The data collected under the European Market Infrastructure Regulation ("EMIR data") provide authorities with voluminous transaction-by-transaction details on derivatives but their use poses numerous challenges. To overcome one major challenge, this chapter draws from eight different data sources and develops a greedy algorithm to obtain a new counterparty sector classification. We classify counterparties' sector for 96% of the notional value of outstanding contracts in the euro area derivatives market. Our classification is also detailed, comprehensive, and well suited for the analysis of the derivatives market, which we illustrate in four case studies. Overall, we show that our algorithm can become a key building block for a wide range of research- and policy-oriented studies with EMIR data.

Lenoci, F., Letizia, E. (2021). Classifying Counterparty Sector in EMIR Data. In S. Consoli, D. Reforgiato Recupero, S. Saisana (a cura di), Data Science for Economics and Finance Methodologies and Applications (pp. 117-143). Springer [10.1007/978-3-030-66891-4_6].

Classifying Counterparty Sector in EMIR Data

Lenoci, Francesca D.
;
2021

Abstract

The data collected under the European Market Infrastructure Regulation ("EMIR data") provide authorities with voluminous transaction-by-transaction details on derivatives but their use poses numerous challenges. To overcome one major challenge, this chapter draws from eight different data sources and develops a greedy algorithm to obtain a new counterparty sector classification. We classify counterparties' sector for 96% of the notional value of outstanding contracts in the euro area derivatives market. Our classification is also detailed, comprehensive, and well suited for the analysis of the derivatives market, which we illustrate in four case studies. Overall, we show that our algorithm can become a key building block for a wide range of research- and policy-oriented studies with EMIR data.
Capitolo o saggio
Counterparty sector; EMIR data
English
Data Science for Economics and Finance Methodologies and Applications
Consoli, S; Reforgiato Recupero, D; Saisana, S
2021
9783030668907
Springer
117
143
Lenoci, F., Letizia, E. (2021). Classifying Counterparty Sector in EMIR Data. In S. Consoli, D. Reforgiato Recupero, S. Saisana (a cura di), Data Science for Economics and Finance Methodologies and Applications (pp. 117-143). Springer [10.1007/978-3-030-66891-4_6].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/515819
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