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Modelling and prediction of financial trading networks: an application to the New York Mercantile Exchange natural gas futures market
Author(s) -
Betancourt Brenda,
Rodríguez Abel,
Boyd Naomi
Publication year - 2020
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/rssc.12387
Subject(s) - futures contract , financial market , complex network , cluster analysis , markov chain , market microstructure , clustering coefficient , computer science , econometrics , order (exchange) , financial economics , economics , finance , artificial intelligence , machine learning , world wide web
Summary 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.