IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i21p2719-d665309.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/21/2719/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/21/2719/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Richard M. Golden & Steven S. Henley & Halbert White & T. Michael Kashner, 2019. "Consequences of Model Misspecification for Maximum Likelihood Estimation with Missing Data," Econometrics, MDPI, vol. 7(3), pages 1-27, September.
    2. Bakas, Dimitrios & Triantafyllou, Athanasios, 2020. "Commodity price volatility and the economic uncertainty of pandemics," Economics Letters, Elsevier, vol. 193(C).
    3. Claudiu Albulescu, 2020. "Coronavirus and financial volatility: 40 days of fasting and fear," Papers 2003.04005, arXiv.org.
    4. Albulescu, Claudiu Tiberiu, 2021. "COVID-19 and the United States financial markets’ volatility," Finance Research Letters, Elsevier, vol. 38(C).
    5. Dyhrberg, Anne Haubo, 2016. "Bitcoin, gold and the dollar – A GARCH volatility analysis," Finance Research Letters, Elsevier, vol. 16(C), pages 85-92.
    6. Zaffaroni, Paolo, 2009. "Whittle estimation of EGARCH and other exponential volatility models," Journal of Econometrics, Elsevier, vol. 151(2), pages 190-200, August.
    7. Brandt, Michael W. & Jones, Christopher S., 2006. "Volatility Forecasting With Range-Based EGARCH Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 470-486, October.
    8. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    9. Claudio Nuber & Patrick Velte & Jacob Hörisch, 2020. "The curvilinear and time‐lagging impact of sustainability performance on financial performance: Evidence from Germany," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 27(1), pages 232-243, January.
    10. Breusch, T S & Pagan, A R, 1979. "A Simple Test for Heteroscedasticity and Random Coefficient Variation," Econometrica, Econometric Society, vol. 47(5), pages 1287-1294, September.
    11. Marcel P. Visser, 2011. "GARCH Parameter Estimation Using High-Frequency Data," Journal of Financial Econometrics, Oxford University Press, vol. 9(1), pages 162-197, Winter.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lucas Hafemann, 2021. "The Nexus between lockdown Shocks and Economic Uncertainty: Empirical Evidence from a VAR model," MAGKS Papers on Economics 202132, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    2. Jean-Paul Azam & Catherine Bonjean, 1995. "La formation du prix du riz : théorie et application au cas d'Antananarivo (Madagascar) ," Revue Économique, Programme National Persée, vol. 46(4), pages 1145-1166.
    3. Stern, David I. & Gerlagh, Reyer & Burke, Paul J., 2017. "Modeling the emissions–income relationship using long-run growth rates," Environment and Development Economics, Cambridge University Press, vol. 22(6), pages 699-724, December.
    4. Tsimpanos, Apostolos & Tsimbos, Cleon & Kalogirou, Stamatis, 2018. "Assessing spatial variation and heterogeneity of fertility in Greece at local authority level," MPRA Paper 100406, University Library of Munich, Germany.
    5. hafeez, neelam & naseem naik, sadia, 2023. "Economic Consequences of the COVID-19 Eruption: A Study of Selected South Asian Countries," MPRA Paper 117319, University Library of Munich, Germany.
    6. Caldara, Dario & Iacoviello, Matteo & Molligo, Patrick & Prestipino, Andrea & Raffo, Andrea, 2020. "The economic effects of trade policy uncertainty," Journal of Monetary Economics, Elsevier, vol. 109(C), pages 38-59.
    7. Marijke Verpoorten & Lode Berlage, 2004. "Genocide and land scarcity: Can Rwandan rural households manage?," CSAE Working Paper Series 2004-15, Centre for the Study of African Economies, University of Oxford.
    8. Machado, Jose A. F. & Silva, J. M. C. Santos, 2000. "Glejser's test revisited," Journal of Econometrics, Elsevier, vol. 97(1), pages 189-202, July.
    9. Katarzyna Jabłońska, 2018. "Dealing With Heteroskedasticity Within The Modeling Of The Quality Of Life Of Older People," Statistics in Transition New Series, Polish Statistical Association, vol. 19(3), pages 423-452, September.
    10. Michael O'Connor Keefe & David Gallagher, 2014. "Does the effect of revealed private information on initial public offering (IPO) first trading day return differ by IPO market heat?," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 54(3), pages 921-964, September.
    11. D’Augusta, Carlo & Grossetti, Francesco, 2023. "How did Covid-19 affect investors’ interpretation of earnings news? The role of accounting conservatism," Finance Research Letters, Elsevier, vol. 52(C).
    12. Guilhem Bascle, 2008. "Controlling for endogeneity with instrumental variables in strategic management research," Post-Print hal-00576795, HAL.
    13. Richard H. Spady & Sami Stouli, 2018. "Simultaneous Mean-Variance Regression," Bristol Economics Discussion Papers 18/697, School of Economics, University of Bristol, UK.
    14. Russell, Bill & Chowdhury, Rosen Azad, 2013. "Estimating United States Phillips curves with expectations consistent with the statistical process of inflation," Journal of Macroeconomics, Elsevier, vol. 35(C), pages 24-38.
    15. Olivier Damette & Philippe Delacote, 2009. "The environmental resource curse hypothesis : the forest case [L'hypothèse de malédiction environnemental des ressources : le cas des forêts]," Working Papers hal-01189378, HAL.
    16. Joachim Zietz, 2006. "Detecting neglected parameter heterogeneity with Chow tests," Applied Economics Letters, Taylor & Francis Journals, vol. 13(6), pages 369-374.
    17. Christopher F Baum & Mark E. Schaffer & Steven Stillman, 2003. "Instrumental variables and GMM: Estimation and testing," Stata Journal, StataCorp LP, vol. 3(1), pages 1-31, March.
    18. Pedro Delicado & Juan Romo, 1998. "Constant coefficient tests for random coefficient regression," Economics Working Papers 329, Department of Economics and Business, Universitat Pompeu Fabra.
    19. Russell Davidson & Victoria Zinde‐Walsh, 2017. "Advances in specification testing," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 50(5), pages 1595-1631, December.
    20. Dufour, Jean-Marie & Khalaf, Lynda & Bernard, Jean-Thomas & Genest, Ian, 2004. "Simulation-based finite-sample tests for heteroskedasticity and ARCH effects," Journal of Econometrics, Elsevier, vol. 122(2), pages 317-347, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:9:y:2021:i:21:p:2719-:d:665309. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.