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. The methodology is illustrated through an application to volatility spillovers across major stock markets using daily realized volatility data for 2003-2015