  Over the last few years there has been a growing interest in using financial
trading networks to understand the microstructure of financial markets. Most of
the methodologies developed so far for this purpose have been based on the
study of descriptive summaries of the networks such as the average node degree
and the clustering coefficient. In contrast, this paper develops novel
statistical methods for modeling sequences of financial trading networks. Our
approach uses a stochastic blockmodel to describe the structure of the network
during each period, and then links multiple time periods using a hidden Markov
model. This structure allows us to identify events that affect the structure of
the market and make accurate short-term prediction of future transactions. The
methodology is illustrated using data from the NYMEX natural gas futures market
from January 2005 to December 2008.
