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 (< 0.05) indicate that null hypothesis (H0) is rejected and the time series is 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.