Thursday 16 March 2017, 3.00PM to 4.00pm
Speaker(s): Weining Wang, City University London
joint with Xuening Zhu, Hansheng Wang and Wolfgang Haerdle
Abstract: : It is a challenging task to understand the complex dependency structures in an ultra-high dimensional network, especially when one concentrates on the tail dependency. To tackle this problem, we consider a network quantile autoregression model (NQAR) to characterize the dynamic quantile behavior in a complex system. In particular, we relate responses to its connected nodes and node specific characteristics in a quantile autoregression process. A inimum contrast estimation approach for the NQAR model is introduced, and the asymptotic properties are studied with assumptions on the adjacency matrix. Moreover, innovative tail-event driven impulse functions are defined. Finally, we demonstrate the usage of our model by investigating the financial contagions in the Chinese stock market accounting for shared ownership of companies.
Location: Economics Staff Room A/EC202
Admission: Staff & PhD Students