Bayesian SAR model with stochastic volatility and multiple time-varying weights

Michele Costola, Matteo Iacopini and Casper Wichers

No 407, SAFE Working Paper Series from Leibniz Institute for Financial Research SAFE

Abstract: A novel spatial autoregressive model for panel data is introduced, which incorporates multilayer networks and accounts for time-varying relationships. Moreover, the proposed approach allows the structural variance to evolve smoothly over time and enables the analysis of shock propagation in terms of time-varying spillover effects. The framework is applied to analyse the dynamics of international relationships among the G7 economies and their impact on stock market returns and volatilities. The findings underscore the substantial impact of cooperative interactions and highlight discernible disparities in network exposure across G7 nations, along with nuanced patterns in direct and indirect spillover effects.

Keywords: Bayesian inference; International relationships; Multilayer networks; Spatial autoregressive model; Time-varying networks; Stochastic volatility (search for similar items in EconPapers)
JEL-codes: C11 C33 C51 C58 (search for similar items in EconPapers)
Date: 2023
New Economics Papers: this item is included in nep-ecm, nep-net and nep-ure
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:safewp:279783

DOI: 10.2139/ssrn.4620913

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