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

Daniela Scidá

Journal of Applied Econometrics, 2023, vol. 38, issue 1, 49-68

Abstract: 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.

Date: 2023
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https://doi.org/10.1002/jae.2935

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