TY - JOUR

T1 - Volatility Estimation and Forecasts Based on Price Durations

AU - Hong, Seok Young

AU - Nolte, Ingmar

AU - Taylor, Stephen

AU - Zhao, Vera

N1 - This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Journal of Financial Econometrics following peer review. The definitive publisher-authenticated version Seok Young Hong, Ingmar Nolte, Stephen J Taylor, Xiaolu Zhao, Volatility Estimation and Forecasts Based on Price Durations, Journal of Financial Econometrics, Volume 21, Issue 1, Winter 2023, Pages 106–144, https://doi.org/10.1093/jjfinec/nbab006 is available online at: https://academic.oup.com/jfec/article-abstract/21/1/106/6155899

PY - 2023/1/19

Y1 - 2023/1/19

N2 - 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.

AB - 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.

U2 - 10.1093/jjfinec/nbab006

DO - 10.1093/jjfinec/nbab006

M3 - Journal article

VL - 21

SP - 106

EP - 144

JO - Journal of Financial Econometrics

JF - Journal of Financial Econometrics

SN - 1479-8409

IS - 1

ER -