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A0395
Title: A semi-parametric realized joint quantile regression framework for financial tail risk forecasting Authors:  Chao Wang - The University of Sydney (Australia) [presenting]
Richard Gerlach - University of Sydney (Australia)
Abstract: A new realized joint Value at Risk (VaR) and expected shortfall (ES) quantile regression framework is proposed, through incorporating a measurement equation into the joint quantile regression model. The measurement equation models the contemporaneous dependence between the realized measures (e.g. Realized Variance and Realized Range) and the latent conditional quantile. Further, sub-sampling and scaling methods are applied to both the realized range and realized variance, to help deal with inherent micro-structure noise and inefficiency. An adaptive Bayesian Markov Chain Monte Carlo method is employed for estimation and forecasting, whose properties are assessed and compared with maximum likelihood through simulation study. In a forecasting study applied to 7 market indices and 2 individual assets, compared to a range of parametric, non-parametric and semi-parametric models, including GARCH, Realized-GARCH, CARE and the original joint VaR and ES quantile regression models, one-day-ahead Value-at-Risk and Expected Shortfall forecasting results favor the proposed models, especially when incorporating the sub-sampled Realized Variance and the sub-sampled Realized Range.