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Title: Combining dynamic conditional quantile functions with a viewtowards tail risk management Authors:  Pierluigi Vallarino - Aarhus BSS (Denmark) [presenting]
Alessandra Luati - University of Bologna (Italy)
Leopoldo Catania - Aarhus BBS (Denmark)
Abstract: A new method is introduced to model the quantiles of a time series using all past information on a set of explanatory variables and on the time series interest. The resulting quantiles: do not cross over time, have dynamics which enhance the information set available to extreme quantiles, and incorporate information coming from all regions of the conditional distributions of explanatory variables. Parameters of the model are estimated through a two-stage quasi-maximum likelihood estimator (2SQMLE). Consistency and asymptotic normality of the 2SQMLE are derived, and its finite sample properties are assessed through a simulation study. An empirical analysis concerning macro-financial variables reveals a tight connection between financial and macroeconomic tail risk, and shows that the model delivers competitive density and tail risk predictions.