TY - JOUR
T1 - Volatility measurement with pockets of extreme return persistence
AU - Andersen, Torben G.
AU - Li, Yingying
AU - Todorov, Viktor
AU - Zhou, Bo
N1 - Funding Information:
The research of Andersen and Todorov is partially supported by National Science Foundation grant SES-1530748. Li acknowledges support from the Research Grants Council, Hong Kong SAR GRF16503419 and T31-604/18-N, as well as the National Natural Science Foundation of China grant NSFC19BM03. We thank Serena Ng (the Editor), the Guest CoEditor, anonymous referees as well as participants at the CFE-CMStatistics Conference at the University of London, December 2019 and the 2020 Econometric Society World Congress, for helpful comments and suggestions.
Funding Information:
The research of Andersen and Todorov is partially supported by National Science Foundation grant SES-1530748 . Li acknowledges support from the Research Grants Council, Hong Kong SAR GRF16503419 and T31-604/18-N , as well as the National Natural Science Foundation of China grant NSFC19BM03 . We thank Serena Ng (the Editor), the Guest CoEditor, anonymous referees as well as participants at the CFE-CMStatistics Conference at the University of London, December 2019 and the 2020 Econometric Society World Congress, for helpful comments and suggestions.
Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2021
Y1 - 2021
N2 - Increasing evidence points towards the episodic emergence of pockets with extreme return persistence. This notion refers to intraday periods of non-trivial duration, for which stock returns are highly positively autocorrelated. Such episodes include, but are not limited to, gradual jumps and prolonged bursts in the drift component. In this paper, we develop a family of integrated volatility estimators, labeled differenced-return volatility (DV) estimators, which provide robustness to these types of Itô semimartingale violations. Specifically, we show that, by using differences in consecutive high-frequency returns, our DV estimators can reduce the non-trivial bias that all commonly-used estimators exhibit during such periods of apparent short-term intraday return predictability. A Monte Carlo study demonstrates the reliability of the newly developed volatility estimators in finite samples. In our empirical volatility forecasting application to S&P 500 index futures and individual equities, our DV-based Heterogeneous Autoregressive (HAR) model performs well relative to existing procedures according to standard out-of-sample MSE and QLIKE criteria.
AB - Increasing evidence points towards the episodic emergence of pockets with extreme return persistence. This notion refers to intraday periods of non-trivial duration, for which stock returns are highly positively autocorrelated. Such episodes include, but are not limited to, gradual jumps and prolonged bursts in the drift component. In this paper, we develop a family of integrated volatility estimators, labeled differenced-return volatility (DV) estimators, which provide robustness to these types of Itô semimartingale violations. Specifically, we show that, by using differences in consecutive high-frequency returns, our DV estimators can reduce the non-trivial bias that all commonly-used estimators exhibit during such periods of apparent short-term intraday return predictability. A Monte Carlo study demonstrates the reliability of the newly developed volatility estimators in finite samples. In our empirical volatility forecasting application to S&P 500 index futures and individual equities, our DV-based Heterogeneous Autoregressive (HAR) model performs well relative to existing procedures according to standard out-of-sample MSE and QLIKE criteria.
KW - Extreme return persistence
KW - High-frequency data
KW - Integrated volatility estimation
KW - Market microstructure noise
KW - Volatility forecasting
UR - http://www.scopus.com/inward/record.url?scp=85100565471&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85100565471&partnerID=8YFLogxK
U2 - 10.1016/j.jeconom.2020.11.005
DO - 10.1016/j.jeconom.2020.11.005
M3 - Article
AN - SCOPUS:85100565471
JO - Journal of Econometrics
JF - Journal of Econometrics
SN - 0304-4076
ER -