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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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/9/21/2719 |
| _version_ | 1797512110175944704 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:57:15Z |
| format | Article |
| id | doaj.art-da0304ba18b64892b0ac29d7e4bc92f8 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T05:57:15Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-da0304ba18b64892b0ac29d7e4bc92f82023-11-22T21:17:50ZengMDPI AGMathematics2227-73902021-10-01921271910.3390/math9212719Heteroscedasticity and Precise Estimation Model Approach for Complex Financial Time-Series Data: An Example of Taiwan Stock Index Futures before and during COVID-19Chih-Wen Hsiao0Ya-Chuan Chan1Mei-Yu Lee2Hsi-Peng Lu3Graduate School of Management, National Taiwan University of Science and Technology, Taipei 106335, TaiwanDepartment of Finance, Minghsin University of Science and Technology, Hsinchu 304, TaiwanDepartment of Finance, Minghsin University of Science and Technology, Hsinchu 304, TaiwanDepartment of Information Management, National Taiwan University of Science and Technology, Taipei 106335, TaiwanIn 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.https://www.mdpi.com/2227-7390/9/21/2719heteroscedasticitymodel form selectioncomplex financial data |
| spellingShingle | 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 Mathematics heteroscedasticity model form selection complex financial data |
| 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_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_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_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 |
| topic | heteroscedasticity model form selection complex financial data |
| url | https://www.mdpi.com/2227-7390/9/21/2719 |
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