A0300
Title: Enhancing financial volatility models with intraday range and overnight information
Authors: Edward Meng-Hua Lin - Tunghai University (Taiwan) [presenting]
Abstract: Accurately capturing financial market volatility is crucial for risk management, portfolio optimization, and policymaking. Traditional return-based volatility models, such as GARCH and stochastic volatility models, often struggle to fully incorporate the intricate features of financial time series, including volatility clustering, leverage effects, and asymmetric market responses. Recent advancements suggest that incorporating intraday range data and overnight information can provide a more comprehensive understanding of volatility dynamics. An innovative range-based volatility model is proposed that integrates overnight information as a threshold variable to capture the asymmetric effects. The model leverages a Bayesian framework to address the complexities of nonlinear relationships and asymmetric volatility. The model's forecasting performance and ability to capture stylized facts are enhanced by overnight information and intraday range data, as demonstrated by combining simulations and empirical analyses.
