中央交易对手是否降低了交易对手和流动性风险?实证结果

IF 0.3 Q4 BUSINESS, FINANCE
Carlos León, R. Mariño, Carlos Cadena
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引用次数: 1

摘要

中央对手方(CCP)介入金融合同的买方和卖方之间,以消除其双边风险。因此,通过中央对手方清算所进行的中央清算和结算应影响金融机构参与金融市场的方式。然而,金融机构的互动很难观察和分析。基于与哥伦比亚比索非交割远期市场相对应的独特交易数据集,本文首次将CCP同意清算和结算的交易网络与双边清算和结算交易网络进行了比较。要集中清理和结算的网络显示出金融机构之间显著更高的连通性和更低的距离。这表明同意集中清算和结算可以降低流动性风险。CCP介入后,风险敞口网络显示出显著较低的连通性和较高的距离,这与交易对手风险的降低一致。因此,有证据表明,CCP会导致金融机构的行为发生变化,成为同一市场的两种不同的经济结构,这与CCP有意降低流动性和交易对手风险相对应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Do central counterparties reduce counterparty and liquidity risk? Empirical results
A central counterparty (CCP) interposes itself between buyers and sellers of financial contracts to extinguish their bilateral exposures. Therefore, central clearing and settlement through a CCP should affect how financial institutions engage in financial markets. Though, financial institutions’ interactions are difficult to observe and analyze. Based on a unique transaction dataset corresponding to the Colombian peso non-delivery forward market, this article compares—for the first time—networks of transactions agreed to be cleared and settled by the CCP with those to be cleared and settled bilaterally. Networks to be centrally cleared and settled show significantly higher connectivity and lower distances among financial institutions. This suggests that agreeing on central clearing and settlement reduces liquidity risk. After CCP interposition, exposure networks show significantly lower connectivity and higher distances, consistent with a reduction of counterparty risk. Consequently, evidence shows CCPs induce a change of behavior in financial institutions that emerges as two distinctive economic structures for the same market, which corresponds to CCP’s intended reduction of liquidity and counterparty risks.
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来源期刊
Algorithmic Finance
Algorithmic Finance BUSINESS, FINANCE-
CiteScore
0.40
自引率
0.00%
发文量
6
期刊介绍: Algorithmic Finance is both a nascent field of study and a new high-quality academic research journal that seeks to bridge computer science and finance. It covers such applications as: High frequency and algorithmic trading Statistical arbitrage strategies Momentum and other algorithmic portfolio management Machine learning and computational financial intelligence Agent-based finance Complexity and market efficiency Algorithmic analysis of derivatives valuation Behavioral finance and investor heuristics and algorithms Applications of quantum computation to finance News analytics and automated textual analysis.
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