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Improved Dividend Estimation from Intraday Quotes

Liquid financial markets, such as the options market of the S&P 500 index, create vast amounts of data every day, i.e., so-called intraday data. However, this highly granular data is often reduced to single-time when used to estimate financial quantities. This under-utilization of the data may r...

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Autores principales: Söderbäck, Pontus, Blomvall, Jörgen, Singull, Martin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8774800/
https://www.ncbi.nlm.nih.gov/pubmed/35052121
http://dx.doi.org/10.3390/e24010095
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author Söderbäck, Pontus
Blomvall, Jörgen
Singull, Martin
author_facet Söderbäck, Pontus
Blomvall, Jörgen
Singull, Martin
author_sort Söderbäck, Pontus
collection PubMed
description Liquid financial markets, such as the options market of the S&P 500 index, create vast amounts of data every day, i.e., so-called intraday data. However, this highly granular data is often reduced to single-time when used to estimate financial quantities. This under-utilization of the data may reduce the quality of the estimates. In this paper, we study the impacts on estimation quality when using intraday data to estimate dividends. The methodology is based on earlier linear regression (ordinary least squares) estimates, which have been adapted to intraday data. Further, the method is also generalized in two aspects. First, the dividends are expressed as present values of future dividends rather than dividend yields. Second, to account for heteroscedasticity, the estimation methodology was formulated as a weighted least squares, where the weights are determined from the market data. This method is compared with a traditional method on out-of-sample S&P 500 European options market data. The results show that estimations based on intraday data have, with statistical significance, a higher quality than the corresponding single-times estimates. Additionally, the two generalizations of the methodology are shown to improve the estimation quality further.
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spelling pubmed-87748002022-01-21 Improved Dividend Estimation from Intraday Quotes Söderbäck, Pontus Blomvall, Jörgen Singull, Martin Entropy (Basel) Article Liquid financial markets, such as the options market of the S&P 500 index, create vast amounts of data every day, i.e., so-called intraday data. However, this highly granular data is often reduced to single-time when used to estimate financial quantities. This under-utilization of the data may reduce the quality of the estimates. In this paper, we study the impacts on estimation quality when using intraday data to estimate dividends. The methodology is based on earlier linear regression (ordinary least squares) estimates, which have been adapted to intraday data. Further, the method is also generalized in two aspects. First, the dividends are expressed as present values of future dividends rather than dividend yields. Second, to account for heteroscedasticity, the estimation methodology was formulated as a weighted least squares, where the weights are determined from the market data. This method is compared with a traditional method on out-of-sample S&P 500 European options market data. The results show that estimations based on intraday data have, with statistical significance, a higher quality than the corresponding single-times estimates. Additionally, the two generalizations of the methodology are shown to improve the estimation quality further. MDPI 2022-01-07 /pmc/articles/PMC8774800/ /pubmed/35052121 http://dx.doi.org/10.3390/e24010095 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Söderbäck, Pontus
Blomvall, Jörgen
Singull, Martin
Improved Dividend Estimation from Intraday Quotes
title Improved Dividend Estimation from Intraday Quotes
title_full Improved Dividend Estimation from Intraday Quotes
title_fullStr Improved Dividend Estimation from Intraday Quotes
title_full_unstemmed Improved Dividend Estimation from Intraday Quotes
title_short Improved Dividend Estimation from Intraday Quotes
title_sort improved dividend estimation from intraday quotes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8774800/
https://www.ncbi.nlm.nih.gov/pubmed/35052121
http://dx.doi.org/10.3390/e24010095
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