Multilayer financial networks and systemic importance: Evidence from China

In this paper, we develop a multilayer network structure and reveal the relationship between network structure and systemic risk. Unlike many previous studies, our model considers both liability and cross-holding of shares between financial institutions simultaneously. We propose a new systemic risk measurement by exploring the dynamic mechanism of financial contagion in the multilayer network. We display the network structure of Chinese financial institutions, including connectivity and diversity, and identify the systemic importance of them. We demonstrate that the multilayer network plays a non-linear role in financial risk spreading. Using the panel regression model and several experiment evidences, we show that the systemic risk can be explained more effectively by the linkage diversity more than the connectivity at both the institutional level and the system level. Our results highlight the importance of considering contagion mechanisms that go beyond a simple single-layer network structure.     

浅析体质投资对旅游院校的重要性

旅游业的日益兴旺大大提升了社会对高质量旅游人才的渴求，然而，目前中国大学生的体育教育不尽如人意，如何加强对学生健康第一教育，体质的投入，在一定程度上决定了培养高质量旅游人才的大问题。     

论流通观念创新

市场经济的实质和核心是交换经济、流通经济;流通不仅是中介,而且直接渗透到再生产的各个领域;流通不是地摊经济,现代流通也要运用高新技术,实现流通现代化;开放是流通的本质特征,市场经济的发达程度与流通的开放程度紧密相连;品牌本身是一种流通现象,品牌形象形成名牌和名牌经济,要在扩大对外开放中让中国自己的名牌走向世界。     

论互联网与保险科技结合的重要性

随着互联网科技的快速发展,其影响力已深入市场经济的各个部分,保险行业依托云计算、大数据等互联网科学技术,不断推动自身发展。但在互联网科技时代,互联网保险行业也出现了诸多问题,有必要对这些问题进行探讨,并提出相应的指导策略。     

A Bayesian approach for more reliable tail risk forecasts

This paper demonstrates that existing quantile regression models used for jointly forecasting Value-at-Risk (VaR) and expected shortfall (ES) are sensitive to initial conditions. Given the importance of these measures in financial systems, this sensitivity is a critical issue. A new Bayesian quantile regression approach is proposed for estimating joint VaR and ES models. By treating the initial values as unknown parameters, sensitivity issues can be dealt with. Furthermore, new additive-type models are developed for the ES component that are more robust to initial conditions. A novel approach using the open-faced sandwich (OFS) method is proposed which improves uncertainty quantification in risk forecasts. Simulation and empirical results highlight the improvements in risk forecasts ensuing from the proposed methods.     

企业转让制度之比较研究

企业转让是指为了一定的营业目的通过签定合同将企业作为一个有机组织体的全部财产或部分重要财产进行有偿转让的活动。它是企业的所有权在不同的市场主体之间的移转,既不同于货物买卖也不同于企业财产的转让。企业转让制度在《德国商法典》、《日本商法典》和《韩国商法典》中都有专门规定,但在《澳门商法典》中规定的更为全面,主要涉及商号、债权债务关系、竞业禁止和劳动关系内容。建立企业转让制度是市场经济的内在体现,对我国发展经济和解决就业有着重要的现实意义,同时也符合我国建立现代产权制度的要求。     

Social networks in the global banking sector

We show that banks with shared social connections partner more often in the global syndicated loan market and that central banks in the network play dominant roles in various interbank transactions, indicating that social connections facilitate business connections. However, more centralized banks in the network also contribute significantly to the global systemic risk. Moreover, we find the soft information generated by social networks is particularly valuable when potential partners operate under different accounting and regulatory standards. Finally, we show that the recent banking crisis significantly limited the positive soft information effects of social networks in the global banking system.     

Modified efficient importance sampling for partially non‐Gaussian state space models

The construction of an importance density for partially non‐Gaussian state space models is crucial when simulation methods are used for likelihood evaluation, signal extraction, and forecasting. The method of efficient importance sampling is successful in this respect, but we show that it can be implemented in a computationally more efficient manner using standard Kalman filter and smoothing methods. Efficient importance sampling is generally applicable for a wide range of models, but it is typically a custom‐built procedure. For the class of partially non‐Gaussian state space models, we present a general method for efficient importance sampling. Our novel method makes the efficient importance sampling methodology more accessible because it does not require the computation of a (possibly) complicated density kernel that needs to be tracked for each time period. The new method is illustrated for a stochastic volatility model with a Student's t distribution.     

Systemic event prediction by an aggregate early warning system: An application to the Czech Republic

This work develops an early warning framework for assessing systemic risks and predicting systemic events over a short horizon of six quarters and a long horizon of 12 quarters on a panel of 14 countries, both advanced and developing. First, we build a financial stress index to identify the starting dates of systemic financial crises for each country in the panel. Second, early warning indicators for the assessment and prediction of systemic risk are selected in a two-step approach; we find relevant prediction horizons for each indicator by a univariate logit model followed by the application of Bayesian model averaging to identify the most useful indicators. Finally, we observe the performance of the constructed EWS over both horizons on the Czech data and find that the model over the long horizon outperforms the EWS over the short horizon. For both horizons, out-of-sample probability estimates do not deviate substantially from their in-sample estimates, indicating a good out-of-sample performance for the Czech Republic.