Research on the Dynamic Volatility Relationship between Chinese and U.S. Stock Markets Based on the DCC-GARCH Model under the Background of the COVID-19 Pandemic

This study utilizes the Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model to investigate the dynamic relationship between Chinese and U.S. stock markets amid the COVID-19 pandemic. Initially, a univariate GARCH model is developed to derive residual sequences, which are then used to estimate the DCC model parameters. The research reveals a significant rise in the interconnection between the Chinese and U.S. stock markets during the pandemic. The S&P 500 index displayed higher sensitivity and greater volatility in response to the pandemic, whereas the CSI 300 index showed superior resilience and stability. Analysis and model estimation suggest that the market’s dependence on historical data has intensified and its sensitivity to recent shocks has heightened. Predictions from the model indicate increased market volatility during the pandemic. While the model is proficient in capturing market trends, there remains potential for enhancing the accuracy of specific volatility predictions. The study proposes recommendations for policymakers and investors, highlighting the importance of improved cooperation in international financial market regulation and investor education.

CRISPR/Cas基因组编辑技术在乳酸菌中的应用及研究展望

乳酸菌是一类重要的食品工业微生物,目前对其功能基因鉴定和挖掘优良功能基因主要依赖于传统的基因同源重组技术,该技术尽管有较高的可靠性,但是存在操作繁琐、效率低下等不足,严重制约了乳酸菌优良菌株的遗传选育。CRISPR/Cas基因编辑技术极大提升了对多物种基因组的编辑效率,这为乳酸菌功能基因的快速鉴定及遗传改良提供了可能,但是现有的CRISPR/Cas基因编辑技术在乳酸菌的应用还存在诸多限制。本文综述了CRISPR/Cas基因编辑技术在乳酸菌基因组上的应用现状及亟待解决的问题,并展望了乳酸菌基因组编辑技术的未来发展趋势,为乳酸菌功能基因鉴定及遗传改良提供参考。