Mixed-scale jump regressions with bootstrap inference

We develop an efficient mixed-scale estimator for jump regressions using high-frequency asset returns. A fine time scale is used to accurately identify the locations of large rare jumps in the explanatory variables such as the price of the market portfolio. A coarse scale is then used in the estimat...

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Main Authors: LI, Jia, TODOROV, Viktor, TAUCHEN, George
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Language:English
Published: Institutional Knowledge at Singapore Management University 2017
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Online Access:https://ink.library.smu.edu.sg/soe_research/2573
https://ink.library.smu.edu.sg/context/soe_research/article/3572/viewcontent/Mixed_scale.pdf
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spelling sg-smu-ink.soe_research-35722022-02-07T03:55:45Z Mixed-scale jump regressions with bootstrap inference LI, Jia TODOROV, Viktor TAUCHEN, George We develop an efficient mixed-scale estimator for jump regressions using high-frequency asset returns. A fine time scale is used to accurately identify the locations of large rare jumps in the explanatory variables such as the price of the market portfolio. A coarse scale is then used in the estimation in order to attenuate the effect of trading frictions in the dependent variable such as the prices of potentially less liquid assets. The proposed estimator has a non-standard asymptotic distribution that cannot be made asymptotically pivotal via studentization. We propose a novel bootstrap procedure for feasible inference and justify its asymptotic validity. We show that the bootstrap provides an automatic higher-order asymptotic approximation by accounting for the sampling variation in estimates of nuisance quantities that are used in efficient estimation. The Monte Carlo analysis indicates good finite-sample performance of the general specification test and confidence intervals based on the bootstrap. We apply the method to a high-frequency panel of Dow stock prices together with the market index defined by the S&P 500 index futures over the period 2007–2014. We document remarkable temporal stability in the way that stocks react to market jumps. However, this relationship for many of the stocks in the sample is significantly noisier and more unstable during sector-specific jump events. 2017-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/2573 info:doi/10.1016/j.jeconom.2017.08.017 https://ink.library.smu.edu.sg/context/soe_research/article/3572/viewcontent/Mixed_scale.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Bootstrap; High-frequency data; Jumps; Regression; Semimartingale; Specification test; Stochastic volatility Economic Theory
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Bootstrap; High-frequency data; Jumps; Regression; Semimartingale; Specification test; Stochastic volatility
Economic Theory
spellingShingle Bootstrap; High-frequency data; Jumps; Regression; Semimartingale; Specification test; Stochastic volatility
Economic Theory
LI, Jia
TODOROV, Viktor
TAUCHEN, George
Mixed-scale jump regressions with bootstrap inference
description We develop an efficient mixed-scale estimator for jump regressions using high-frequency asset returns. A fine time scale is used to accurately identify the locations of large rare jumps in the explanatory variables such as the price of the market portfolio. A coarse scale is then used in the estimation in order to attenuate the effect of trading frictions in the dependent variable such as the prices of potentially less liquid assets. The proposed estimator has a non-standard asymptotic distribution that cannot be made asymptotically pivotal via studentization. We propose a novel bootstrap procedure for feasible inference and justify its asymptotic validity. We show that the bootstrap provides an automatic higher-order asymptotic approximation by accounting for the sampling variation in estimates of nuisance quantities that are used in efficient estimation. The Monte Carlo analysis indicates good finite-sample performance of the general specification test and confidence intervals based on the bootstrap. We apply the method to a high-frequency panel of Dow stock prices together with the market index defined by the S&P 500 index futures over the period 2007–2014. We document remarkable temporal stability in the way that stocks react to market jumps. However, this relationship for many of the stocks in the sample is significantly noisier and more unstable during sector-specific jump events.
format text
author LI, Jia
TODOROV, Viktor
TAUCHEN, George
author_facet LI, Jia
TODOROV, Viktor
TAUCHEN, George
author_sort LI, Jia
title Mixed-scale jump regressions with bootstrap inference
title_short Mixed-scale jump regressions with bootstrap inference
title_full Mixed-scale jump regressions with bootstrap inference
title_fullStr Mixed-scale jump regressions with bootstrap inference
title_full_unstemmed Mixed-scale jump regressions with bootstrap inference
title_sort mixed-scale jump regressions with bootstrap inference
publisher Institutional Knowledge at Singapore Management University
publishDate 2017
url https://ink.library.smu.edu.sg/soe_research/2573
https://ink.library.smu.edu.sg/context/soe_research/article/3572/viewcontent/Mixed_scale.pdf
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