Volatility Estimation and Forecasts Based on Price Durations
74 Pages Posted: 11 Jan 2016 Last revised: 29 Jan 2021
Date Written: January 26, 2021
We investigate price duration variance estimators that have long been neglected in the literature. In particular, we consider simple-to-construct non-parametric duration estimators, and parametric price duration estimators using autoregressive conditional duration specifications. This paper shows i) how price duration estimators can be used for the estimation and forecasting of the integrated variance of an underlying semi-martingale price process and ii) how they are affected by discrete and irregular spacing of observations, market microstructure noise and finite price jumps. Specifically, we contribute to the literature by constructing the asymptotic theory for the non-parametric estimator with and without the presence of bid/ask spread and time discreteness. Further, we provide guidance about how our estimators can best be implemented in practice by appropriately selecting a threshold parameter that defines a price duration event, or by averaging over a range of non-parametric duration estimators. We also provide simulation and forecasting evidence that price duration estimators can extract relevant information from high-frequency data better and produce more accurate forecasts than competing realized volatility and option-implied variance estimators, when considered in isolation or as part of a forecasting combination setting.
Keywords: Price durations; Volatility estimation; High-frequency data; Market microstructure noise; Forecasting
JEL Classification: C22, C41, C51, C52, C53, C14, C13
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