.AssetList(['GC.COMM', 'FNER.INDX'], first_date='2000-01', last_date='2021-01')
>>> al.names
{'GC.COMM': 'Gold',
'FNER.INDX': 'FTSE NAREIT All Equity REITs'}
>>> al.kurtosis.plot()
>>> plt.show()

../_images/okama-AssetList-kurtosis-1.png