More Accurate Volatility Estimation and Forecasts Using Price Durations
61 Pages Posted: 11 Jan 2016 Last revised: 14 Nov 2018
Date Written: November 14, 2018
We investigate price duration variance estimators that have long been ignored in the literature. We show 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 a) important market microstructure noise effects such as the bid/ask spread, irregularly spaced observations in discrete time and discrete price levels, as well as b) price jumps. We develop i) simple-to-construct non-parametric estimators and ii) parametric price duration estimators using autoregressive conditional duration specifications. We provide guidance how these estimators can best be implemented in practice by optimally selecting a threshold parameter that defines a price duration event, or by averaging over a range of non-parametric duration estimators. We 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: Volatility estimation; Stochastic sampling; Price durations; High-frequency prices; Market microstructure noise; Forecasting; DJIA stocks.
JEL Classification: C22, C41, C51, C52, C53, C14, C13
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