【简报】天美制作果冻视频系列学术研讨会(172)
2016-03-02 00:00:00
2月24日，来自悉尼大学的Chao Wang博士在汇丰商学院335教室为天美制作果冻视频师生带来主题为"Bayesian Semi-parametric Realized-CARE Models for Tail Risk Forecasting Incorporating Range and Realized Measure"的学术研讨会。
Abstract:
A new model framework called Realized Conditional Autoregressive Expectile (Realized-CARE) is proposed, through incorporating a measurement equation into the conventional CARE model, in a manner analogous to the Realized-GARCH model. The intra-day Range and realized measures (e.g. Realized Variance and Realized Range) are employed as the dependent variable in the measurement equation. The measurement equation here models the contemporaneous dependence between the realized measure and the latent conditional expectile. In addition, a targeted search based on a quadratic approximation is proposed that improves the computational speed of estimation of the expectile level parameter. Bayesian adaptive Markov Chain Monte Carlo methods and likelihood-based frequentist methods are proposed for estimation, whilst their properties are compared via a simulation study. Furthermore, the methods of sub-sampling and scaling are applied to the Realized Range, to help deal with the inherent microstructure noise of the realized volatility measures. In a real forecasting study applied to 6 market indices and 3 individual assets, compared to the original CARE, the parametric GARCH and Realized-GARCH models, one-day- ahead Value-at-Risk and Expected Shortfall forecasting results favor the proposed Realized-CARE model, especially when incorporating the Realized Range and the sub-sampled Realized Range.
Abstract:
A new model framework called Realized Conditional Autoregressive Expectile (Realized-CARE) is proposed, through incorporating a measurement equation into the conventional CARE model, in a manner analogous to the Realized-GARCH model. The intra-day Range and realized measures (e.g. Realized Variance and Realized Range) are employed as the dependent variable in the measurement equation. The measurement equation here models the contemporaneous dependence between the realized measure and the latent conditional expectile. In addition, a targeted search based on a quadratic approximation is proposed that improves the computational speed of estimation of the expectile level parameter. Bayesian adaptive Markov Chain Monte Carlo methods and likelihood-based frequentist methods are proposed for estimation, whilst their properties are compared via a simulation study. Furthermore, the methods of sub-sampling and scaling are applied to the Realized Range, to help deal with the inherent microstructure noise of the realized volatility measures. In a real forecasting study applied to 6 market indices and 3 individual assets, compared to the original CARE, the parametric GARCH and Realized-GARCH models, one-day- ahead Value-at-Risk and Expected Shortfall forecasting results favor the proposed Realized-CARE model, especially when incorporating the Realized Range and the sub-sampled Realized Range.
