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    Rights statement: 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 Volatility estimation and forecasts based on price durations, Journal of Financial Econometrics 2021 is available online at: https://academic.oup.com/jfec/advance-article/doi/10.1093/jjfinec/nbab006/6155899

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    Embargo ends: 5/02/23

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

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Volatility estimation and forecasts based on price durations

Research output: Contribution to journalJournal articlepeer-review

E-pub ahead of print
<mark>Journal publication date</mark>1/03/2021
<mark>Journal</mark>Journal of Financial Econometrics
Publication StatusE-pub ahead of print
Early online date1/03/21
<mark>Original language</mark>English

Abstract

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.

Bibliographic note

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 Volatility estimation and forecasts based on price durations, Journal of Financial Econometrics 2021 is available online at: https://academic.oup.com/jfec/advance-article/doi/10.1093/jjfinec/nbab006/6155899