A0499
Title: Semi parametric financial risk forecasting incorporating multiple realized measures
Authors: Chao Wang - The University of Sydney (Australia)
Richard Gerlach - University of Sydney (Australia)
Minh-Ngoc Tran - University of Sydney (Australia)
Rangika Peiris - University of Sydney Business School (Australia) [presenting]
Abstract: A semi-parametric joint value at risk (VaR) and expected shortfall (ES) forecasting framework employing multiple realized measures are developed. The proposed framework extends the realized exponential GARCH model to be semi-parametrically estimated via a joint loss function whilst extending quantile time series models to incorporate multiple realized measures. A quasi-likelihood is built, employing the asymmetric Laplace distribution directly linked to a joint loss function, enabling 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.
