Contagion or interdependence? Comparing spillover indices

Empirical Economics. 2022. P. 1403–1455.

Islam R., Volkov V.

We propose a novel risk measure that is built on comparing high-frequency time-varying volatility and low-frequency return spillover estimates. This measure permits to identify the markets that are epidemic in their complex interdependence. We conjecture that initially a highly volatile market experiences episodes of risk transmission, but only later absorbs risk and becomes an epidemic market. Moreover, we can detect newly emerging ‘contagion’ in the system. We examine the behaviour of 30 global equity markets and compare spillover measures, which encapsulate many large and small crises episodes. Instead of relying on ex post crisis information, our model identifies crises periods. An important implication of the proposed approach is that highly interrelated markets, such as China, are less likely to transmit a global economic crisis under the current interdependence setting.

Keywords: ARTIFICIAL NEURAL NETWORKS, Contagion effect