<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The development of technology and the globalization of financial markets have increased the volatility in financial markets and caused the emergence of risks and uncertainties that have not been previously encountered. Since traditional econometric models cannot fully explain this volatility, nonlinear conditional variance models such as ARCH, GARCH, EGARCH and TARCH are used today. From this point of view, this study aims to determine the most explanatory model that fund managers who are considering investing in the Borsa Istanbul 100 (BIST 100) Index, and academicians doing research on this subject, can use in estimating the BIST 100 Index return volatility. For this purpose, ARCH and GARCH models, as symmetric models, and EGARCH and TARCH models, as asymmetric nonlinear conditional models, are included in the econometric analysis by using the end-of-day values of 2657 observations belonging to the 04.01.2010-28.07.2020 period. According to the empirical results of the study, the TARCH model, which has the highest level of explanatory power, gives the most successful results among related models in revealing BIST 100 Index return volatility.</p>
</abstract>

The development of technology and the globalization of financial markets have increased the volatility in financial markets and caused the emergence of risks and uncertainties that have not been previously encountered. Since traditional econometric models cannot fully explain this volatility, nonlinear conditional variance models such as ARCH, GARCH, EGARCH and TARCH are used today. From this point of view, this study aims to determine the most explanatory model that fund managers who are considering investing in the Borsa Istanbul 100 (BIST 100) Index, and academicians doing research on this subject, can use in estimating the BIST 100 Index return volatility. For this purpose, ARCH and GARCH models, as symmetric models, and EGARCH and TARCH models, as asymmetric nonlinear conditional models, are included in the econometric analysis by using the end-of-day values of 2657 observations belonging to the 04.01.2010-28.07.2020 period. According to the empirical results of the study, the TARCH model, which has the highest level of explanatory power, gives the most successful results among related models in revealing BIST 100 Index return volatility.

Symmetric and asymmetric volatility: Forecasting the Borsa Istanbul 100 index return volatility

Istanbul University-Cerrahpaşa, Vocational School of Social Sciences

Financial Internet Quarterly

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The development of technology and the globalization of financial markets have increased the volatility in financial markets and caused the emergence of risks...

Symmetric and asymmetric volatility: Forecasting the Borsa Istanbul 100 index return volatility

Publié en ligne: 29 mars 2023

Pages: 48 - 56

Reçu: 30 nov. 2022

Accepté: 12 févr. 2023

DOI: https://doi.org/10.2478/fiqf-2023-0005

Mots clés
ARCH models, symmetric volatility, asymmetric volatility

© 2023 Selma Öner et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Symmetric and asymmetric volatility: Forecasting the Borsa Istanbul 100 index return volatility

Publié en ligne: 29 mars 2023

Pages: 48 - 56

Reçu: 30 nov. 2022

Accepté: 12 févr. 2023

DOI: https://doi.org/10.2478/fiqf-2023-0005

Mots clésARCH models, symmetric volatility, asymmetric volatility

© 2023 Selma Öner et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
ARCH models, symmetric volatility, asymmetric volatility