In studying the correlation between cryptocurrencies and traditional financial assets, several new quantitative models and analysis tools can be applied. For example, the Copula - GARCH model can capture the non - linear dependence and volatility clustering. The Bayesian VAR model is useful for handling uncertainty. Additionally, machine learning - based methods like random forests and neural networks can analyze complex relationships and predict correlations. These tools help to better understand the dynamic connection between the two asset classes.