Multiple Testing For The Topology Of Financial Networks

Abstract

This paper advances the econometric analysis of network connectedness by introducing exact simulation-based inference methods to assess pairwise and aggregated spillover effects among variables in vector autoregressive models. While the estimation of connectedness using forecast error variance decompositions is well-established, our contribution lies in the development of hypothesis testing procedures that provide a statistical foundation to formally test the significance of connectedness measures. To address the multiple testing issue, we present detailed algorithms for both single-step and step-down p-value adjustments to effectively control the familywise error rate in finite samples. We also extend our methodology to include group-based analysis, thereby broadening the applicability of our approach. Simulation results confirm that our procedures not only ensure the simultaneous finite-sample correctness of the set of inferences but also demonstrate good statistical power. We illustrate our inference procedures through empirical analyses of return and volatility spillovers among global stock markets, revealing new insights into financial market linkages and the vulnerability of financial networks to contagion.

Short bio

Richard Luger is a Professor in the Department of Finance, Insurance and Real Estate at Université Laval (Québec City, Canada). He has previously held positions at the Bank of Canada, Emory University, and Georgia State University. His research is in financial econometrics, with a focus on financial risk modelling, dependence structure testing, and simulation-based inference. His work has appeared in leading journals, including the Journal of Econometrics, the Journal of Financial Econometrics, and the International Journal of Forecasting. He has been a visiting researcher at Melbourne Business School and Monash University, and will be based at the University of Melbourne in April–May 2026.