A semi-parametric joint Value-at-Risk (VaR) and Expected Shortfall (ES) forecasting framework employing multiple realized measures is developed. The proposed framework extends the realized exponential GARCH model to be semi-parametrically estimated, via a joint loss function, whilst extending existing quantile time series models to incorporate multiple realized measures. A quasi-likelihood is built, employing the asymmetric Laplace distribution that is directly linked to a joint loss function, which enables Bayesian inference for the proposed model. An adaptive Markov Chain Monte Carlo method is used for the model estimation. The empirical section evaluates the performance of the proposed framework with six stock markets from January 2000 to June 2022, covering the period of COVID-19. Three realized measures, including 5- minute realized variance, bi-power variation, and realized kernel, are incorporated and evaluated in the proposed framework. One-step-ahead VaR and ES forecasting results of the proposed model are compared to a range of parametric and semi-parametric models, lending support to the effectiveness of the proposed framework.
FCOC: A Fractal-Chaotic Co-driven Framework for Financial Volatility Forecasting
Faculty of Science and Technology, Beijing Normal-Hong Kong Baptist University（北京-香港浸会大学联合国际学院）；Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, Beijing Normal-Hong Kong Baptist University（数据科学交叉研究与应用广东省重点实验室（北京-香港浸会大学联合国际学院））；The Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen（深圳大数据研究院（香港中文大学（深圳）））
On options-driven realized volatility forecasting: Information gains via rough volatility model
Hong Kong University of Science and Technology（香港科技大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Green Pine Innorise Fund（绿松创新基金）；Beijing Normal - Hong Kong Baptist University（北京师范大学-香港浸会大学联合国际学院）
