The Impact of COVID-19 on Chinese Systemic Risk Based on Dynamic Tail-risk Network Model

This book illustrates and assesses the dramatic recent transformations in capital markets worldwide and the impact of those transformations. ‘Market making’ by humans in centralized markets has been replaced by supercomputers and algorithmic high frequency trading operating in often highly fragmented markets. How do recent market changes impact on core public policy objectives such as investor protection, reduction of systemic risk, fairness, efficiency, and transparency in markets? The operation and health of capital markets affect all of us and have profound implications for equality and justice in society. This unique set of chapters by leading scholars, industry insiders, and regulators sheds light on these and related questions and discusses ways to strengthen market governance for the benefit of society at large.

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Impact Mechanism of the Ecological Vulnerability of Highly Developed Islands Based on the Bayesian Network Model—Applied to the Changshan Islands

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18084150 ◽

2021 ◽

Vol 18 (8) ◽

pp. 4150

Author(s):

Keyu Qin ◽

Haijun Huang ◽

Jingya Liu ◽

Liwen Yan ◽

Yanxia Liu ◽

...

Keyword(s):

Bayesian Network ◽

Network Model ◽

Geographical Information ◽

Global Changes ◽

Bayesian Network Model ◽

Island Ecosystems ◽

Impact Mechanism ◽

The North ◽

Ecological Vulnerability ◽

The Impact

Islands are one of the most sensitive interfaces between global changes and land and sea dynamic effects, with high sensitivity and low stability. Therefore, under the dynamic coupling effect of human activities and frequent natural disasters, the vulnerability of the ecological environment of islands shows the characteristics of complexity and diversity. For the protection of island ecosystems, a system for the assessment of island ecosystems and studies on the mechanism of island ecological vulnerability are highly crucial. In this study, the North and South Changshan Islands of China were selected as the study area. Considering various impact factors of island ecological vulnerability, the geographical information systems (GIS) spatial analysis, field surveys, data sampling were used to evaluate island ecological vulnerability. The Bayesian network model was used to explore the impact mechanism of ecological vulnerability. The results showed that the ecological vulnerability of the North Changshan Island is higher than that of the South Changshan Island. Among all the indicators, the proportion of net primary productivity (NPP) and the steep slope has the strongest correlation with ecological vulnerability. This study can be used as references in the relevant departments to formulate management policies and promote the sustainable development of islands and their surrounding waters

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The impact of COVID-19 on tail risk: Evidence from Nifty index options

Economics Letters ◽

10.1016/j.econlet.2021.109878 ◽

2021 ◽

Vol 204 ◽

pp. 109878

Author(s):

Sobhesh Kumar Agarwalla ◽

Jayanth R. Varma ◽

Vineet Virmani

Keyword(s):

Index Options ◽

Tail Risk ◽

The Impact

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Statistical Modeling and Prediction for Tourism Economy Using Dendritic Neural Network

Computational Intelligence and Neuroscience ◽

10.1155/2017/7436948 ◽

2017 ◽

Vol 2017 ◽

pp. 1-9 ◽

Cited By ~ 12

Author(s):

Ying Yu ◽

Yirui Wang ◽

Shangce Gao ◽

Zheng Tang

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Rapid Development ◽

Seasonal Trend ◽

Moving Averages ◽

Forecasting Methods ◽

Tourism Economy ◽

The Impact ◽

D Model

With the impact of global internationalization, tourism economy has also been a rapid development. The increasing interest aroused by more advanced forecasting methods leads us to innovate forecasting methods. In this paper, the seasonal trend autoregressive integrated moving averages with dendritic neural network model (SA-D model) is proposed to perform the tourism demand forecasting. First, we use the seasonal trend autoregressive integrated moving averages model (SARIMA model) to exclude the long-term linear trend and then train the residual data by the dendritic neural network model and make a short-term prediction. As the result showed in this paper, the SA-D model can achieve considerably better predictive performances. In order to demonstrate the effectiveness of the SA-D model, we also use the data that other authors used in the other models and compare the results. It also proved that the SA-D model achieved good predictive performances in terms of the normalized mean square error, absolute percentage of error, and correlation coefficient.

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