The article analyzes complex models to understand how different factors affect the correlations between multiple time series data. The researchers use a combination of factor models and stochastic volatility models to estimate these correlations. They develop a method to efficiently estimate the parameters of the models and compare different versions to determine the best fit. By applying their methods to real datasets of exchange rates and stock returns, they provide a practical way to analyze high-dimensional models of stochastic volatility.