HYBRID-GARCH: A Generic Class of Models for Volatility Predictions Using High Frequency Data
Abstract
We propose a general GARCH framework that allows one to predict
volatility using returns sampled at a higher frequency than the prediction horizon.
We call the class of models High Frequency Data-Based Projection-Driven
GARCH, or HYBRID-GARCH models, as volatility dynamics are driven by what
we call HYBRID processes. The HYBRID processes can involve data sampled at
any frequency. We study the theoretical properties as well as statistical inference.
An application reports the superior out-of-sample forecasting performance of the
new class of models, including the time of the recent financial crisis.
Note: Research papers posted on ResearchGate, including any findings, may differ from the final version chosen for publication in academic journals.
- Keyword(s):
- data project 74
- finance 247
https://kenaninstitute.unc.edu/publication/hybrid-garch-a-generic-class-of-models-for-volatility-predictions-using-high-frequency-data/
