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 financial integration among countries based on daily realized volatility data for 2003-2015.

• Publication year

  2018

• Venue

  Journal of applied econometrics

• Publication date

  2018-04-14

• Fields of study

  Mathematics, Business, Economics, Computer Science

• Identifiers
  DOI 10.2139/SSRN.2860866
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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