技术创业型企业孵化资本投入的风险管控——来自多案例对比

待孵化的技术创业型企业同时具有概念性技术向差异化产品转化的高风险与产品异质性被认同后溢价评估的高收益特点，导致其投资者多处于隔岸观火却欲罢不能的的孵化投资矛盾中。对孵化资本实施有效的风险管控，成为吸引外部资本投入的关键。基于此，选择在可创概念选育与技术创业孵化领域探索出高成功率路径的概念证明中心、YCombinator和创新工场为研究案例，分析其在创业项目筛选、孵化资金运营等关键环节的风险管控方式，以期完善我国孵化资本管控理论、助力我国商业孵化器提高资金风险管控能力、促进外部资本投入。     

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.     

预算软约束与风险偏好强化：非均衡式创新中的风险点识别研究

计划经济体制导致资源使用效率低下，社会中存在大量非均衡市场和领域，这为中国企业实施均衡式创新创造了条件。随着均衡式创新的帕累托改进空间逐渐缩小，利用非均衡式创新提升资源使用效率已刻不容缓。在非均衡式创新过程中，政府极易侵入社会主体创新边界，导致创新租值降低或耗散。政府对创新主体的财政资金扶持容易对创新预算形成软约束，导致创新目标替代，强化创新主体的风险偏好，放大创新系统风险，诱发俘获政府行为，最终导致创新企业逆向淘汰。因此，明确政府在创新活动中的边界与风险，对我国实施创新驱动发展战略至关重要。     

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.     

The fair value of a token: How do markets price cryptocurrencies?

With the rise of cryptocurrency tokens as a new asset class, the question of the fair evaluation of a cryptocurrency token has become a question of increasing importance. We estimate the pricing kernel with which users price factors affecting their token holdings. We investigate how traditional risk factors such as market risk are evaluated, as well as how blockchain specific risk factors are priced in. In order to do so, we introduce an asset pricing model and modify its properties to make it applicable to cryptocurrency markets. We group the risk factors into market related and Bitcoin- and Ethereum blockchain specific risk factors. We find that blockchain specific risk factors are priced in. There is evidence that risk factors have moved from Bitcoin to Ethereum specific risk factors with an increasing importance of market factors, providing evidence for a decoupling of on-chain and off-chain trading activity.     

Independence in bank governance structure: Empirical evidence of effects on bank risk and performance

We investigate how different governance arrangements affect risk and return in banks. Using a new data set for UK banks over the period 2003–2012, we employ a simultaneous equations framework to control for the reciprocal relationship between risk and return. We show that separation of the roles of CEO and Chairman increases bank risk without causing a concurrent increase in return. We also find that oversight by a Remuneration Committee and Non-Executive Directors (NEDs) lowers the probability of bank failure, indicating that empowering an independent Chairman has different effects from empowering independent NEDs. Overall, our results underline the importance of accounting for the heterogeneity in corporate governance arrangements within banks.     

基于动态因子Copula模型的我国银行系统性风险度量

本文基于2007年至2019年我国14家上市银行的股票收益率,构建偏态t-分布动态因子Copula模型,利用时变荷载因子刻画单家银行与整个系统的相关性,计算联合风险概率作为系统性风险整体水平的度量,基于关联性视角提出了新的单家机构系统脆弱性和系统重要性度量指标——系统脆弱性程度和系统重要性程度。该方法充分考虑了银行个体差异性和系统的内在关联性以及收益率的厚尾性和非对称性,从而能够捕捉到更多的信息且兼具时效性。研究表明:银行机构在风险聚集时期相关程度更大,联合风险概率能够准确识别出系统性风险事件且在我国推行宏观审慎评估体系以后有明显降低;整体而言,大型商业银行系统重要性水平最高,同时风险抗压能力也最强;本文使用的度量方法降低了数据获取成本且更具时效性,有助于为宏观审慎差异化监管工作提供借鉴和参考。     

Decomposing financial (in)stability in emerging economies

The analysis of the build-up of risks in emerging economies have traditionally been scarce and focused mostly on external risks, despite the recent substantial development of their financial system. This paper builds an index of financial vulnerabilities tailored to emerging economies, grouping 32 indicators around four poles: valuation and risk appetite, imbalances in the non-financial sector, financial sector vulnerabilities, and global vulnerabilities. It adopts a model-free approach, purposely departing from early warning models or complex econometric constructs, and rely on data made already available by international organisations. Our index of financial vulnerabilities enables a granular mapping of where risk originates and how it spreads to other parts of the financial system. Using various data visualisation tools and benefitting from the flexibility of our index’s methodology, we are able build a narrative of the evolution of financial stability in emerging economies from 2005 to 2015. Finally, we also discuss the relation between our index and both the business cycle (proxied by GDP) and the credit cycle (proxied by the credit-to-GDP gap).     

Discovering bank risk factors from financial statements based on a new semi‐supervised text mining algorithm

This paper aims to comprehensively uncover bank risk factors from qualitative textual risk disclosures reported in financial statements, which contain a huge amount of information on bank risks. We propose a new semi‐supervised text mining approach named naive collision algorithm to analyse the textual risk disclosures, which can more accurately identify bank risk factors compared with the typical unsupervised text mining approach. We identified 21 bank risk factors in total, which is far more than identified in previous studies. We further analyse the importance of each bank risk factor and how the importance of each risk factor changes over time.     

Tales from tails: On the empirical distributions of forecasting errors and their implication to risk

When evaluating the performances of time series extrapolation methods, both researchers and practitioners typically focus on the average or median performance according to some specific error metric, such as the absolute error or the absolute percentage error. However, from a risk-assessment point of view, it is far more important to evaluate the distributions of such errors, and especially their tails. For instance, a lack of normality and symmetry in error distributions can have significant implications for decision making, such as in stock control. Moreover, frequently these distributions can only be constructed empirically, as they may be the result of a computationally-intensive non-parametric approach, such as an artificial neural network. This study proposes an approach for evaluating the empirical distributions of forecasting methods and uses it to assess eleven popular time series extrapolation approaches across two different datasets (M3 and ForeDeCk). The results highlight some very interesting tales from the tails.