Real estate prices and systemic banking crises

The collapse of real estate prices has historically jeopardized banking stability and triggered systemic banking crises. This paper studies risk contagion in a banking system in real estate price shock by adopting complex network theory. Modelling the real estate-related asset as a common exposure of banks to the real estate market, we propose a model that incorporates two main risk contagion channels, i.e., the financial network and asset fire sales, and reveal how the real estate price shock is transmitted and propagated across banks. We demonstrate that banking stability is highly sensitive to the real estate price shock. Moreover, due to the particularly low liquidity of the real estate market, the asset fire-sales of real estate assets overwhelms the financial network, playing the dominant role in risk contagion. Our model can be adopted by regulators to conduct stress testing and to forge effective risk management strategies.     

Using artificial neural network and multivariate analysis techniques to evaluate road operating conditions

Regional paved roads are low volume roads with a prevalence of heavy traffic. In the world, these roads concern about 80% of the total road network; however, the traffic that affects these roads is about 20%. Since regional roads are characterized by weak demand, budget for their management/maintenance is very low. This produces considerable difficulties in the choice of strategies for maintenance planning and scheduling. For this reason, the recurring topics of research in this field deal with typical roads issues and aim to develop low cost tools and methods. The study proposes a decision support system to evaluate regional paved roads operating condition in relation to the hydrogeological situation. In particular, the system allows to evaluate in a quick and easy manner, the operating conditions of the road, through low-cost tools (i.e. using low economic resources). This is very useful in the case of LVRs because administrations for these roads have a limited budget. The procedure is developed on a regional paved roads network based on more than 80 roads located in Southern Italy. Data is collected by direct surveys in the field and is integrated with cartography and information available in road agency records. From data analysis, obtained using two different techniques, an easy and quick use procedure is made. In particular, Model 1 is built through multivariate analysis and Model 2 using the artificial neural network (ANN) technique. The results show the validity of the two models in Regional paved roads operating conditions estimation in relation to hydrogeological situations of sites. Both models show good reliability. In particular, the first model (Model 1) is characterized by a high level of significance (p < 0.01) and by a coefficient of determination equal to 0.82. Comparative tests between the second model (Model 2) on which standard tests cannot be performed for obvious reasons, and the first model (Model 1). The results show that the ANN model (model 2), characterized by lower residual, simulates more accurately than the second (Model 1).     

The Internet of Things as an accelerator of advancement of broadband networks: A case of Thailand

The advanced development of broadband access is an inevitable step toward supportive daily lives and sustainable economic growth. However, the decisive outcome of the development relies heavily on the demand side. Consumer choice among advanced Internet broadband technologies will determine the achievement of the development. This study aims to illustrate that consumer decisions on choices of advanced Internet access are influenced by the emergence of the Internet of Things (IoT) applications. A bivariate probit model is applied to examine the effects of IoT, consumer's individual characteristics, and their current use of networks on their decision pertaining to the choice of advanced Internet access. Using Thailand as a case study, the result indicates that the emergence of IoT applications accelerates the adoption of both advanced mobile broadband (5G) and advanced fixed broadband (FTTH). This finding may help policy makers to optimize the future direction of the Internet infrastructure development in Thailand, and also countries in a similar stage.     

Virtual interlining within the European airport network: An airfare analysis

Virtual interlining, which covers the actively marketed or ‘non-hidden’ segment of all potential self-connecting flight itineraries, is often assumed to be a money-saving travel strategy. In this paper we assess the price difference between virtual interlined and ‘traditional’ flight itineraries within the intra-European airport network. We query Kiwi.com's recently developed Tequila platform, one of the few specialised online travel agencies (OTAs) offering both ‘traditional’ and virtual interlined flight itineraries, to obtain information on all available flights in the first week of August, October and December 2019. Using a series of sign tests, we investigate whether a statistically significant fare difference exists between the cheapest available (direct and/or indirect) ‘traditional’ and virtual interlined flight itineraries. Our results indicate a statistically significant fare difference between the cheapest indirect ‘traditional’ and the virtual interlined flight itineraries in favour of the latter. However, with regard to direct traditional flight itineraries the results are mixed. We explore the size and the scope of these patterns in more detail, and outline possible avenues for further research.     

Enhanced network effects and stochastic modelling in generation expansion planning: Insights from an insular power system

Electricity generation capacity expansion is driven by both economic and socio-political realities. Policy makers determine public infrastructural decisions, such as climate and renewable targets, and transmission infrastructure, and the optimal generation capacity expansion follows. Policy makers therefore require planning models that can determine the optimal generation capacity mix in the long run under various scenarios, including policy choices. This work presents a planning model based on linearised alternating current optimal power flow which determines optimal generation capacity expansion and operation, in a least-cost manner, given global and local technical constraints, as well as policy decisions. We apply the model to a test case of the island of Ireland, which has two weakly interconnected systems, high renewable generation targets and low storage and interconnection. We determine the optimal generation expansion and operation out to 2030 considering the effects of increased multi-area interconnection, existing fossil fuel generation phase-out and increased renewable generation targets and carbon prices. Our results find that costs and emissions are driven primarily by the decommissioning of old inefficient generation units. High renewable targets, on the other hand, render increased carbon prices relatively ineffective in reducing system emissions. Furthermore, high renewable generation targets crowd out low-carbon power generation options such as carbon capture and storage (CCS). The strategic north-south interconnection has little effect on renewable energy source installations required to achieve renewable power generation targets but does impact on security of supply and the congestion level across the island.     

基于双层规划模型的冷链物流网络多目标优化

针对冷链物流时效性强这一特性,应用软时间窗反映客户满意度,并结合T.T.T理论换算货损成本。在考虑满足客户时间窗的条件下,寻找配送中心建设及操作成本、车辆成本、惩罚成本及货损成本所构成的总成本最小时的最优配送方案,建立冷链物流配送选址及路径优化的双层规划模型,并将改进的遗传算法运用到该模型中,借助Matlab软件对具体实例进行求解,得到最优结果,为冷链物流网络多目标优化问题提供理论依据。     

基于人工神经网络的互联网公司估值方法研究

资本市场认为互联网公司市值的驱动因素应包括盈利因子、运营因子、流量因子和协同因子。将协同效应指标考虑到公司估值体系中,意图构造互联网公司优化估值模型。使用美股上市的互联网企业数据建立了评价指标体系,通过因子分析实现了二级指标降维,通过实证分析确认了四个因子与公司市值的相关关系,最后构建了基于人工神经网络BP算法的互联网公司估值模型,通过预测数据的检验发现模型的准确度较高。随着2018年互联网公司美股上市潮的持续,该模型能有效为资本市场估值提供参考。     

Connectedness and systemic risk spillovers analysis of Chinese sectors based on tail risk network

This paper investigates the systemic risk spillovers and connectedness in the sectoral tail risk network of Chinese stock market, and explores the transmission mechanism of systemic risk spillovers by block models. Based on conditional value at risk (CoVaR) and single index model (SIM) quantile regression technique, we analyse the tail risk connectedness and find that during market crashes, stock market exposes to more systemic risk and more connectedness. Further, the orthogonal pulse function shows that Herfindahl-Hirschman Index (HHI) of edges has a significant positive effect on systemic risk, but the impact shows a certain lagging feature. Besides, the directional connectedness of sectors shows that systemic risk receivers and transmitters vary across time, and we adopt PageRank index to identify systemically important sector released by utilities and financial sectors. Finally, by block model we find that the tail risk network of Chinese sectors can be divided into four different spillover function blocks. The role of blocks and the spatial spillover transmission path between risk blocks are time-varying. Our results provide useful and positive implications for market participants and policy makers dealing with investment diversification and tracing the paths of risk shock transmission.     

简析数据加密技术在计算机网络安全中的应用

随着我国科学技术的不断发展,计算机被广泛应用于各行各业的发展建设以及人们日常生活和学习中,在给人们带来较大便利的同时,也存在安全隐患。网络安全问题威胁到了计算机信息的保密性,给计算机的可持续发展带来了安全隐患,若用户的信息被泄露会给用户带来巨大的财产损失及心理伤害。因此,出现了数据加密技术,其为计算机网络安全提供了很好的保障。基于此,论文对数据加密技术在提高网络安全性中的应用方面作了简要阐述。