{"title":"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","authors":"Carlos León, R. Mariño, Carlos Cadena","doi":"10.3233/AF-200341","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"9 1","pages":"25-34"},"PeriodicalIF":0.3000,"publicationDate":"2021-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/AF-200341","citationCount":"1","resultStr":"{\"title\":\"Do central counterparties reduce counterparty and liquidity risk? Empirical results\",\"authors\":\"Carlos León, R. Mariño, Carlos Cadena\",\"doi\":\"10.3233/AF-200341\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":42207,\"journal\":{\"name\":\"Algorithmic Finance\",\"volume\":\"9 1\",\"pages\":\"25-34\"},\"PeriodicalIF\":0.3000,\"publicationDate\":\"2021-08-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.3233/AF-200341\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Algorithmic Finance\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3233/AF-200341\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"BUSINESS, FINANCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Algorithmic Finance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/AF-200341","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
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.
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.