Random coefficient continuous systems: Testing for extreme sample path behavior

This paper studies a continuous time dynamic system with a random persistence parameter. The exact discrete time representation is obtained and related to several discrete time random coefficient models currently in the literature. The model distinguishes various forms of unstable and explosive beha...

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Main Authors: TAO, Yubo, PHILLIPS, Peter C. B., Jun YU
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Language:English
Published: Institutional Knowledge at Singapore Management University 2019
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Online Access:https://ink.library.smu.edu.sg/soe_research/2310
https://ink.library.smu.edu.sg/context/soe_research/article/3309/viewcontent/Double_Asymptotics_for_OU_process_in_a_random_environment_av.pdf
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spelling sg-smu-ink.soe_research-33092020-04-01T08:34:43Z Random coefficient continuous systems: Testing for extreme sample path behavior TAO, Yubo PHILLIPS, Peter C. B. Jun YU, This paper studies a continuous time dynamic system with a random persistence parameter. The exact discrete time representation is obtained and related to several discrete time random coefficient models currently in the literature. The model distinguishes various forms of unstable and explosive behavior according to specific regions of the parameter space that open up the potential for testing these forms of extreme behavior. A two-stage approach that employs realized volatility is proposed for the continuous system estimation, asymptotic theory is developed, and test statistics to identify the different forms of extreme sample path behavior are proposed. Simulations show that the proposed estimators work well in empirically realistic settings and that the tests have good size and power properties in discriminating characteristics in the data that differ from typical unit root behavior. The theory is extended to cover models where the random persistence parameter is endogenously determined. An empirical application based on daily real S&P 500 index data over 1928–2018 reveals strong evidence against parameter constancy over the whole sample period leading to a long duration of what the model characterizes as extreme behavior in real stock prices. 2019-04-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/2310 info:doi/10.1016/j.jeconom.2019.01.002 https://ink.library.smu.edu.sg/context/soe_research/article/3309/viewcontent/Double_Asymptotics_for_OU_process_in_a_random_environment_av.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Bubble testing Explosive path Continuous time models Infill asymptotics Extreme behavior Random coefficient autoregression Econometrics
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Bubble testing
Explosive path
Continuous time models
Infill asymptotics
Extreme behavior
Random coefficient autoregression
Econometrics
spellingShingle Bubble testing
Explosive path
Continuous time models
Infill asymptotics
Extreme behavior
Random coefficient autoregression
Econometrics
TAO, Yubo
PHILLIPS, Peter C. B.
Jun YU,
Random coefficient continuous systems: Testing for extreme sample path behavior
description This paper studies a continuous time dynamic system with a random persistence parameter. The exact discrete time representation is obtained and related to several discrete time random coefficient models currently in the literature. The model distinguishes various forms of unstable and explosive behavior according to specific regions of the parameter space that open up the potential for testing these forms of extreme behavior. A two-stage approach that employs realized volatility is proposed for the continuous system estimation, asymptotic theory is developed, and test statistics to identify the different forms of extreme sample path behavior are proposed. Simulations show that the proposed estimators work well in empirically realistic settings and that the tests have good size and power properties in discriminating characteristics in the data that differ from typical unit root behavior. The theory is extended to cover models where the random persistence parameter is endogenously determined. An empirical application based on daily real S&P 500 index data over 1928–2018 reveals strong evidence against parameter constancy over the whole sample period leading to a long duration of what the model characterizes as extreme behavior in real stock prices.
format text
author TAO, Yubo
PHILLIPS, Peter C. B.
Jun YU,
author_facet TAO, Yubo
PHILLIPS, Peter C. B.
Jun YU,
author_sort TAO, Yubo
title Random coefficient continuous systems: Testing for extreme sample path behavior
title_short Random coefficient continuous systems: Testing for extreme sample path behavior
title_full Random coefficient continuous systems: Testing for extreme sample path behavior
title_fullStr Random coefficient continuous systems: Testing for extreme sample path behavior
title_full_unstemmed Random coefficient continuous systems: Testing for extreme sample path behavior
title_sort random coefficient continuous systems: testing for extreme sample path behavior
publisher Institutional Knowledge at Singapore Management University
publishDate 2019
url https://ink.library.smu.edu.sg/soe_research/2310
https://ink.library.smu.edu.sg/context/soe_research/article/3309/viewcontent/Double_Asymptotics_for_OU_process_in_a_random_environment_av.pdf
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