Special Issue: Monitoring Systemic Risk: Data, Models and Metrics

IF 1.3 Q2 STATISTICS & PROBABILITY
R. Cont, Michael B. Gordy
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引用次数: 0

Abstract

The financial crisis of 2007–2009 has underlined the importance of interconnectedness among financial institutions andmarkets [1], the insufficiency of monitoring the balance sheet of individual financial institutions in isolation, and the necessity of adopting a system-wide view of financial stability. In the wake of the crisis, regulators have sought well-grounded and forward-looking indicators for monitoring the development of systemic risks in the financial system. The construction and interpretation of indicators and the identification and collection of relevant data for computing such indicators have proven to be major and ongoing challenges. The design of indicators for monitoring systemic risk requires the prior identification of contagionmechanisms and calls for an interplay between theory and empirical research. Many researchers have attempted to tackle the challenge of understanding the mechanisms underlying systemic risk. This two-part special issue grew out of a one-week workshop on Monitoring Systemic Risk: Data, Models and Metrics, organized by Rama Cont (Imperial College), Michael Gordy (Federal Reserve Board) and Christian Gourieroux (CREST and University of Toronto). The workshop, held in September 2014, was hosted by the Isaac Newton Institute of Mathematical Sciences (Cambridge, UK) as part of a semester-long program on “SystemicMathematicalmodelling and interdisciplinary approaches” (www.newton.ac.uk/event/syr). The workshop gathered together more than 100 researchers from various disciplines – mathematical finance, economics, econometrics and operations research – together with regulators, central bankers and industry risk professionals, to discuss how mathematical modeling may contribute to the modeling and monitoring of systemic risk. Further material and video recordings of all lectures are available for download from the website of the workshop at www.newton.ac.uk/event/syrw02. The contributions to this Special Issue underline some key issues that arose during the discussions at the workshop: estimation and validation of risk measures for capital adequacy, models of interconnectedness and centrality in banking networks, fire sales spillovers and portfolio overlaps. We thank the Isaac Newton Institute of Mathematical Sciences (Cambridge) for hosting and supporting theworkshop andOldMutual for its financial support of the program“Systemic Risk:MathematicalModeling and Interdisciplinary Approaches”.
特刊:监测系统性风险:数据、模型和度量
2007-2009年的金融危机凸显了金融机构和市场之间相互联系的重要性,凸显了孤立地监测单个金融机构资产负债表的不足,凸显了从全系统角度看待金融稳定的必要性。危机过后,监管机构一直在寻求有充分依据的前瞻性指标,以监测金融体系中系统性风险的发展。指标的构建和解释以及确定和收集计算这些指标的有关数据已证明是重大和持续的挑战。设计监测系统性风险的指标需要事先确定传染机制,并要求理论和实证研究之间的相互作用。许多研究人员试图解决理解系统性风险背后机制的挑战。这个由两部分组成的特刊是由Rama Cont(帝国理工学院)、Michael Gordy(联邦储备委员会)和Christian Gourieroux (CREST和多伦多大学)组织的为期一周的“监测系统风险:数据、模型和度量”研讨会发展而来的。该研讨会于2014年9月举行,由艾萨克·牛顿数学科学研究所(英国剑桥)主办,作为“系统数学建模和跨学科方法”学期项目的一部分(www.newton.ac.uk/event/syr)。研讨会汇聚了100多位来自数学金融学、经济学、计量经济学和运筹学等不同学科的研究人员,以及监管机构、央行行长和行业风险专业人士,讨论数学建模如何有助于系统风险的建模和监测。所有讲座的进一步材料和录像可从研讨会网站www.newton.ac.uk/event/syrw02下载。本期特刊的文章强调了研讨会讨论期间出现的一些关键问题:资本充足率风险措施的估计和验证、银行网络互联性和中心性模型、贱卖溢出效应和投资组合重叠。我们感谢艾萨克·牛顿数学科学研究所(剑桥)主办和支持本次研讨会,感谢oldmutual对“系统性风险:数学建模和跨学科方法”项目的资金支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Statistics & Risk Modeling
Statistics & Risk Modeling STATISTICS & PROBABILITY-
CiteScore
1.80
自引率
6.70%
发文量
6
期刊介绍: Statistics & Risk Modeling (STRM) aims at covering modern methods of statistics and probabilistic modeling, and their applications to risk management in finance, insurance and related areas. The journal also welcomes articles related to nonparametric statistical methods and stochastic processes. Papers on innovative applications of statistical modeling and inference in risk management are also encouraged. Topics Statistical analysis for models in finance and insurance Credit-, market- and operational risk models Models for systemic risk Risk management Nonparametric statistical inference Statistical analysis of stochastic processes Stochastics in finance and insurance Decision making under uncertainty.
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