Investigating Competition in Financial Markets: A Sparse Autologistic Model for Dynamic Network Data

摘要

We develop a sparse autologistic model for investigating the impact ofdiversification and disintermediation strategies in the evolution of financialtrading networks. In order to induce sparsity in the model estimates andaddress substantive questions about the underlying processes the model includesan $L^1$ regularization penalty. This makes implementation feasible for complexdynamic networks in which the number of parameters is considerably greater thanthe number of observations over time. We use the model to characterize traderbehavior in the NYMEX natural gas futures market, where we find thatdisintermediation and not diversification or momentum tend to drive marketmicrostructure.