The article introduces a method to analyze complex datasets with heavy-tailed distributions. By using a combination of stable random vectors with Gaussian copula, researchers can identify stable distributions in multi-dimensional data. This approach allows for testing the stability of data and calculating density functions for multivariate stable distributions. The researchers applied this method to analyze daily stock returns and grain datasets, demonstrating its effectiveness in real-world scenarios.