Modellazione della Dipendenza della Coda

This thesis presents an empirical study to investigate the tail dependence between mortality risk and market risk. We analyze the most extreme events over a sample spanning approximately one hundred years and including the recent pandemic emergency. An empirical analysis is conducted using historical data from six different countries, encompassing two macroeconomic variables, two financial variables, and an additional variable to construct a mortality indicator. Multiple dependence measures are employed to assess the extent of the dependence between the two risks and to examine dependence's distinct behavior in the tails and in the bulk. The results appear more conclusive and consistent across different dependence measures when examining the financial market, particularly the stock market. In fact, for the stock index the linear and rank correlations appear closer to zero in the full sample and show an increase in absolute value in the tail sample. Kendall’s tau and Spearman’s rho seem to support the same dependence directions provided by Pearson’s rho coefficients. The coefficients of tail dependence are harder to interpret and would likely be more informative with data points that produce fatter tails. The employed mortality indicator shows that there have been more severe mortality crises compared to the COVID period. The lack of a significant mortality shock limits our ability to explore very extreme events. This may help explaining why only weak effects in terms of tail correlation can be concluded.