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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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 |
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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 |
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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. |
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BOLLERSLEV, Tim LI, Jia CHAVES, Leonardo Salim Saker |
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BOLLERSLEV, Tim LI, Jia CHAVES, Leonardo Salim Saker |
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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 |
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Generalized jump regressions for local moments |
title_sort |
generalized jump regressions for local moments |
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Institutional Knowledge at Singapore Management University |
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2021 |
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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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