解读《巴塞尔新资本协议》中全面风险管理体系——兼论ERM框架下银行风险管理

从《巴塞尔新资本协议》角度解读了商业银行未来的风险管理方向和标准方法——全面风险管理,同时讨论了ERM框架下我国商业银行如何构建、改进全面风险管理体系。     

公司治理、公司管理与内部控制——对COSO企业风险管理框架(ERM)的分析

2004年COSO委员会正式发布了企业风险管理框架(ERM),这是继1992年其发布内部控制整合框架后的又一个飞跃性的内部控制研究成果。本文从公司治理内涵入手,以广义公司治理所基于的利益相关者理论为基础,分析公司治理与公司管理的关系,考察内部控制演变过程与公司管理的关系,在此基础上重点研究COSO的最新研究成果ERM这一广义的内部控制对公司治理和公司管理的影响。本文的结论是:ERM能提高公司治理效率,实施有效公司管理,实现公司治理和公司管理的有机整合。     

The Maastricht inflation criterion: How unpleasant is purgatory?

The Maastricht inflation criterion, designed in the early 1990s to bring “high-inflation” EU countries in line with “low-inflation” countries prior to the introduction of the euro, poses challenges for both new EU member countries and the European Central Bank. While the criterion has positively influenced the public stance toward low inflation, it has biased the choice of the disinflation strategy toward short-run, fiat measures – rather than adopting structural reforms with longer-term benefits – with unpleasant consequences for the efficiency of the eurozone transmission mechanism. The criterion is also unnecessarily tight for new member countries as it mainly reflects cyclical developments.     

解读《巴塞尔新资本协议》中全面风险管理体系——兼论ERM框架下银行风险管理

花旗银行总裁沃尔特·维斯顿曾有名言＂事实上银行从事的是管理风险的行业。简单来说,这就是银行业＂,足可见风险管理对商业银行的重要性。本文从《巴塞尔新资本协议》角度解读了商业银行未来的风险管理方向和标准方法——全面风险管理,同时讨论了ERM框架下我国商业银行如何构建、改进全面风险管理体系。     

International crises,instability periods and contagion: the case of the ERM

In this article we propose a two step procedure for modeling the propagation of financial shocks. The first step consists in the estimation, by means of SWARCH models, of the conditional probability of being in a period of high volatility, while in the second step such indicators are included in a structural simultaneous equations models for interdependences among different countries. The results show that episodes of financial crisis effectively happened during periods of high volatility and that such measures of instability are important in explaining the propagation of devaluation expectations between six European Countries during the ERM period.

ERM理念下企业业务流程的重组与优化

ERM是企业在实现未来战略目标的过程中,试图将各类不确定因素产生的结果控制在预期可接受范围内的方法和过程,从本质上说,ERM体现了对风险的一种全局观念,考虑的是风险对整个企业、而非局部的潜在冲击.这是与传统意义的企业管理截然不同的管理理念.企业要进行风险管理,就需要对原有的业务流程进行重组与优化.本文经过分析认为,COSO2004的"八要素"框架是基于风险管理的业务流程重组与优化的重要理论依据.     

The drivers and value of enterprise risk management: evidence from ERM ratings

In the course of recent regulatory developments, holistic enterprise-wide risk management (ERM) frameworks have become increasingly relevant for insurance companies. The aim of this paper is to contribute to the literature by analyzing determinants (firm characteristics) as well as the impact of ERM on the shareholder value of European insurers using the Standard & Poor’s ERM rating to identify ERM activities. This has not been done so far, even though it is of high relevance against the background of the introduction of Solvency II, which requires a holistic approach to risk management. Results show a significant positive impact of ERM on firm value for the case of European insurers. In particular, we find that insurers with a high quality risk management (RM) system exhibit a Tobin’s Q that on average is about 6.5% higher than for insurers with less high quality RM after controlling for covariates and endogeneity bias.     

ERM及其最新研究进展述评

ERM是20世纪90年代以来迅速兴起的一种新的风险管理模式。国外大量的问卷调查和实证研究表明：ERM与传统的风险管理模式具有质的不同,它是对传统风险管理模式的扬弃,是企业风险管理理念和实践的一次重大转型。目前,ERM在提升企业风险管理能力方面的作用已得到较为普遍的认同。但是,ERM要在改善企业风险管理并最终优化战略决策方面充分发挥作用仍然面临着各种各样的挑战。     

Deep empirical risk minimization in finance: Looking into the future

Many modern computational approaches to classical problems in quantitative finance are formulated as empirical loss minimization (ERM), allowing direct applications of classical results from statistical machine learning. These methods, designed to directly construct the optimal feedback representation of hedging or investment decisions, are analyzed in this framework demonstrating their effectiveness as well as their susceptibility to generalization error. Use of classical techniques shows that over-training renders trained investment decisions to become anticipative, and proves overlearning for large hypothesis spaces. On the other hand, nonasymptotic estimates based on Rademacher complexity show the convergence for sufficiently large training sets. These results emphasize the importance of synthetic data generation and the appropriate calibration of complex models to market data. A numerically studied stylized example illustrates these possibilities, including the importance of problem dimension in the degree of overlearning, and the effectiveness of this approach.