What is the null hypothesis in the Jacque-Bera Test?

The null hypothesis in the Jarque-Bera Test asserts that the data being analyzed is normally distributed. This test is specifically designed to assess the normality of a dataset by examining both skewness and kurtosis. The null hypothesis serves as a baseline assumption that the distribution of the dataset aligns with a normal distribution (bell curve), meaning that it possesses the symmetric properties characteristic of normality.

If the Jarque-Bera statistic leads to a rejection of the null hypothesis, it suggests that the data deviates from normality in terms of skewness or kurtosis, indicating that the dataset may have asymmetrical properties or lack the appropriate peakedness. However, if the null hypothesis is not rejected, it strengthens the case that the data does indeed follow a normal distribution.

Understanding the null hypothesis is crucial as it lays the groundwork for statistical inference and analysis in various finance-related applications, including risk management and portfolio optimization.