Heteroscedasticity and Precise Estimation Model Approach for Complex Financial Time-Series Data: An Example of Taiwan Stock Index Futures before and during COVID-19

In this paper, we provide a mathematical and statistical methodology using heteroscedastic estimation to achieve the aim of building a more precise mathematical model for complex financial data. Considering a general regression model with explanatory variables (the expected value model form) and the...

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Autores principales:	Chih-Wen Hsiao, Ya-Chuan Chan, Mei-Yu Lee, Hsi-Peng Lu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	heteroscedasticity model form selection complex financial data Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/da0304ba18b64892b0ac29d7e4bc92f8
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spelling	oai:doaj.org-article:da0304ba18b64892b0ac29d7e4bc92f82021-11-11T18:16:27ZHeteroscedasticity and Precise Estimation Model Approach for Complex Financial Time-Series Data: An Example of Taiwan Stock Index Futures before and during COVID-1910.3390/math92127192227-7390https://doaj.org/article/da0304ba18b64892b0ac29d7e4bc92f82021-10-01T00:00:00Zhttps://www.mdpi.com/2227-7390/9/21/2719https://doaj.org/toc/2227-7390In this paper, we provide a mathematical and statistical methodology using heteroscedastic estimation to achieve the aim of building a more precise mathematical model for complex financial data. Considering a general regression model with explanatory variables (the expected value model form) and the error term (including heteroscedasticity), the optimal expected value and heteroscedastic model forms are investigated by linear, nonlinear, curvilinear, and composition function forms, using the minimum mean-squared error criterion to show the precision of the methodology. After combining the two optimal models, the fitted values of the financial data are more precise than the linear regression model in the literature and also show the fitted model forms in the example of Taiwan stock price index futures that has three cases: (1) before COVID-19, (2) during COVID-19, and (3) the entire observation time period. The fitted mathematical models can apparently show how COVID-19 affects the return rates of Taiwan stock price index futures. Furthermore, the fitted heteroscedastic models also show how COVID-19 influences the fluctuations of the return rates of Taiwan stock price index futures. This methodology will contribute to the probability of building algorithms for computing and predicting financial data based on mathematical model form outcomes and assist model comparisons after adding new data to a database.Chih-Wen HsiaoYa-Chuan ChanMei-Yu LeeHsi-Peng LuMDPI AGarticleheteroscedasticitymodel form selectioncomplex financial dataMathematicsQA1-939ENMathematics, Vol 9, Iss 2719, p 2719 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	heteroscedasticity model form selection complex financial data Mathematics QA1-939
spellingShingle	heteroscedasticity model form selection complex financial data Mathematics QA1-939 Chih-Wen Hsiao Ya-Chuan Chan Mei-Yu Lee Hsi-Peng Lu Heteroscedasticity and Precise Estimation Model Approach for Complex Financial Time-Series Data: An Example of Taiwan Stock Index Futures before and during COVID-19
description	In this paper, we provide a mathematical and statistical methodology using heteroscedastic estimation to achieve the aim of building a more precise mathematical model for complex financial data. Considering a general regression model with explanatory variables (the expected value model form) and the error term (including heteroscedasticity), the optimal expected value and heteroscedastic model forms are investigated by linear, nonlinear, curvilinear, and composition function forms, using the minimum mean-squared error criterion to show the precision of the methodology. After combining the two optimal models, the fitted values of the financial data are more precise than the linear regression model in the literature and also show the fitted model forms in the example of Taiwan stock price index futures that has three cases: (1) before COVID-19, (2) during COVID-19, and (3) the entire observation time period. The fitted mathematical models can apparently show how COVID-19 affects the return rates of Taiwan stock price index futures. Furthermore, the fitted heteroscedastic models also show how COVID-19 influences the fluctuations of the return rates of Taiwan stock price index futures. This methodology will contribute to the probability of building algorithms for computing and predicting financial data based on mathematical model form outcomes and assist model comparisons after adding new data to a database.
format	article
author	Chih-Wen Hsiao Ya-Chuan Chan Mei-Yu Lee Hsi-Peng Lu
author_facet	Chih-Wen Hsiao Ya-Chuan Chan Mei-Yu Lee Hsi-Peng Lu
author_sort	Chih-Wen Hsiao
title	Heteroscedasticity and Precise Estimation Model Approach for Complex Financial Time-Series Data: An Example of Taiwan Stock Index Futures before and during COVID-19
title_short	Heteroscedasticity and Precise Estimation Model Approach for Complex Financial Time-Series Data: An Example of Taiwan Stock Index Futures before and during COVID-19
title_full	Heteroscedasticity and Precise Estimation Model Approach for Complex Financial Time-Series Data: An Example of Taiwan Stock Index Futures before and during COVID-19
title_fullStr	Heteroscedasticity and Precise Estimation Model Approach for Complex Financial Time-Series Data: An Example of Taiwan Stock Index Futures before and during COVID-19
title_full_unstemmed	Heteroscedasticity and Precise Estimation Model Approach for Complex Financial Time-Series Data: An Example of Taiwan Stock Index Futures before and during COVID-19
title_sort	heteroscedasticity and precise estimation model approach for complex financial time-series data: an example of taiwan stock index futures before and during covid-19
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/da0304ba18b64892b0ac29d7e4bc92f8
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Heteroscedasticity and Precise Estimation Model Approach for Complex Financial Time-Series Data: An Example of Taiwan Stock Index Futures before and during COVID-19

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