Non-Normal Market Losses and Spatial Dependence Using Uncertainty Indices

We analyse spatial dependence between the risks of stock markets. An alternative definition of neighbour is used and is based on a proposed exogenous criterion obtained with a dynamic Google Trends Uncertainty Index (GTUI) designed specifically for this analysis. We show the impact of systemic risk on spatial dependence related to the most significant financial crises from 2005: the Lehman Brothers bankruptcy, the sub-prime mortgage crisis, the European debt crisis, Brexit and the COVID-19 pandemic, which also affected the financial markets. The risks are measured using the monthly variance or volatility and the monthly Value-at-Risk (VaR) of the filtered losses associated with the analysed indices. Given that the analysed risk measures follow non-normal distributions and the number of neighbours changes over time, we carry out a simulation study to check how these characteristics affect the results of global and local inference using Moran's I statistic. Lastly, we analyse the global spatial dependence between the risks of 46 stock markets and we study the local spatial dependence for 10 benchmark stock markets worldwide.

Automated summary

This study examines the spatial dependence between risks in stock markets and investigates the impact of systemic risk on financial markets. By using a dynamic Google Trends Uncertainty Index, we propose an alternative definition of neighbors and explore the risk transmission effects during different financial crises. Additionally, a simulation study is conducted to examine the influence of non-normal risk measures and changing number of neighbors on the results.