Structural VAR and financial networks: A minimum distance approach to spatial modeling

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.