Calculate the Jarque-Bera test for normality.

The test combines sample skewness and excess kurtosis into a single statistic that is chi-squared distributed with 2 degrees of freedom under the null hypothesis that returns are normally distributed. A significant result (low p-value) indicates that returns are not normally distributed, which is relevant when choosing between e.g. gaussian and Student-T based Value at Risk models.

For more information about the method, see the following paper:

  • Jarque, C.M. and Bera, A.K. (1987). “A Test for Normality of Observations and Regression Residuals.” International Statistical Review, 55(2), 163-172.

Also known as: JB test, normality test.

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Calculate the Jarque-Bera Test in Python

The Jarque-Bera Test is available in the Econometrics module of the open-source Finance Toolkit. Install it with:

pip install financetoolkit -U

Then call get_jarque_bera_test as shown below.

from financetoolkit import Toolkit

toolkit = Toolkit(["AMZN", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.econometrics.get_jarque_bera_test(period="quarterly")

Which returns:

  AMZN TSLA
Jarque-Bera Statistic 3.0505 1.9354
P-Value 0.2175 0.38

Parameters

get_jarque_bera_test accepts the following parameters:

  • period (str, optional): The data frequency for returns (daily, weekly, monthly, quarterly, or yearly). Defaults to “daily”.
  • within_period (bool, optional): Whether to calculate the test within the specified period or for the entire period. Thus whether to look at the test within a specific year (if period = ‘yearly’) or look at the entirety of all years. Defaults to False.
  • include_benchmark (bool, optional): Whether to include “Benchmark” among the assets tested. Defaults to False.
  • rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
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The Econometrics module page introduces the module, and the sidebar lists all of its functions.

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