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Realising the future: forecasting with high‐frequency‐based volatility (HEAVY) models

Journal of Applied Econometrics · 2010 · 444 citations · 47 references

Open access

TL;DR

The paper investigates high‑frequency‑based volatility (HEAVY) models, which directly model daily asset return volatility using realised measures from high‑frequency data. The study aims to estimate HEAVY models and evaluate their performance during the credit crunch relative to traditional GARCH models. The authors estimate the models, use a model‑based bootstrap to obtain full predictive return distributions, and analyze missing data effects, comparing fit to GARCH during the credit crunch. The analysis shows that HEAVY models exhibit momentum and mean‑reversion dynamics and quickly adapt to structural breaks in volatility. © 2010 John Wiley & Sons, Ltd.

Abstract

Abstract This paper studies in some detail a class of high‐frequency‐based volatility (HEAVY) models. These models are direct models of daily asset return volatility based on realised measures constructed from high‐frequency data. Our analysis identifies that the models have momentum and mean reversion effects, and that they adjust quickly to structural breaks in the level of the volatility process. We study how to estimate the models and how they perform through the credit crunch, comparing their fit to more traditional GARCH models. We analyse a model‐based bootstrap which allows us to estimate the entire predictive distribution of returns. We also provide an analysis of missing data in the context of these models. Copyright © 2010 John Wiley & Sons, Ltd.