Web Appendix of 'Structural VAR and Financial Networks: A Minimum Distance Approach to Spatial Modeling'

26 Pages Posted: 11 Mar 2020 Last revised: 12 Jun 2021

See all articles by Daniela Scida

Daniela Scida

Quantitative Supervision and Research - Federal Reserve Bank of Richmond

Date Written: March 2, 2020

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

Keywords: financial networks, SVAR models, spatial models, minimum distance estimation, financial crisis

JEL Classification: C3, C58, C45, G15

Suggested Citation

Scida, Daniela, Web Appendix of 'Structural VAR and Financial Networks: A Minimum Distance Approach to Spatial Modeling' (March 2, 2020). Available at SSRN: https://ssrn.com/abstract=3544990 or http://dx.doi.org/10.2139/ssrn.3544990

Daniela Scida (Contact Author)

Quantitative Supervision and Research - Federal Reserve Bank of Richmond ( email )

Charlotte, NC
United States

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