ICEA ENDEKSİNDE RİSKİN KALICILIĞI: COVID-19 DÖNEMİNDE VOLATİLİTE VE YAPISAL DEĞİŞİM

Öz

COVID-19 pandemisi, küresel finansal sistemde daha önce benzeri görülmemiş düzeyde bir belirsizlik yaratmış ve risk modellemesine dair yaklaşımların yeniden değerlendirilmesini zorunlu kılmıştır. Bu bağlamda, bu çalışma Türkiye’nin finansal kırılganlığını temsil eden ICEA (Kripto Para Çevresel Dikkat Endeksi) Endeksi özelinde, pandeminin ve sonrasındaki sürecin yarattığı yapısal kırılmaları ve kalıcılık etkilerini incelemektedir. 2014–2024 dönemine ait haftalık verilere dayalı olarak yürütülen analizlerde, yapısal kırılmalar Zivot-Andrews testiyle, bu kırılmaların süregiden etkileri ise Bai–Perron yöntemiyle değerlendirilmiştir. Uzun dönemli bağımlılık ise hem ARFIMA modeli hem de Hurst katsayısı aracılığıyla ölçülmüştür. Bunun yanında, endeksin volatilite özelliklerini ortaya koymak amacıyla GARCH model ailesi uygulanmış, tail risk düzeyleri ise Value-at-Risk (VaR) yöntemiyle tahmin edilmiştir. Elde edilen bulgular, pandemi sürecinin hem volatilite düzeylerini hem de bellek yapısını anlamlı biçimde etkilediğini göstermektedir. Bu çalışma, yalnızca literatüre katkı sağlamayı değil, aynı zamanda finansal karar süreçlerinde kullanılabilecek erken uyarı mekanizmalarının geliştirilmesine de ışık tutmayı hedeflemektedir. Türkiye gibi yükselen piyasalarda COVID sonrası risk yönetimine ilişkin yaklaşımların yeniden şekillendirilmesi gerektiğini ortaya koymaktadır.

Anahtar Kelimeler

• ICEA Endeksi
• COVİD-19
• Uzun Bellek
• GARCH
• ARFIMA

PERSISTENT RISK and STRUCTURAL CHANGE in THE ICEA INDEX: VOLATILITY UNDER COVID-19

Öz

The COVID-19 pandemic has created an unprecedented level of uncertainty in the global financial system and necessitated a reassessment of risk modeling approaches.In this context, this study analyzes the structural breaks and long-term effects of the pandemic and its aftermath on the ICEA Index, which represents Turkey's financial fragility.Based on weekly data for the 2014-2024 period, structural breaks are analyzed using the Zivot-Andrews test and the persistent effects of these breaks are evaluated using the Bai-Perron method.Long-run dependence is measured by both the ARFIMA model and the Hurst coefficient.In addition, the GARCH model family is applied to reveal the volatility characteristics of the index, while tail risk levels are estimated using the Value-at-Risk (VaR) method.The findings show that the pandemic has a significant impact on both volatility levels and memory structure.This study aims not only to contribute to the literature but also to shed light on the development of early warning mechanisms that can be used in financial decision-making processes.It reveals that post-COVID-19 approaches to risk management in emerging markets such as Turkey need to be reshaped.

Anahtar Kelimeler

• ICEA Index
• COVID-19
• Long Memory
• ARFIMA
• GARCH

Kaynakça

1. Arif, M., Naeem, M. A. & Shahzad, S. J. H. (2021). COVID-19 and time–frequency connectedness between green and conventional financial markets. Finance Research Letters, 39, 101621. https://doi.org/10.1016/j.frl.2020.101621
2. Baker, S. R., Bloom, N., Davis, S. J. & Terry, S. J. (2020). COVID-induced economic uncertainty. NBER Working Paper, No. 26983. https://doi.org/10.3386/w26983
3. Bai, J. & Perron, P. (2003). Computation and analysis of multiple structural change models. Journal of Applied Econometrics, 18(1), 1–22. https://doi.org/10.1002/jae.659
4. Baillie, R. T. (1996). Long memory processes and fractional integration in econometrics. Journal of Econometrics, 73(1), 5–59. https://doi.org/10.1016/0304-4076(95)01732-1
5. Baillie, R. T., Bollerslev, T. & Mikkelsen, H. O. (1996). Fractionally integrated generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 74(1), 3–30. https://doi.org/10.1016/S0304-4076(95)01749-6
6. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1
7. Bouri, E., Jain, A. & Roubaud, D. (2021). Memory in financial time series under pandemic-induced stress: A FIGARCH approach. Finance Research Letters, 39, 101653. https://doi.org/10.1016/j.frl.2020.101653
8. Corbet, S., Larkin, C. & Lucey, B. (2020). The contagion effects of the COVID-19 pandemic: Evidence from gold and cryptocurrencies. Finance Research Letters, 35, 101554. https://doi.org/10.1016/j.frl.2020.101554

9. Christoffersen, P. F. (2003). Elements of Financial Risk Management. San Diego: Academic Press. https://doi.org/10.1016/B978-0-12-174232-4.X5000-4
10. Davidson, J. (2004). Moment and memory properties of linear conditional heteroscedasticity models, and a new model. Journal of Business & Economic Statistics, 22(1), 16–29. https://doi.org/10.1198/073500103288619359
11. Granger, C. W. J., & Joyeux, R. (1980). An introduction to long memory time series models and fractional differencing. Journal of Time Series Analysis, 1(1), 15–29. https://doi.org/10.1111/j.1467-9892.1980.tb00297.x
12. Hammoudeh, S., Omari, S. & Ajmi, A. N. (2022). Memory in volatility and return: Evidence from Islamic and conventional markets. International Review of Financial Analysis, 81, 102128. https://doi.org/10.1016/j.irfa.2022.102128
13. Hosking, J. R. M. (1981). Fractional differencing. Biometrika, 68(1), 165–176. https://doi.org/10.1093/biomet/68.1.165
14. ICEA. (2024). ICEA Endeksi veri açıklamaları. Erişim adresi: https://icea.org/data
15. Iqbal, Z. ve Mirakhor, A. (2022). Ethical dimensions of Islamic finance in post-pandemic recovery. Journal of Islamic Financial Studies, 9(1), 45–60. https://doi.org/10.1007/978-3-319-66390-6
16. Jorion, P. (2007). Value at Risk: The New Benchmark for Managing Financial Risk (3rd ed.). New York: McGraw-Hill. https://doi.org/10.1007/978-3-540-71050-9
17. Koutrouvelis, I. A., Magrini, L., & Caporin, M. (2023). Testing long memory in financial markets under crises. Journal of Empirical Finance, 70, 123–140. https://doi.org/10.1016/j.jempfin.2023.123140
18. Mandelbrot, B. B. & Wallis, J. R. (1969). Some long-run properties of geophysical records. Water Resources Research, 5(2), 321–340. https://doi.org/10.1029/WR005i002p00321
19. Mensi, W., Rehman, M. U. & Kang, S. H. (2022). Spillovers and long memory in ESG and conventional stock markets during COVID-19. Resources Policy, 76, 102553. https://doi.org/10.1016/j.resourpol.2022.102553
20. Nasseri, A. & Tasharofi, S. (2023). Long memory behavior in return dynamics of cryptocurrency and equity markets. The North American Journal of Economics and Finance, 66, 101746. https://doi.org/10.1016/j.najef.2023.101746
21. Nofsinger, J. & Varma, A. (2021). Socially responsible investing and market resilience during the pandemic. Journal of Behavioral Finance, 22(3), 289–301. https://doi.org/10.1080/15427560.2020.1865185
22. Ozturk, I. & Uluyol, O. (2023). Comparative resilience of Islamic and conventional indices during pandemic shocks: Evidence from Türkiye. Borsa Istanbul Review, 23(2), 130–142. https://doi.org/10.1016/j.bir.2023.03.001
23. Perron, P. (1989). The great crash, the oil price shock, and the unit root hypothesis. Econometrica, 57(6), 1361–1401. https://doi.org/10.2307/1913712
24. Pham, L. (2021). Long-memory volatility models in emerging stock markets: A case study from Southeast Asia. Economic Modelling, 95, 158–170. https://doi.org/10.1016/j.econmod.2020.12.003
25. Sowell, F. (1992). Modeling long-run behavior with the fractional ARIMA model. Journal of Monetary Economics, 29(2), 277–302. https://doi.org/10.1016/0304-3932(92)90029-Z
26. Szczygielski, J. J., Chipeta, C. & Charteris, A. (2022). Volatility and memory in emerging markets: A FIGARCH re-evaluation during COVID-19. Emerging Markets Review, 50, 100855. https://doi.org/10.1016/j.ememar.2022.100855
27. Tsay, R. S. (2010). Analysis of Financial Time Series (3rd ed.). Hoboken, NJ: Wiley.
28. Wang, Y., Lucey, B., Vigne, S.A. & Yarovaya, L. (2022), "An index of cryptocurrency environmental attention (ICEA)", China Finance Review International, Vol. 12 No. 3, pp. 378-414. https://doi.org/10.1108/CFRI-09-2021-0191
29. Zivot, E., & Andrews, D. W. K. (1992). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business & Economic Statistics, 10(3), 251–270. https://doi.org/10.1080/07350015.1992.10509901