Generalized jump regressions for local moments

We develop new high-frequency-based inference procedures for analyzing the relationship between jumps in instantaneous moments of stochastic processes. The estimation consists of two steps: the nonparametric determination of the jumps as differences in local averages, followed by a minimum-distance...

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Main Authors: BOLLERSLEV, Tim, LI, Jia, CHAVES, Leonardo Salim Saker
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Language:English
Published: Institutional Knowledge at Singapore Management University 2021
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Online Access:https://ink.library.smu.edu.sg/soe_research/2545
https://ink.library.smu.edu.sg/context/soe_research/article/3544/viewcontent/Generalized_Jump_Regressions_for_Local_Moments.pdf
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spelling sg-smu-ink.soe_research-35442022-02-07T04:55:56Z Generalized jump regressions for local moments BOLLERSLEV, Tim LI, Jia CHAVES, Leonardo Salim Saker We develop new high-frequency-based inference procedures for analyzing the relationship between jumps in instantaneous moments of stochastic processes. The estimation consists of two steps: the nonparametric determination of the jumps as differences in local averages, followed by a minimum-distance type estimation of the parameters of interest under general loss functions that include both least-square and more robust quantile regressions as special cases. The resulting asymptotic distribution of the estimator, derived under an infill asymptotic setting, is highly nonstandard and generally not mixed normal. In addition, we establish the validity of a novel bootstrap algorithm for making feasible inference including bias-correction. The new methods are applied in a study on the relationship between trading intensity and spot volatility in the U.S. equity market at the time of important macroeconomic news announcement. 2021-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/2545 info:doi/10.1080/07350015.2020.1753526 https://ink.library.smu.edu.sg/context/soe_research/article/3544/viewcontent/Generalized_Jump_Regressions_for_Local_Moments.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University high-frequency data jumps robust regression semimartingale news announcements news surprises investor disagreement volume volatility Economics
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic high-frequency data
jumps
robust regression
semimartingale
news announcements
news surprises
investor disagreement
volume
volatility
Economics
spellingShingle high-frequency data
jumps
robust regression
semimartingale
news announcements
news surprises
investor disagreement
volume
volatility
Economics
BOLLERSLEV, Tim
LI, Jia
CHAVES, Leonardo Salim Saker
Generalized jump regressions for local moments
description We develop new high-frequency-based inference procedures for analyzing the relationship between jumps in instantaneous moments of stochastic processes. The estimation consists of two steps: the nonparametric determination of the jumps as differences in local averages, followed by a minimum-distance type estimation of the parameters of interest under general loss functions that include both least-square and more robust quantile regressions as special cases. The resulting asymptotic distribution of the estimator, derived under an infill asymptotic setting, is highly nonstandard and generally not mixed normal. In addition, we establish the validity of a novel bootstrap algorithm for making feasible inference including bias-correction. The new methods are applied in a study on the relationship between trading intensity and spot volatility in the U.S. equity market at the time of important macroeconomic news announcement.
format text
author BOLLERSLEV, Tim
LI, Jia
CHAVES, Leonardo Salim Saker
author_facet BOLLERSLEV, Tim
LI, Jia
CHAVES, Leonardo Salim Saker
author_sort BOLLERSLEV, Tim
title Generalized jump regressions for local moments
title_short Generalized jump regressions for local moments
title_full Generalized jump regressions for local moments
title_fullStr Generalized jump regressions for local moments
title_full_unstemmed Generalized jump regressions for local moments
title_sort generalized jump regressions for local moments
publisher Institutional Knowledge at Singapore Management University
publishDate 2021
url https://ink.library.smu.edu.sg/soe_research/2545
https://ink.library.smu.edu.sg/context/soe_research/article/3544/viewcontent/Generalized_Jump_Regressions_for_Local_Moments.pdf
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