Using Covariates to Improve the Efficacy of Univariate Bubble Detection Methods

Journal article


Astill, S., Taylor, A. M. R., Kellard, N. and Korkos, I. Using Covariates to Improve the Efficacy of Univariate Bubble Detection Methods. Journal of Empirical Finance. https://doi.org/10.1016/j.jempfin.2022.12.008
AuthorsAstill, S., Taylor, A. M. R., Kellard, N. and Korkos, I.
Abstract

We explore how information additional to a specific price series can be used to improve the power of popular univariate autoregressive-based methods for detecting and dating speculative price bubble episodes. Following Phillips, Wu and Yu (2011) and Phillips, Shi and Yu (2015) we base our approach on sequences of sub-sample regression-based augmented Dickey-Fuller [ADF] statistics. Our point of departure from these extant procedures is to allow for additional information in the testing and dating procedures. To do so we follow the approach of Hansen (1995) and augment the sub-sample ADF regressions with covariate regressors. The limiting null distributions of the resulting statistics depend on the long-run squared correlation between the covariates and the regression error. We show that this dependence can be accounted for by using a residual bootstrap re-sampling method. Simulation evidence shows that including relevant covariates can significantly improve the efficacy of both the resulting bubble detection tests and the associated date-stamping procedure, relative to using standard sub-sample ADF statistics. An empirical application of the proposed methodology to monthly S&P 500 data is considered, using a variety of candidate covariates. Using these covariates, the onset of the dotcom bubble and the bubble associated with Black Monday are both identified significantly earlier than when using standard methods.

KeywordsRational bubbles, explosive behaviour, covariates, sub-sample unit root statistics, i.i.d. residual bootstrap
JournalJournal of Empirical Finance
PublisherElsevier
ISSN1879-1727
Digital Object Identifier (DOI)https://doi.org/10.1016/j.jempfin.2022.12.008
Web address (URL)https://www.sciencedirect.com/science/article/pii/S0927539822001141
Publication dates
Online23 Dec 2022
Publication process dates
Accepted15 Dec 2022
Deposited04 Jan 2023
Publisher's version
License
File Access Level
Open
Accepted author manuscript
License
File Access Level
Open
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https://openresearch.lsbu.ac.uk/item/92xv3

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Publisher's version
1-s2.0-S0927539822001141-main.pdf
License: CC BY 4.0
File access level: Open


Accepted author manuscript
Covariates Final.pdf
License: CC BY-NC-ND 4.0
File access level: Open

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