This paper reconsiders the roles of China and some developed countries in the network of carbon emission transfers via international trade in value added from a new perspective of network governance.Network search int...This paper reconsiders the roles of China and some developed countries in the network of carbon emission transfers via international trade in value added from a new perspective of network governance.Network search intensity(NSI)and the extended gravity model are used with cross-country panel data to analyze the mechanism of China's engagement in network governance of carbon emission transfers.The results show that from 2000 to 2009,China was a net exporter of carbon emissions,even though it shifted from the semi-periphery to the core in the network of carbon emissions embodied in imports.Meanwhile,NSI had a significant positive impact on carbon emissions embodied in exports.Given China's important role in the global production network and division of labor,NSI may also affect industrial structure and the quality of the ecological environment to a large extent.This study analyses the network governance mechanism of China's participation in global carbon transfers.The results suggest that the technical complexity of export products and product heterogeneity do not change the positive impact of NSI on carbon emissions.展开更多
This paper develops a new class of multivariate models for large-dimensional time-varying covariance matrices,called Cholesky generalized autoregressive score(GAS)models,which are based on the Cholesky decomposition o...This paper develops a new class of multivariate models for large-dimensional time-varying covariance matrices,called Cholesky generalized autoregressive score(GAS)models,which are based on the Cholesky decomposition of the covariance matrix and assume that the parameters are score-driven.Specifically,two types of score-driven updates are considered:one is closer to the GARCH family,and the other is inspired by the stochastic volatility model.We demonstrate that the models can be estimated equation-wise and are computationally feasible for high-dimensional cases.Moreover,we design an equationwise dynamic model averaging or selection algorithm which simultaneously extracts model and parameter uncertainties,equipped with dynamically estimated model parameters.The simulation results illustrate the superiority of the proposed models.Finally,using a sizeable daily return dataset that includes 124 sectors in the Chinese stock market,two empirical studies with a small sample and a full sample are conducted to verify the advantages of our models.The full sample analysis by a dynamic correlation network documents significant structural changes in the Chinese stock market before and after COVID-19.展开更多
基金the National Social Science Foundation of China(Nos.21BJL102 and 18BJL118)the Major Program of National Social Science Foundation of China(No.21&ZD109)+2 种基金the National Natural Science Foundation of China(Nos.72074186 and 71673230)the Basic Scientific Center Project of National Science Foundation of China(No.71988101)the Fundamental Research Funds for the Central Universities concerned Chinese Modernization(No.20720231061).
文摘This paper reconsiders the roles of China and some developed countries in the network of carbon emission transfers via international trade in value added from a new perspective of network governance.Network search intensity(NSI)and the extended gravity model are used with cross-country panel data to analyze the mechanism of China's engagement in network governance of carbon emission transfers.The results show that from 2000 to 2009,China was a net exporter of carbon emissions,even though it shifted from the semi-periphery to the core in the network of carbon emissions embodied in imports.Meanwhile,NSI had a significant positive impact on carbon emissions embodied in exports.Given China's important role in the global production network and division of labor,NSI may also affect industrial structure and the quality of the ecological environment to a large extent.This study analyses the network governance mechanism of China's participation in global carbon transfers.The results suggest that the technical complexity of export products and product heterogeneity do not change the positive impact of NSI on carbon emissions.
基金The authors would like to acknowledge that this work is supported by the Basic Scientific Center of National Science Foundation of China(Project 71988101)the Humanities and Social Science Fund of Ministry of Education of the People's Republic of China under Grant No.22JJD790050+4 种基金the National Natural Science Foundation of China,General Program under Grant No.71973110 and No.72373125the National Natural Science Foundation of China,Key Program under Grant No.72033008the Fundamental Research Funds for the Central Universities under Grant No.20720191072the Statistical Science Research Program of China under Grant No.2022LZ37 and No.2022LZ06the Cultivation Program of Financial Security Collaborative Innovation Center,Southwestern University of Finance and Economics under Grant No.JRXTP202202.
文摘This paper develops a new class of multivariate models for large-dimensional time-varying covariance matrices,called Cholesky generalized autoregressive score(GAS)models,which are based on the Cholesky decomposition of the covariance matrix and assume that the parameters are score-driven.Specifically,two types of score-driven updates are considered:one is closer to the GARCH family,and the other is inspired by the stochastic volatility model.We demonstrate that the models can be estimated equation-wise and are computationally feasible for high-dimensional cases.Moreover,we design an equationwise dynamic model averaging or selection algorithm which simultaneously extracts model and parameter uncertainties,equipped with dynamically estimated model parameters.The simulation results illustrate the superiority of the proposed models.Finally,using a sizeable daily return dataset that includes 124 sectors in the Chinese stock market,two empirical studies with a small sample and a full sample are conducted to verify the advantages of our models.The full sample analysis by a dynamic correlation network documents significant structural changes in the Chinese stock market before and after COVID-19.