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Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/185282
Non-Normal Market Losses and Spatial Dependence Using Uncertainty Indices
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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.
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BOLANCÉ LOSILLA, Catalina, ACUÑA, Carlos and TORRA PORRAS, Salvador. Non-Normal Market Losses and Spatial Dependence Using Uncertainty Indices. Mathematics. 2022. Vol. 10(8), num. 1317, pags. 1-23. ISSN 2227-7390. [consulted: 18 of June of 2026]. Available at: https://hdl.handle.net/2445/185282