Brenda Betancourt, Abel RodrÃguez, Naomi Boyd
05/08/2013 05:40 PM
Applied Mathematics & Statistics
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.
