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Westerlund and Narayan predictability test: Step-by-step approach using COVID-19 and oil price data

In this note, we provide a step-by-step approach of Westerlund and Narayan (WN, 2012, 2015) predictability test using COVID-19 and oil price data. This is an important exercise because the WN model addresses three salient features of time series data, namely persistency, endogeneity and heteroskedas...

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Detalles Bibliográficos
Autor principal: Sharma, Susan Sunila
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8374153/
https://www.ncbi.nlm.nih.gov/pubmed/34434725
http://dx.doi.org/10.1016/j.mex.2020.101201
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author Sharma, Susan Sunila
author_facet Sharma, Susan Sunila
author_sort Sharma, Susan Sunila
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description In this note, we provide a step-by-step approach of Westerlund and Narayan (WN, 2012, 2015) predictability test using COVID-19 and oil price data. This is an important exercise because the WN model addresses three salient features of time series data, namely persistency, endogeneity and heteroskedasticity. We consider COVID-19 and oil price data as predictors of stock market returns for four Asian countries to demonstrate the applicability of the WN (2012, 2015) predictability approach. • This note demonstrates a step-by-step approach of the WN (2012, 2015) predictability test. • WN model accommodates three salient features of time-series data, namely persistency, endogeneity, and heteroskedasticity. • COVID-19 and oil price does not significantly predict stock returns of Japan, Russia, and Singapore (except in the case of South Korea).
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spelling pubmed-83741532021-08-24 Westerlund and Narayan predictability test: Step-by-step approach using COVID-19 and oil price data Sharma, Susan Sunila MethodsX Method Article In this note, we provide a step-by-step approach of Westerlund and Narayan (WN, 2012, 2015) predictability test using COVID-19 and oil price data. This is an important exercise because the WN model addresses three salient features of time series data, namely persistency, endogeneity and heteroskedasticity. We consider COVID-19 and oil price data as predictors of stock market returns for four Asian countries to demonstrate the applicability of the WN (2012, 2015) predictability approach. • This note demonstrates a step-by-step approach of the WN (2012, 2015) predictability test. • WN model accommodates three salient features of time-series data, namely persistency, endogeneity, and heteroskedasticity. • COVID-19 and oil price does not significantly predict stock returns of Japan, Russia, and Singapore (except in the case of South Korea). Elsevier 2020-12-25 /pmc/articles/PMC8374153/ /pubmed/34434725 http://dx.doi.org/10.1016/j.mex.2020.101201 Text en © 2020 The Author. Published by Elsevier B.V. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Method Article
Sharma, Susan Sunila
Westerlund and Narayan predictability test: Step-by-step approach using COVID-19 and oil price data
title Westerlund and Narayan predictability test: Step-by-step approach using COVID-19 and oil price data
title_full Westerlund and Narayan predictability test: Step-by-step approach using COVID-19 and oil price data
title_fullStr Westerlund and Narayan predictability test: Step-by-step approach using COVID-19 and oil price data
title_full_unstemmed Westerlund and Narayan predictability test: Step-by-step approach using COVID-19 and oil price data
title_short Westerlund and Narayan predictability test: Step-by-step approach using COVID-19 and oil price data
title_sort westerlund and narayan predictability test: step-by-step approach using covid-19 and oil price data
topic Method Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8374153/
https://www.ncbi.nlm.nih.gov/pubmed/34434725
http://dx.doi.org/10.1016/j.mex.2020.101201
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