A1000
Title: A semi-parametric dynamic conditional correlation framework for risk forecasting
Authors: Giuseppe Storti - University of Salerno (Italy) [presenting]
Chao Wang - The University of Sydney (Australia)
Abstract: The purpose is to develop a novel multivariate semi-parametric framework for joint portfolio value-at-risk (VaR) and expected shortfall (ES) forecasting. Unlike existing univariate semi-parametric approaches, the proposed framework explicitly models the dependence structure among portfolio asset returns through a dynamic conditional correlation (DCC) parameterization. To estimate the model, a two-step procedure based on the minimization of a strictly consistent VaR and ES joint loss function is employed. This procedure allows for a simultaneous estimate of the DCC parameters and the portfolio risk factors. The performance of the proposed model in risk forecasting on various probability levels is evaluated by means of a forecasting study on the components of the Dow Jones index for an out-of-sample period from December 2016 to September 2021. The empirical results support the effectiveness of the proposed framework compared to a variety of existing approaches.
