Title: Decoupling Systemic Risk into Endopathic and Exopathic Competing Risks Through Autoregressive Conditional Accelerated Fréchet Model
Speaker: Prof. Zhengjun Zhang, University of Wisconsin-Madison
Abstract: Identifying systemic risk patterns in geopolitical, economic, financial, environmental, transportation, epidemiological systems and their impacts is the key to risk management. We propose a new nonlinear time series model: autoregressive conditional accelerated Fréchet (AcAF) model and introduce two new endopathic and exopathic competing risk indices for better learning risk patterns, decoupling systemic risk, and making better risk management. We establish the probabilistic properties of stationarity and ergodicity of the AcAF model. Statistical inference is developed through conditional maximum likelihood estimation. The consistency and asymptotic normality of the estimators are derived. Simulation demonstrates the efficiency of the proposed estimators and the AcAF model's flexibility in modeling heterogeneous data. Empirical studies on the stock returns in S&P 500 and the cryptocurrency trading show the superior performance of the proposed model in terms of the identified risk patterns, endopathic and exopathic competing risks, being informative with greater interpretability, enhancing the understanding of the systemic risks of a market and their causes, and making better risk management possible. (Joint work with Jingyu Ji and Deyuan Li).
Time: 16:30-18:00, Friday, Sept. 23, 2022
Venue: Tecent Meeting ID:765-189-025
