Observer-Based Dynamic Event-Triggered Semiglobal Bipartite Consensus of Linear Multi-Agent Systems With Input Saturation.

Observer-based dynamic event-triggered semiglobal bipartite consensus (SGBC) is investigated for linear multi-agent systems (MASs) with input saturation under a competitive network. Based on the estimated relative information and low-gain feedback technology, distributed dynamic event-triggered control (DETC) protocols are proposed for solving the observer-based SGBC problems for MASs under a fixed topology and a jointly connected topology, respectively. It is turned out that the SGBC of MASs can be achieved under the proposed protocols. By using gauge transformation and the Lyapunov theory, the bipartite consensus conditions are obtained. Moreover, Zeno behaviors will be excluded. Finally, two simulation examples are presented to verify the theoretical results efficiently.

Quantifying interconnectedness and centrality ranking among financial institutions with TVP-VAR framework.

Financial risk is spread and amplified through the interconnectedness among financial institutions. We apply a time-varying parameter vector autoregression model to analyze the dynamic spillover effects in the Chinese financial system. We find that the 2017 house price control policies have significantly increased the risk of China's financial system. Before 2017, with the prosperity of the real estate market, the interconnectedness of the Chinese financial system continued to decline, while after 2017, with the slowdown of house price growth and the downturn of the real estate market, the interconnectedness turned to increase. For different sectors, the trends and the magnitudes of the spillover effects are diverse, and any sector can contribute to systemic risk in a dynamic way. Finally, we rank 20 systemically important financial institutions according to two centrality measures. The stable institution ranking provides less noisy information for regulators to formulate a policy and intervene in the market effectively.

Modeling aggressive market order placements with Hawkes factor models.

Price changes are induced by aggressive market orders in stock market. We introduce a bivariate marked Hawkes process to model aggressive market order arrivals at the microstructural level. The order arrival intensity is marked by an exogenous part and two endogenous processes reflecting the self-excitation and cross-excitation respectively. We calibrate the model for a Shenzhen Stock Exchange stock. We find that the exponential kernel with a smooth cut-off (i.e. the subtraction of two exponentials) produces much better calibration than the monotonous exponential kernel (i.e. the sum of two exponentials). The exogenous baseline intensity explains the U-shaped intraday pattern. Our empirical results show that the endogenous submission clustering is mainly caused by self-excitation rather than cross-excitation.

Direct determination approach for the multifractal detrending moving average analysis.

In the canonical framework, we propose an alternative approach for the multifractal analysis based on the detrending moving average method (MF-DMA). We define a canonical measure such that the multifractal mass exponent τ(q) is related to the partition function and the multifractal spectrum f(α) can be directly determined. The performances of the direct determination approach and the traditional approach of the MF-DMA are compared based on three synthetic multifractal and monofractal measures generated from the one-dimensional p-model, the two-dimensional p-model, and the fractional Brownian motions. We find that both approaches have comparable performances to unveil the fractal and multifractal nature. In other words, without loss of accuracy, the multifractal spectrum f(α) can be directly determined using the new approach with less computation cost. We also apply the new MF-DMA approach to the volatility time series of stock prices and confirm the presence of multifractality.