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Investigating competition in financial markets: a sparse autologistic model for dynamic network data

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  • Brenda Betancourt
  • Abel Rodríguez
  • Naomi Boyd

Abstract

We develop a sparse autologistic model for investigating the impact of diversification and disintermediation strategies in the evolution of financial trading networks. In order to induce sparsity in the model estimates and address substantive questions about the underlying processes the model includes an $ L^1 $ L1 regularization penalty. This makes implementation feasible for complex dynamic networks in which the number of parameters is considerably greater than the number of observations over time. We use the model to characterize trader behavior in the NYMEX natural gas futures market, where we find that disintermediation and not diversification or momentum tend to drive market microstructure.

Suggested Citation

  • Brenda Betancourt & Abel Rodríguez & Naomi Boyd, 2018. "Investigating competition in financial markets: a sparse autologistic model for dynamic network data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(7), pages 1157-1172, May.
    • Handle: RePEc:taf:japsta:v:45:y:2018:i:7:p:1157-1172
    DOI: 10.1080/02664763.2017.1357684
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    Cited by:

    1. Domenico Di Gangi & Giacomo Bormetti & Fabrizio Lillo, 2022. "Score Driven Generalized Fitness Model for Sparse and Weighted Temporal Networks," Papers 2202.09854, arXiv.org, revised Mar 2022.

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