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

Modelling and prediction of financial trading networks: An application to the NYMEX natural gas futures market