okama.AssetList.jarque_bera

property AssetList.jarque_bera

Perform Jarque-Bera test for normality of assets returns historical data.

Jarque-Bera test shows whether the returns have the skewness and kurtosis matching a normal distribution (null hypothesis or H0).

Returns:

DataFrame: Returns test statistic and the p-value for the hypothesis test. large Jarque-Bera statistics and tiny p-value indicate that null hypothesis (H0) is rejected and the time series are not normally distributed. Low statistic numbers correspond to normal distribution.

See also

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

Examples

>>> 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.jarque_bera
            GC.COMM   FNER.INDX
statistic  4.507287  593.633047
p-value    0.105016    0.000000

Gold return time series (GC.COMM) distribution have small p-values (H0 is not rejected). Null hypothesis (H0) is rejected for FTSE NAREIT Index (FNER.INDX) as Jarque-Bera test shows very small p-value and large statistic.