A0615
Title: Loss-based Bayesian sequential prediction of value-at-risk with a long-memory and non-linear realized volatility model
Authors: Richard Gerlach - University of Sydney (Australia) [presenting]
Chao Wang - The University of Sydney (Australia)
Minh-Ngoc Tran - University of Sydney (Australia)
Rangika Peiris - University of Sydney Business School (Australia)
Abstract: A long-memory and non-linear realized volatility model class is proposed for direct value-at-risk (VaR) forecasting. This model, referred to as RNN-HAR, extends the heterogeneous autoregressive (HAR) model, a framework known for efficiently capturing long memory in realized measures, by integrating a recurrent neural network (RNN) to handle the non-linear dynamics. Quantile loss-based generalized Bayesian method with sequential Monte Carlo is employed for model estimation and sequential prediction in RNN-HAR. The empirical analysis is conducted using daily closing prices and realized measures with around 12 years of data till 2022, covering 31 market indices. The proposed model's one-step-ahead VaR forecasting performance is compared against a basic HAR model and its extensions. The results demonstrate that the proposed RNN-HAR model consistently outperforms all other models considered in the study.
