Abstract
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.
Funding Statement
Wu’s research was supported by National Natural Science Foundation of China (NSFC, 12201628) and the MOE Project of Key Research Institute of Humanities and Social Sciences (22JJD110001). Zhang’s research was supported by the National natural Science Foundation of China(72271232) and the MOE Project of Key Research Institute of Humanities and Social Sciences (22JJD110001). This research was supported by Public Computing Cloud, Renmin University of China.
Acknowledgments
The authors would like to thank the anonymous referees, an Associate Editor and the Editor for their constructive comments that improved the quality of this paper.
Citation
Weiqing Gao. Ben Wu. Bo Zhang. "High-frequency volatility estimation and forecasting with a novel Bayesian LGI model." Electron. J. Statist. 18 (2) 3497 - 3534, 2024. https://doi.org/10.1214/24-EJS2280
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