Information Feedback in Temporal Networks as a Predictor of Market Crashes

摘要: In complex systems, statistical dependencies between individual components are often considered one of the key mechanisms which drive system dynamics observed on a macroscopic level. this paper, we study cross-sectional time-lagged in financial markets, quantified by nonparametric measures from information theory, and estimate directed temporal dependency networks markets. We examine emergence strongly connected feedback estimated networks, hypothesize that existence induces strong spatiotemporal spillover effects thus indicates systemic risk. obtain empirical results applying our methodology stock market real estate data, demonstrate exhibit around periods high volatility To further phenomenon, construct risk indicator based proposed approach, show it can be used to predict future distress. Results both data suggest approach useful obtaining early-warning signals for crashes