okama.AssetList.kurtosis_rolling

AssetList.kurtosis_rolling(window=60)

Calculate rolling 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.

Parameters:

windowint, default 60

Rolling window size in months. This is the number of observations used for calculating the statistic. The window size should be at least 12 months.

Returns:

DataFrame

Rolling kurtosis time series for each asset.

See also

skewness

Compute skewness.

skewness_rolling

Compute rolling skewness.

kurtosis

Calculate expanding 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_rolling(window=12*5).plot()
>>> plt.show()