B0448
Title: Stock co-jump networks
Authors: Yi Ding - The University of Macau (China) [presenting]
Yingying Li - Hong Kong University of Science and Technology (Hong Kong)
Guoli Liu - Hong Kong University of Science and Technology (Hong Kong)
Xinghua Zheng - HKUST (China)
Abstract: A Degree-Corrected Block Model with Dependent Multivariate Poisson edges (DCBM-DMP) is proposed to study stock co-jump dependency. To estimate the community structure, we extend the SCORE algorithm and develop a Spectral Clustering On Ratios-of-Eigenvectors for networks with Dependent Multivariate Poisson edges (SCORE-DMP) algorithm. We prove that SCORE-DMP enjoys strong consistency in community detection. Empirically, using high-frequency data of S\&P 500 constituents, we construct two co-jump networks according to whether the market jumps and find that they exhibit different community features than GICS. We further show that the co-jump networks help in stock return prediction.
