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()