The study intends to conduct a systematic mechanism for herding detection in the Hong Kong stock market. We take stocks from three market sectors as samples and investigate the existence of herding in the two periods: before and during the outbreak of COVID-19 in Hong Kong, from August 2019 to July 2020. We adopt CCK model-based OLS and quantile regression to examine herding in each observed period and employ HS model to measure the magnitude of herding during the time. The empirical results indicate the emergence of mild herding from August 2019 to January 2020, and the herding phenomenon is generally weakened between February and July 2020. Our study confirms the implication of the systematic herding detection mechanism that can improve the sensitivity of detection and capture the magnitude and variation of herding.

In this study, the volatilities of CSI300 index futures, SSE50 index futures, and CSI500 index futures are modeled and predicted based on both parametric and nonparametric modeling approaches. Four ARMA-GARCH-type models and four HAR-type models are taken as the framework of volatility prediction. The last one-third of transaction data are used as the testing sample and the rolling window approach is adopted for prediction. The best predictive models for these three stock index futures vary with the properties of the futures, while volatility prediction based on the HAR-type models always has a higher accuracy than the ARMA-GARCH-type models. Moreover, we find that the property of target assets influences the performance of models, and the choice of extended models in prediction is suggested to be based on the peculiarity in the sample.

Digital-economy patents are widely read as a marker of regional technological capability, yet whether they actually reach the market, and by what route, is a question that the literature has left largely unanswered. This study assembles a panel of 31 provincial-level administrative regions in the Chinese mainland covering 2012-2024 and pursues three questions in sequence: whether digital economy patents raise regional e-commerce development, whether the platform economy carries part of that effect, and under which regional conditions the effect becomes larger. Two-way fixed-effects estimates put the elasticity of e-commerce sales with respect to digital economy patents at about 0.222, which remains robust when excluding centrally administered municipalities, dropping the pandemic period, and instrumenting digital economy patents with historical telecommunications infrastructure. Mediation tests attribute roughly one-fifth of the total effect to platform-economy expansion, a decomposition that bias-corrected bootstrap resampling confirms at conventional levels. Heterogeneity work along three axes delivers a consistent picture: the eastern provinces, and those with tighter labour markets and narrower urban-rural income gaps, convert patented digital technology into e-commerce turnover far more effectively than the rest. A moderation exercise, reported in the final empirical table, adds a financial dimension to this picture. The marginal elasticity rises from 0.124 to 0.258 as regional financial development moves from one standard deviation below to one standard deviation above its mean, which suggests that credit supply and patent collateral behave as complements in the commercialization of digital technology rather than as substitutes.