This vignette combines and replicates the work done in Dieblod and Yilmaz 2009, DY 2010, and DY 2011, as well as scaling the methods for quick application and replication. The DY papers describe how to quantify in an index how much error in a forecast can be attributed to shocks to its variables. These “spillovers” are found using standard time-series techniques for finding an impulse-response function and forecast error variance decompositions. In addition, DY 2011 show how the effects of these shocks can be represented as a network when one relaxes the assumptions that vector autoregressions have normally distributed, i.i.d. error terms and that shocks to each variable must be orthongonal to the other shocks. In the spirit of DY 2011, stocks of large banks and AIG will be the subjects of analysis. Like all the DY publications, the analysis will focus on connections and spillovers of volatility.