 Modelling and prediction of financial trading networks: an application to the New York Mercantile Exchange natural gas futures marketBrenda Betancourt, Abel Rodríguez and Naomi Boyd Journal of the Royal Statistical Society Series C, 2020, vol. 69, issue 1, 195-218 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. Date: 2020 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssc:v:69:y:2020:i:1:p:195-218 Ordering information: This journal article can be ordered from Access Statistics for this article Journal of the Royal Statistical Society Series C is currently edited by R. Chandler and P. W. F. Smith More articles in Journal of the Royal Statistical Society Series C from Royal Statistical Society Contact information at EDIRC. |
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