A0695
Title: Adaptive combinations of tail-risk forecasts
Authors: Alessandra Amendola - University of Salerno (Italy) [presenting]
Vincenzo Candila - University of Salerno (Italy)
Antonio Naimoli - University of Salerno (Italy)
Giuseppe Storti - University of Salerno (Italy)
Abstract: The continuous evolution of financial markets highlights how quantitative financial risk management has become a key tool in investment decisions, capital allocation, and regulation. Although several methods have been proposed to estimate the risk of an investment in capital markets, value-at-risk (VaR) and expected shortfall (ES) can be considered standard measures of market risk, as they are used both for internal control of financial institutions and for regulatory purposes. In this direction, modelling and estimation methods selection for VaR and ES play a critical role. Nowadays, a variety of possibilities is available. For instance, there are models belonging to parametric, semi-parametric, and non-parametric methods. Moreover, several error distributions could be considered among the class of parametric models. Also, some models allow for the use of variables mixed at different frequencies. To mitigate the impact of these sources of uncertainty, a forecast combination strategy is proposed by adaptively weighting the pool of most accurate predictors based on the model confidence set (MCS) results. The empirical analysis suggests that combinations of VaR and ES forecasts lead to higher predictive accuracy over a wide range of competitors.
