High-frequency volatility estimation and forecasting with a novel Bayesian LGI model

Volatility modeling is a challenging topic in high-frequency financial data analysis. In this paper, we propose a novel Bayesian framework for modeling and forecasting spot volatility by assuming a latent GARCH structure is embedded into the volatility process at a series of unobserved "anchor" time points, which can well describe the evolving volatility of financial assets in high frequency. We introduce an ideal approximation of latent anchors, which shares similar posterior distribution with true latent anchors. Furthermore, we develop an efficient two-stage inference framework with its corresponding two-stage MCMC sampling algorithm. The simulation study and real data analysis both show our method outperforms the existing alternatives in explanation of latent anchors and the estimation and forecasting of volatility.