By employing two systemic risk methods, the marginal expected shortfall (MES) and the component expected shortfall (CES), this paper measures the systemic risk level of all sectors in China’s financial market from 2014 to 2022; thereby, it researches the total effect of sectoral systemic risk using a panel event study model during the three main emergency crisis events. Moreover, two nonparametric methods are utilized, the Wilcoxon signed rank sum test and the bootstrap Kolmogorov–Smirnov test, in order to investigate the changes in individual effects and the dominant ranks of sectoral systemic risk. The empirical results show that (1) the mean values and volatilities of CES and MES of all sectors have a higher level of magnitude in the extreme risk status than those in the normal risk status; (2) by comparing the total effects of three crisis events, we find that different from the continuous shock effect caused by two other events, sectoral systemic risk has a hysteresis effect on the entire market after the outbreak of COVID-19; (3) the long-term and short-term individual effects of sectoral systemic risk in all sectors are different from each other during three events; and (4) the dominance tests of MES are more sensitive and thus better demonstrate the changes in the rankings of sectoral systemic risk than the dominant tests of CES during the emergency crisis events.
The past decades witness the rise and proliferation of online reputation systems. These reputation systems are vulnerable to malicious attacks, and most recent studies have focused on how to better defend the system. This paper aims to analyze the optimal attacking strategy, especially when considering the "message-based persuasion" phenomenon where users' ratings tend to be influenced by earlier ones. Based on a simple model of users' herding behavior in reputation systems, we study how attackers can explore this phenomenon to attack the system more effectively, and quantitatively analyze the optimal attacking strategies. This investigation is critical to the design of defensive mechanisms, and to the protection of online reputation systems.
This study investigates the spillover and contagion effects of systemic risk among Chinese financial institutions in terms of the conditional Value-at-Risk method and spatial econometric techniques. We construct different representative spatial weight matrices to demonstrate various risk connective categories and contagion channels. The spatial autoregression model is built to reveal the different magnitudes of systemic risk contagion effects and extended as the spatial quantile regression model to measure the change in spillovers across quantiles. The results highlight that the spatial agglomeration pattern of institution-level systemic risk is highly concentrated within the same sector but highly disparate between the different sectors. The closeness of the asset price channel and the information channel would enhance the systemic risk spillover effects among institutions. The higher the single institution’s systemic risk level is, the stronger its spillovers among all of them within the same financial department, yet contrarily, the spillovers are larger at lower quantiles between different sectors due to the disparate spatial tendency of systemic risk. Besides, the spillover effects across quantiles indicate the higher tail contagion of systemic risk spatial spillovers, especially during 2015 A-share market crash and 2020 COVID-19 outbreak.
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