Extreme risk spillovers between US and Chinese agricultural futures markets in crises: A dependence-switching copula-CoVaR model
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ABSTRACT:
SUBMITTER: Hu X
PROVIDER: S-EPMC10917274 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:The spillover effect of university-based agricultural research and development (R&D) has been recognized as a crucial factor contributing to the enhanced performance of the agricultural industry. Nonetheless, the psychological impact of organizational inertia on individuals and groups may shape the influence of such spillovers for agribusiness firms. To date, there has been limited exploration of the interplay between university agricultural R&D spillovers and agribusiness firms. Utilizing panel data from Chinese listed agribusiness firms between 2009 and 2019, our empirical investigation reveals a negative short-term relationship between university agricultural R&D spillovers and agribusiness firm performance due to the similarity in knowledge backgrounds. In the short term, organizational inertia, stemming from familiar and parallel knowledge, renders university agricultural R&D spillovers unfavorable to agribusiness firm performance, thereby reducing their value to the enterprises. Conversely, the long-term influence of university agricultural R&D spillovers on agricultural enterprise performance is positive, as organizational inertia dissipates over time. Additionally, our findings indicate that university non-agricultural R&D spillovers serve to positively moderate the relationship between agricultural R&D spillovers and agribusiness firm performance in the short term, while exerting a negative moderating effect in the long term. Lastly, our analysis reveals a negative correlation between the effect of university agricultural R&D spillovers and the geographical distance between agribusiness firms and universities. This suggests that proximity to academic institutions may play a role in shaping the impact of R&D spillovers on agribusiness performance. In summary, our study highlights the complex dynamics between university agricultural R&D spillovers and agribusiness firm performance, revealing both short-term and long-term effects. We also underscore the importance of considering the influence of organizational inertia and the moderating role of non-agricultural R&D spillovers. Understanding these relationships is crucial for informing strategic decisions and fostering innovation within the agricultural industry.
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Project description:Clarifying the time-varying spillovers among pilot carbon emission permit trading markets in China is an important foundation for building the national carbon emission trading market. We calculate the dynamic spillover of carbon price return among the pilot carbon emission permit trading markets in China with the time-varying connectedness approach. The dataset is constructed from transaction data from seven pilot carbon markets in China during the period of June 23, 2014, to December 31, 2020. The quantitative analysis suggests that (i) Beijing and Chongqing carbon emission trading markets are the main spillover markets of carbon price returns, with strong pricing power, while the Guangdong and Tianjin markets are the main receivers of the price return spillover in other pilot carbon emission trading markets. (ii) The spillover effect among China's carbon markets has a strong policy orientation. The improvement and development of the carbon market driven by macroeconomic regulation and control policies can effectively improve the spillover ability of the carbon market, and the market trading activity, namely the volatility of the carbon price return rate, can amplify the spillover ability of the carbon market in the short term. (iii) There exist three types of price return spillover among China's pilot carbon emission trading markets, including central divergence, one-way chain transmission, and circular spillover. Along with the improvement of market operation efficiency, the central divergent type of spillover shifts to the pattern of circular spillover. It is necessary for the government to improve market efficiency and ensure the coordinated development of China's pilot carbon emission trading market and national carbon emission trading market.
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Project description:BackgroundGenes interact with each other as basic building blocks of life, forming a complicated network. The relationship between groups of genes with different functions can be represented as gene networks. With the deposition of huge microarray data sets in public domains, study on gene networking is now possible. In recent years, there has been an increasing interest in the reconstruction of gene networks from gene expression data. Recent work includes linear models, Boolean network models, and Bayesian networks. Among them, Bayesian networks seem to be the most effective in constructing gene networks. A major problem with the Bayesian network approach is the excessive computational time. This problem is due to the interactive feature of the method that requires large search space. Since fitting a model by using the copulas does not require iterations, elicitation of the priors, and complicated calculations of posterior distributions, the need for reference to extensive search spaces can be eliminated leading to manageable computational affords. Bayesian network approach produces a discretely expression of conditional probabilities. Discreteness of the characteristics is not required in the copula approach which involves use of uniform representation of the continuous random variables. Our method is able to overcome the limitation of Bayesian network method for gene-gene interaction, i.e. information loss due to binary transformation.ResultsWe analyzed the gene interactions for two gene data sets (one group is eight histone genes and the other group is 19 genes which include DNA polymerases, DNA helicase, type B cyclin genes, DNA primases, radiation sensitive genes, repaire related genes, replication protein A encoding gene, DNA replication initiation factor, securin gene, nucleosome assembly factor, and a subunit of the cohesin complex) by adopting a measure of directional dependence based on a copula function. We have compared our results with those from other methods in the literature. Although microarray results show a transcriptional co-regulation pattern and do not imply that the gene products are physically interactive, this tight genetic connection may suggest that each gene product has either direct or indirect connections between the other gene products. Indeed, recent comprehensive analysis of a protein interaction map revealed that those histone genes are physically connected with each other, supporting the results obtained by our method.ConclusionThe results illustrate that our method can be an alternative to Bayesian networks in modeling gene interactions. One advantage of our approach is that dependence between genes is not assumed to be linear. Another advantage is that our approach can detect directional dependence. We expect that our study may help to design artificial drug candidates, which can block or activate biologically meaningful pathways. Moreover, our copula approach can be extended to investigate the effects of local environments on protein-protein interactions. The copula mutual information approach will help to propose the new variant of ARACNE (Algorithm for the Reconstruction of Accurate Cellular Networks): an algorithm for the reconstruction of gene regulatory networks.
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Project description:This paper studies the US and global economic fundamentals that exacerbate emerging stock markets volatility and can be considered as systemic risk factors increasing financial stability vulnerabilities. We apply the bivariate HEAVY system of daily and intra-daily volatility equations enriched with powers, leverage, and macro-effects that improve its forecasting accuracy significantly. Our macro-augmented asymmetric power HEAVY model estimates the inflammatory effect of US uncertainty and infectious disease news impact on equities alongside global credit and commodity factors on emerging stock index realized volatility. Our study further demonstrates the power of the economic uncertainty channel, showing that higher US policy uncertainty levels increase the leverage effects and the impact from the common macro-financial proxies on emerging markets' financial volatility. Lastly, we provide evidence on the crucial role of both financial and health crisis events (the 2008 global financial turmoil and the recent Covid-19 pandemic) in raising markets' turbulence and amplifying the volatility macro-drivers impact, as well.Supplementary informationThe online version supplementary material available at 10.1007/s10479-021-04042-y.
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