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Modelling and prediction of financial trading networks: an application to the New York Mercantile Exchange natural gas futures market

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  • Brenda Betancourt
  • Abel Rodríguez
  • Naomi Boyd

Abstract

Over recent years there has been a growing interest in using financial trading networks to understand the microstructure of financial markets. Most of the methodologies that have been developed so far for this 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 modelling sequences of financial trading networks. Our approach uses a stochastic block model to describe the structure of the network during each period, and then links multiple time periods by using a hidden Markov model. This structure enables us to identify events that affect the structure of the market and make accurate short‐term prediction of future transactions. The methodology is illustrated by using data from the New York Mercantile Exchange natural gas futures market from January 2005 to December 2008.

Suggested Citation

  • Brenda Betancourt & Abel Rodríguez & Naomi Boyd, 2020. "Modelling and prediction of financial trading networks: an application to the New York Mercantile Exchange natural gas futures market," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(1), pages 195-218, January.
    • Handle: RePEc:bla:jorssc:v:69:y:2020:i:1:p:195-218
    DOI: 10.1111/rssc.12387
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    Cited by:

    1. Raffaele Mattera & Philipp Otto, 2023. "Network log-ARCH models for forecasting stock market volatility," Papers 2303.11064, arXiv.org.
    2. Chai, Jian & Zhang, Xiaokong & Lu, Quanying & Zhang, Xuejun & Wang, Yabo, 2021. "Research on imbalance between supply and demand in China's natural gas market under the double-track price system," Energy Policy, Elsevier, vol. 155(C).
    3. Sosa, Juan & Betancourt, Brenda, 2022. "A latent space model for multilayer network data," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).

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