Managing leverage of infrastructure projects: Aggregate and sectoral risk effect

Funding for infrastructure projects recently shows that debt has a portion more than equity, triggering public debates. Therefore, leverage as an instrument to measure the ability and willingness of project sponsors to fund becomes the utmost importance to discuss. Relating to leverage, risk and government participation are two main factors that can explain the choice of funding decisions by the project sponsors. For this reason, this study would analyze the effect of risk and government participation on leverage through the two main sectors of infrastructure projects, namely the transportation sector and the energy sector, and derivating risk to political risk and financial risk. The objects of research were 976 infrastructure projects listed in the Asian Development Bank during 2007−2016. We use censored regression to examine the model by infrastructure sectors, both as individual and through interaction effects. The analysis showed that overall, leverage of infrastructure projects was rather influenced by financial risk than by political risk. However, the leverage of infrastructure projects in the transportation sector was more vulnerable to risk than that in other sectors.     

Larger,counter-intuitive and lasting – The PSM role in responding to the COVID-19 pandemic,exploring opportunities for theoretical and actionable advances

PSM has played an important role in the initial mitigation of risks caused by the COVID-19 pandemic. We explore the nature and scope of this role and develop a roadmap for PSM contributions towards greater supply chain resilience. We find that the role of PSM is (1) multidimensional; responding to supply risks but also to demand and logistics risks, (2) counter to game theory; collaboration increased instead of decrease, (3) multi-stage; beyond the initial response the hardest change efforts are still ahead. The pandemic is accelerating the journey towards future-proof PSM but not necessarily revolutionizing the future of PSM.     

金融支持国防知识产权成果转化问题及策略研究

国防知识产权成果转化应用是国防科技创新体系建设落地见效的关键环节，引入现代金融体系能够有效促进国防知识产权成果转化。目前我国国防知识产权转化工作在组织管理体制、工作运行体系、法规政策制度和信息化建设等方面初显成效，但仍面临整体转化形势不容乐观、金融支持体系尚未成熟等问题，根本原因在于国防知识产权与发明人割裂导致一般金融产品难以通过审核，使融资渠道狭窄；其保密性进一步加大了与金融机构间的信息不对称，使得金融资源配置效率低下；国防知识产权专用性较强，相应抬高了融资成本。为有效解决上述问题，提出3条改进措施，以实现对国防知识产权转化的金融支持，即①从顶层设计上优化解密脱密流程，明确发明人与知识产权的权属关系，细化财税优惠等支持措施；②加强政府财政税收政策支持，引导创新利用多元社会资本进入支持转化领域；③建立各主体参与的金融服务协调保障机制、监测评估体系，启动国防知识产权转化的金融支持试点工作，构建符合国情的国防知识产权转化金融支持体系。     

金融资产配置与违约风险:蓄水池效应,还是逐利效应？

本文以2007—2016年中国A股上市公司为研究对象,检验了企业金融资产配置对违约风险的影响。实证研究发现:金融资产持有量越多,企业的违约风险越低,金融资产配置的“蓄水池效应”显著;在货币政策宽松时期,金融资产配置导致的代理冲突显现,宽松的货币政策会抑制金融资产投资对违约风险的降低作用。政府规制也会有一定的公司治理作用,将产业政策纳入讨论发现:对于产业政策支持的行业来说,企业金融资产配置能够降低违约风险,但是宽松的货币政策会刺激管理层的短视投资行为,抑制政府规制的公司治理作用。进一步地,本文提出会计稳健性的提升是企业金融资产配置降低违约风险的重要路径。本文的研究结论丰富了企业金融资产配置动机和违约风险影响因素的讨论,能够为政府部门防范经济运行中的内在风险提供有益借鉴。     

Analysis of territorial compatibility for Seveso-type sites using different risk assessment methods and GIS technique

The increasing size of chemical plants and rapid growth in residential areas has led to many incompatible land-use scenarios in the last 100 years. In this context, the authors assume that assessment and planning have their significant role in preventing the juxtaposition of hazards and population, and that, in this field, there is a broad recognition of the need for legislative and policy consistency across the European Union. The paper presents a comparative case study in which the Romanian land-use planning (LUP) criteria and the risk-based quantitative approach for a chemical plant are applied. Accident scenarios involving chlorine and propylene arecomprehensively analyzed using consequence and risk modelling software and GIS technique for the territorial compatibility assessment. The objective of the paper is threefold. Firstly, it presents an overview about current risk analysis methods; secondly, the authors advance an understanding of risk assessment practices used in several countries for the prevention and control of major industrial accidents involving dangerous substances and, also, for LUP. Thirdly, a method targeting an improved risk assessment framework for LUP, encompassing Romania’s determinants is outlined. The results obtained using the two different approaches indicate significant differences regarding the possibly affected areas and territorial compatibility. Furthermore, based on the findings, the paper ends with a set of recommendations that can be transformed into the foundation for future enactments of new safety standards that cover risk assessment for LUP. Consequently, the present study aims to become a frame of reference for decision-makers towards more sustainable and updated risk assessment practices in the field of industrial activities.     

资产透明度、监管套利与银行系统性风险

资产不透明的金融机构过度依赖批发性融资进行监管套利不利于系统性风险的防控。在此背景下,本文首先在经典银行道德风险模型的基础上引入关联性,从资产透明度和监管套利的视角分析银行系统性风险累积的内在机理。而后利用2007-2018年中国上市银行微观数据,构建资产透明度指标和系统性风险指标(SRISK、MES),对理论推论进行实证检验。主要结论有:(1)资产不透明、监管套利会提高银行的系统性风险。(2)监管套利弱化了资产透明度和资本监管机制对银行系统性风险承担的约束作用,资产透明度与资本监管机制在约束系统性风险承担中的协调作用不明显。(3)以大银行为主的债权银行受监管套利的影响相较于受资产透明度的影响更明显。在此基础上,我们对完善金融风险防范体系以及监管机制提出了若干建议。     

经济开放与货币需求:国际金融风险及持币成本的测度

在中国开放经济体制下的基准货币需求模型中,本文将源于国际金融市场的持币成本设为遗漏潜变量,并构建特定的国际金融综合指数(CIFI)作为该潜变量的测度。借鉴机器学习与测度理论,本文利用对数误差修正模型提出了分步降维的CIFI构造算法,构造了长期CIFI和短期CIFI。结果表明,CIFI构造中的无监督降维步骤有助于减少高维金融数据中的冗余信息。实证分析发现,国际机会成本对中国货币需求具有规律性的前导影响,而在2007至2008年国际金融危机期间,央行的应急措施对长期CIFI所代表的非均衡冲击起到明显的阻截效果,对短期CIFI的影响基本是持续不变的。通过综合指数构造与宏观货币需求模型的算法连接,可以利用CIFI的构成结构从前导时间与影响强度两方面追踪冲击货币需求的国际金融风险的具体来源,这为宏观决策者监测国际金融市场提供了颇有规律的信息。在方法论上,本研究为如何利用模型监测国际金融市场影响宏观经济开辟了一条新路。     

Deep learning for modeling the collection rate for third-party buyers

This study evaluates a wide range of machine learning techniques such as deep learning, boosting, and support vector regression to predict the collection rate of more than 65,000 defaulted consumer credits from the telecommunications sector that were bought by a German third-party company. Weighted performance measures were defined based on the value of exposure at default for comparing collection rate models. The approach proposed in this paper is useful for a third-party company in managing the risk of a portfolio of defaulted credit that it purchases. The main finding is that one of the machine learning models we investigate, the deep learning model, performs significantly better out-of-sample than all other methods that can be used by an acquirer of defaulted credits based on weighted-performance measures. By using unweighted performance measures, deep learning and boosting perform similarly. Moreover, we find that using a training set with a larger proportion of the dataset does not improve prediction accuracy significantly when deep learning is used. The general conclusion is that deep learning is a potentially performance-enhancing tool for credit risk management.     

Do significant risk warnings in annual reports increase corporate bond credit spreads? Evidence from China

Based on listed companies issuing bonds on the Shanghai and Shenzhen Stock Exchanges from 2007 to 2017, this study analyzes the relationship between significant risk warnings in Chinese companies’ annual reports and corporate bond credit spreads. The main findings are as follows. First, in the Chinese market, “substantial warnings of significant risks” can significantly improve corporate bond credit spreads, reflecting the risk-warning effect; second, state-owned property rights weaken this effect, which only pertains to listed companies with poor risk management and low information quality; third, significant risk warnings increase investors’ heterogeneous beliefs, also affecting credit spreads; and fourth, through textual analysis, it is found that the corporate bond credit spread is greater when the disclosed risk factors are more pessimistic and less similar to those of the previous year. The findings of this paper help to enrich the literature on credit spreads and risk disclosure.     

Natural resource rents and internal conflicts: Can decentralization lift the curse?

We study how natural resource rents affect the risk of internal conflict within countries and how the federal structure of countries influences this relationship. Natural resource abundance may induce excessive rent-seeking and thus increase the risk of internal conflict. Fiscal and political decentralization as an institutional arrangement for rent-sharing and political codetermination of regions within a country may limit the destructive effect of natural resource rents on internal stability. Using cross-country and panel data from more than 90 countries covering the period 1984–2004, we find evidence that natural resource rents indeed increase the risk of internal conflict, but this relationship is significantly mitigated by political decentralization.