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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

Author

Listed:
  • Chih-Wen Hsiao

    (Graduate School of Management, National Taiwan University of Science and Technology, Taipei 106335, Taiwan)

  • Ya-Chuan Chan

    (Department of Finance, Minghsin University of Science and Technology, Hsinchu 304, Taiwan)

  • Mei-Yu Lee

    (Department of Finance, Minghsin University of Science and Technology, Hsinchu 304, Taiwan)

  • Hsi-Peng Lu

    (Department of Information Management, National Taiwan University of Science and Technology, Taipei 106335, Taiwan)

Abstract

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.

Suggested Citation

  • Chih-Wen Hsiao & Ya-Chuan Chan & Mei-Yu Lee & Hsi-Peng Lu, 2021. "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, MDPI, vol. 9(21), pages 1-18, October.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:21:p:2719-:d:665309
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    References listed on IDEAS

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    1. Alena Vagaská & Miroslav Gombár & Antonín Korauš, 2022. "Mathematical Modeling and Nonlinear Optimization in Determining the Minimum Risk of Legalization of Income from Criminal Activities in the Context of EU Member Countries," Mathematics, MDPI, vol. 10(24), pages 1-25, December.
    2. Carlo Drago & Andrea Scozzari, 2023. "A Network-Based Analysis for Evaluating Conditional Covariance Estimates," Mathematics, MDPI, vol. 11(2), pages 1-19, January.

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