Structural VAR and financial networks: A minimum distance approach to spatial modeling
将时间序列空间模型解释为受约束的结构向量自回归模型，提出最小距离方法估计网络矩阵和网络影响参数，并应用于2004-2018年主要股市的波动率溢出分析。
Summary In this paper, I interpret a time series spatial model (T‐SAR) as a constrained structural vector autoregressive (SVAR) model. Based on these restrictions, I propose a minimum distance approach to estimate the (row‐standardized) network matrix and the overall network influence parameter of the T‐SAR from the SVAR estimates. I also develop a Wald‐type test to assess the distance between these two models. To implement the methodology, I discuss machine learning methods as one possible identification strategy of SVAR models. Finally, I illustrate the methodology through an application to volatility spillovers across major stock markets using daily realized volatility data for 2004–2018.