A Nonlinear Autoregressive Distributed Lag (NARDL) Analysis of the FTSE and S&P500 Indexes

The paper features an examination of the link between the behaviour of the FTSE 100 and S&P500 Indexes in both an autoregressive distributed lag ARDL, plus a nonlinear autoregressive distributed lag NARDL framework. The attraction of NARDL is that it represents the simplest method available of m...

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Autores principales:	David E. Allen, Michael McAleer
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	NARDL bounds tests ARDL FTSE asymmetries multiplier effects Insurance HG8011-9999
Acceso en línea:	https://doaj.org/article/a2154d3fa1034cdc9ddaeee9f5e1d8ff
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spelling	oai:doaj.org-article:a2154d3fa1034cdc9ddaeee9f5e1d8ff2021-11-25T18:56:08ZA Nonlinear Autoregressive Distributed Lag (NARDL) Analysis of the FTSE and S&P500 Indexes10.3390/risks91101952227-9091https://doaj.org/article/a2154d3fa1034cdc9ddaeee9f5e1d8ff2021-11-01T00:00:00Zhttps://www.mdpi.com/2227-9091/9/11/195https://doaj.org/toc/2227-9091The paper features an examination of the link between the behaviour of the FTSE 100 and S&P500 Indexes in both an autoregressive distributed lag ARDL, plus a nonlinear autoregressive distributed lag NARDL framework. The attraction of NARDL is that it represents the simplest method available of modelling combined short- and long-run asymmetries. The bounds testing framework adopted means that it can be applied to stationary and non-stationary time series vectors, or combinations of both. The data comprise a daily FTSE adjusted price series, commencing in April 2009 and terminating in March 2021, and a corresponding daily S&P500 Index adjusted-price series obtained from Yahoo Finance. The data period includes all the gyrations caused by the Brexit vote in the UK, beginning with the vote to leave in 2016 and culminating in the actual agreement to withdraw in January 2020. It was then followed by the impact of the global spread of COVID-19 from the beginning of 2020. The results of the analysis suggest that movements in the contemporaneous levels of daily S&P500 Index levels have very significant effects on the behaviour of the levels of the daily FTSE 100 Index. They also suggest that negative movements have larger impacts than do positive movements in S&P500 levels, and that long-term multiplier impacts take about 10 days to take effect. These effects are supported by the results of quantile regression analysis. A key result is that weak form market efficiency does not apply in the second period.David E. AllenMichael McAleerMDPI AGarticleNARDLbounds testsARDLFTSEasymmetriesmultiplier effectsInsuranceHG8011-9999ENRisks, Vol 9, Iss 195, p 195 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	NARDL bounds tests ARDL FTSE asymmetries multiplier effects Insurance HG8011-9999
spellingShingle	NARDL bounds tests ARDL FTSE asymmetries multiplier effects Insurance HG8011-9999 David E. Allen Michael McAleer A Nonlinear Autoregressive Distributed Lag (NARDL) Analysis of the FTSE and S&P500 Indexes
description	The paper features an examination of the link between the behaviour of the FTSE 100 and S&P500 Indexes in both an autoregressive distributed lag ARDL, plus a nonlinear autoregressive distributed lag NARDL framework. The attraction of NARDL is that it represents the simplest method available of modelling combined short- and long-run asymmetries. The bounds testing framework adopted means that it can be applied to stationary and non-stationary time series vectors, or combinations of both. The data comprise a daily FTSE adjusted price series, commencing in April 2009 and terminating in March 2021, and a corresponding daily S&P500 Index adjusted-price series obtained from Yahoo Finance. The data period includes all the gyrations caused by the Brexit vote in the UK, beginning with the vote to leave in 2016 and culminating in the actual agreement to withdraw in January 2020. It was then followed by the impact of the global spread of COVID-19 from the beginning of 2020. The results of the analysis suggest that movements in the contemporaneous levels of daily S&P500 Index levels have very significant effects on the behaviour of the levels of the daily FTSE 100 Index. They also suggest that negative movements have larger impacts than do positive movements in S&P500 levels, and that long-term multiplier impacts take about 10 days to take effect. These effects are supported by the results of quantile regression analysis. A key result is that weak form market efficiency does not apply in the second period.
format	article
author	David E. Allen Michael McAleer
author_facet	David E. Allen Michael McAleer
author_sort	David E. Allen
title	A Nonlinear Autoregressive Distributed Lag (NARDL) Analysis of the FTSE and S&P500 Indexes
title_short	A Nonlinear Autoregressive Distributed Lag (NARDL) Analysis of the FTSE and S&P500 Indexes
title_full	A Nonlinear Autoregressive Distributed Lag (NARDL) Analysis of the FTSE and S&P500 Indexes
title_fullStr	A Nonlinear Autoregressive Distributed Lag (NARDL) Analysis of the FTSE and S&P500 Indexes
title_full_unstemmed	A Nonlinear Autoregressive Distributed Lag (NARDL) Analysis of the FTSE and S&P500 Indexes
title_sort	nonlinear autoregressive distributed lag (nardl) analysis of the ftse and s&p500 indexes
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a2154d3fa1034cdc9ddaeee9f5e1d8ff
work_keys_str_mv	AT davideallen anonlinearautoregressivedistributedlagnardlanalysisoftheftseandsp500indexes AT michaelmcaleer anonlinearautoregressivedistributedlagnardlanalysisoftheftseandsp500indexes AT davideallen nonlinearautoregressivedistributedlagnardlanalysisoftheftseandsp500indexes AT michaelmcaleer nonlinearautoregressivedistributedlagnardlanalysisoftheftseandsp500indexes
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A Nonlinear Autoregressive Distributed Lag (NARDL) Analysis of the FTSE and S&P500 Indexes

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