We have seen in the previous chapter how extreme events coming from asymmetric and fat-tailed distributions can be modeled and how the risk associated with extreme events can be measured and managed.

In some cases we have access to financial data that enables us to construct complex networks. In financial networks, it is quite usual that the distribution of some attributes (degree, quantity, and so on) is highly asymmetric and fat-tailed too.

By nature, available financial networks are usually not complete; they do not contain either all possible players, or all possible connections, or all relevant attributes. But even in their limited state, they constitute an extremely rich and informative data set which can help us to get insight into the detailed microstructure of the market under investigation.

This chapter gives an overview of how financial networks can be represented, simulated, visualized, and analyzed in R. We will focus on two important practical problems...