The Complex Connectedness of Global Large-Scale Assets and the Visualization of Their Return Spillover Paths

Abstract

Due to the increasing connectedness of international financial markets, the measurement of dynamic connectedness among large-scale assets has become a key component of modern financial risk regulation and asset allocation principles. We quantify the dynamic connectedness among large-scale assets and visualize the return spillover paths using cutting-edge complex network spillover measurement theory and physical complex network methods. For the sample period of January 2, 2018 to June 30, 2022, we calculate the daily returns for thirteen representative global large-scale assets. Then, we construct a time-varying parameter-vector autoregressive-stochastic volatility (TVP-VAR-SV) model and measure the time-varying spillover matrix of returns across large-scale assets. From our analyses, the 10-year U.S. treasury rate (shorted for the US10YR) and Brent oil are found to be the core subject matter of global assets. The US10YR has a significant impact on the commodity market. Moreover, there is a significant impact of foreign exchange on other global large-scale assets, and the spillover effects vary from one country to another. On the basis of the empirical findings, this paper proposes recommendations for financial regulators regarding risk monitoring and forward-looking investment recommendations for financial institutions and investors.