COVID-19 Şoklarının BİST Teknoloji Şirketlerinin Getiri ve Volatilite Yapılarına Etkisi: TVP-VAR Modeliyle Bir İnceleme

Öz

Bu çalışma, COVID-19 pandemisinin Borsa İstanbul Teknoloji Endeksi (XUTEK) kapsamındaki şirketlerin getiri ve volatilite dinamikleri üzerindeki etkilerini incelemeyi amaçlamaktadır. Ocak 2015-Mayıs 2025 dönemini kapsayan on firmanın (ASELS, KAREL, LINK, KRONT, NETAS, ALCTL, ARENA, DESPC, PKART, INDES) haftalık hisse senedi verileri kullanılarak Zamanla Değişen Parametreli Vektör Otoregresyon (TVP-VAR) modeli uygulanmıştır. TVP-VAR metodolojisi, finansal piyasalarda sıkça görülen yapısal kırılmalar, ani şoklar ve rejim değişimlerine duyarlılığı nedeniyle tercih edilmiştir. Bulgular, NETAS, INDES ve ALCTL şirketlerinin yüksek pozitif net değerleriyle sistemde “şok yayıcı” konumda olduğunu; LINK, PKART ve KRONT firmalarının ise negatif net değerleriyle “şok alıcı” niteliği taşıdığını göstermektedir. Ayrıca ASELS ve KAREL firmalarının kendi oynaklıklarını büyük ölçüde kontrol ederek krizlere karşı daha dirençli bir yapı sergilediği belirlenmiştir. COVID-19 öncesi ve sonrası dönemlerin karşılaştırmalı analizi, genel olarak tüm firmalarda getirilerin düştüğünü, özellikle LINK ve KRONT’un en yüksek kayıpları yaşarken NETAS’ın en az etkilenen firma olduğunu ortaya koymaktadır. Çalışma hem yatırımcılar hem de politika yapıcılar için önemli çıkarımlar sunmaktadır. Yatırımcılar açısından portföy çeşitlendirmesinin ve güçlü kurumsal yapıya sahip firmalara yönelmenin kriz dönemlerinde riskleri azaltabileceği vurgulanırken; politika yapıcılar için ise finansal istikrarı korumak, bilgi şeffaflığını artırmak ve piyasa güvenini destekleyecek önlemler almak kritik bir gereklilik olarak öne çıkmaktadır.

Anahtar Kelimeler

• Yönetim Bilişim Sistemleri
• BİST Teknoloji
• TVP-VAR

Etik Beyan

Bu araştırma, Giresun Üniversitesi Sosyal Bilimler, Eğitim, Fen ve Mühendislik Bilimleri Araştırmaları Etik Kurulu’nun 02.07.2025 tarih ve 07/385 sayılı kararı ile etik açıdan uygun bulunmuştur.

The Effect of COVID-19 Shocks on the Return and Volatility Structures of BIST Technology Companies: An Examination with the TVP-VAR Model

Öz

This study examines the effects of the COVID-19 pandemic on the return and volatility dynamics of companies included in the Borsa Istanbul Technology Index (XUTEK). Using weekly stock data for ten firms (ASELS, KAREL, LINK, KRONT, NETAS, ALCTL, ARENA, DESPC, PKART, INDES) over the period January 2015–May 2025, we implement a Time-Varying Parameter Vector Autoregression (TVP-VAR) model. The TVP-VAR methodology is preferred due to its sensitivity to structural breaks, sudden shocks, and regime shifts that are frequently observed in financial markets. The findings indicate that NETAS, INDES, and ALCTL act as “shock transmitters” in the system, with high positive net spillover values, whereas LINK, PKART, and KRONT display “shock receiver” characteristics, with negative net spillover values. In addition, ASELS and KAREL appear to exert substantial control over their own volatilities, exhibiting greater resilience to crises. A comparative analysis of the pre- and post-COVID-19 periods reveals a general decline in returns across all firms; notably, LINK and KRONT experience the largest losses, whereas NETAS emerges as the least affected firm. The study offers important implications for both investors and policymakers. For investors, it underscores that portfolio diversification and allocating capital to firms with strong corporate structures can mitigate risks during crises; for policymakers, it highlights the critical need to safeguard financial stability, enhance information transparency, and adopt measures that bolster market confidence.

Anahtar Kelimeler

• Management Information Systems
• BIST Technology
• TVP-VAR

Etik Beyan

The research was found ethically appropriate by the Giresun University Social Sciences, Education, Science and Engineering Research Ethics Committee, with the ethics committee decision dated July 2, 2025, and numbered 07/385.

Kaynakça

1. Adekoya, O. B., & Oliyide, J. A. (2021). How COVID-19 drives connectedness among commodity and financial markets: Evidence from TVP-VAR and causality-in-quantiles techniques. Resources Policy, 70, 101898. https://doi.org/10.1016/j.resourpol.2020.101898
2. Akan, Y., & Ustalar, S. A. (2021). The Impact of the COVID-19 Outbreak on the Volatility of Stock Markets as an Information Channel. Maliye Dergisi, 180, 326-344.
3. Aksoy, M., & Ulusoy, V. (2015). Analysis of Relative Return Behaviour of Borsa Istanbul Reit and Borsa Istanbul 100 Index. Romanian Journal of Economic Forecasting, 18(1), 107-128.
4. Aliyev, F. (2019). Testing Market Efficiency with Nonlinear Methods: Evidence from Borsa Istanbul. International Journal of Financial Studies, 7(2), 27. https://doi.org/10.3390/ijfs7020027
5. Antonakakis, N., & Gabauer, D. (2017). Refined measures of dynamic connectedness based on TVP-VAR. MPRA Paper No. 78282. University Library of Munich, Germany. https://mpra.ub.uni-muenchen.de/78282
6. Antonakakis, N., Chatziantoniou, I., & Gabauer, D. (2019). Refined Measures of Dynamic Connectedness based on TVP-VAR. Energy Economics, 74, 881–895.
7. Antonakakis, N., Cunado J., Filis G., Gabauer, D., & De Gracia F. P. (2019). Oil and asset classes implied volatilities: dynamic connectedness and investment strategies. Energy Economics Forthcoming. http://dx.doi.org/10.2139/ssrn.3399996
8. Arioğlu, E., & Arioğlu Kaya, P. (2015). Busyness and advising at Borsa Istanbul firms. Borsa Istanbul Review, 15(2), 126-136. https://doi.org/10.1016/j.bir.2015.01.001

9. Armagan, I. U. (2023). Price prediction of the Borsa Istanbul banks index with traditional methods and artificial neural networks. Borsa Istanbul Review, 23(1), S30-S39. https://doi.org/10.1016/j.bir.2023.10.005
10. Arsoy, M. F. (2017). The Effects of Share Repurchase Programs' Announcements on Stock Market Values: Evidence from Borsa Istanbul. Eskişehir Osmangazi University Journal of Economics and Administrative Sciences, 12(2), 1-22. https://doi.org/10.17153/oguiibf.318636
11. Balcı, M. A., Batrancea, L. M., Akgüller, Ö., Gaban, L., Rus, M.-I., & Tulai, H. (2022). Fractality of Borsa Istanbul during the COVID-19 Pandemic. Mathematics, 10(14), 2503. https://doi.org/10.3390/math10142503
12. Bash, A., & Al-Awadhi, A. M. (2023). Central Bank Independence and stock market outcomes: An event study on Borsa Istanbul. Cogent Economics & Finance, 11(1). https://doi.org/10.1080/23322039.2023.2186032
13. Belaid, F., Ben Amar, A., Goutte, S., & Guesmi, K. (2023). Emerging and advanced economies markets behaviour during the COVID-19 crisis era. International Journal of Finance & Economics, 28(2), 1563-1581. https://doi.org/10.1002/ijfe.2494
14. Borsa Istanbul. (2025). https://www.borsaistanbul.com/en/index-detail/306/bist-technology (Access Date: 03.04.2025).
15. Caliskan, E. N., & Icke, B. T. (2015). Corporate Governance in Turkey: The Case of Borsa Istanbul 50 Companies. In Corporate Governance and Corporate Social Responsibility: Emerging Markets Focus (pp. 107-131).
16. Can Ergün, Z., Cagli, E. C., & Durukan Salı, M. B. (2023). The interconnectedness across risk appetite of distinct investor types in Borsa Istanbul. Studies in Economics and Finance, 40(3), 425-444. https://doi.org/10.1108/SEF-09-2022-0460
17. Caporale, G. M., Çatik, A. N., Helmi, M. H., Ali, M. M., & Yilmaz, F. (2021). Oil Price Uncertainty and Sectoral Stock Returns in Turkey: A Time-Varying Parameter VAR Approach. Empirical Economics, 61(1), 1-25.
18. Çimen, A. (2019). The Impact of Sustainability Index on Firm Performance: An Event Study. International Journal of Contemporary Economics and Administrative Sciences, 9(1), 170-183. https://doi.org/10.5281/zenodo.3262277
19. Diebold, F. X., & Yılmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. The Economic Journal, 119(534), 158-171. https://doi.org/10.1111/j.1468-0297.2008.02208.x
20. Diebold, F. X., & Yılmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57–66. https://doi.org/10.1016/j.ijforecast.2011.02.006
21. Diebold, F. X., & Yılmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1), 119–134. https://doi.org/10.1016/j.jeconom.2014.04.012
22. Erdoğan, B. (2023). The Volatility Relationship Among Financial Assets: TVP-VAR Model. International Journal of Business and Economic Studies, 5(4), 225-237. https://doi.org/10.54821/uiecd.1392184 Gormsen, N. J., & Koijen, R. S. J. (2020). Coronavirus: Impact on Stock Prices and Growth Expectations. The Review of Asset Pricing Studies, 10(4), 574-597. https://doi.org/10.1093/rapstu/raaa013
23. Gülay, G., & Aydoğmuş, M. (2023). Guest Editors’ introduction: Borsa Istanbul history (1836–2023) and an overview of selected papers on the 150th anniversary of the founding of Borsa Istanbul. Borsa Istanbul Review, 23(2), S1-S5. https://doi.org/10.1016/j.bir.2024.01.007
24. Ha, L. T., & Nham, N. T. H. (2022). An application of a TVP-VAR extended joint connected approach to explore connectedness between WTI crude oil, gold, stock and cryptocurrencies during the COVID-19 health crisis. Technological Forecasting and Social Change, 18, 121909. https://doi.org/10.1016/j.techfore.2022.121909
25. Huseynli, N. (2022). The relationship between Consumer Confidence Index and BIST 50 Index. Journal of Eastern European and Central Asian Research, 9(6), 1107-1116. https://doi.org/10.15549/jeecar.v9i6.1222
26. Inci, A. C., & Ozenbas, D. (2017). Intraday volatility and the implementation of a closing call auction at Borsa Istanbul. Emerging Markets Review, 33, 79-89. https://doi.org/10.1016/j.ememar.2017.09.002
27. Kadıoğlu, E., Küçükkocaoğlu, G., & Kılıç, S. (2015). Closing price manipulation in Borsa Istanbul and the impact of call auction sessions. Borsa Istanbul Review, 15(3), 213-221. https://doi.org/10.1016/j.bir.2015.04.002
28. Karaömer, Y., & Kakilli Acaravcı, S. (2022). The impact of COVID-19 outbreak on Borsa Istanbul: an event study method. Journal of Economic and Administrative Sciences, 38(4), 652-666. https://doi.org/10.1108/JEAS-06-2020-0111
29. Kışla, G. H., Türkcan, B., & Yenilmez, M. I. (2022). Sustainable Covid-19 Recovery and Circular Economy. Sustainability and Climate Change, 15(4), 289-295. https://doi.org/10.1089/scc.2021.0042
30. Kiliç, Y., & Çütcü, I. (2018). The Cointegration and Causality Relationship between Bitcoin Prices and Borsa Istanbul Index. Eskişehir Osmangazi University Journal of Economics and Administrative Sciences, 13(3), 235-250. https://doi.org/10.17153/oguiibf.455083
31. Koop, G., & Korobilis, D. (2014). A new index of financial conditions. European Economic Review, 71, 101-116. https://doi.org/10.1016/j.euroecorev.2014.07.002
32. Koop, G., Leon-Gonzalez, R., & Strachan, R. W. (2009a). Efficient Posterior Simulation for Time-Varying Parameter VARs. Econometric Reviews, 28(3), 276–298.
33. Koop, G., Leon-Gonzalez, R., & Strachan, R. W. (2009b). On the evolution of the monetary policy transmission mechanism. Journal of Economic Dynamics and Control, 33(4), 997-1017. https://doi.org/10.1016/j.jedc.2008.11.003
34. Le, H. B., & Lam, T. H. (2021). Vietnam Economy under the Impact of COVID-19. The Russian Journal of Vietnamese Studies, 5(4), 45-70. https://doi.org/10.54631/VS.2021.54-45-70
35. Mishra, A. K., Arunachalam, V., Olson, D., & Patnaik, D. (2023). Dynamic connectedness in commodity futures markets during Covid-19 in India: New evidence from a TVP-VAR extended joint connectedness approach. Resources Policy, 82, 103490.
36. Nakajima, J. (2011). Time-varying parameter VAR model with stochastic volatility: an overview of methodology and empirical applications. IMES Discussion Paper Series 11-E-09, Institute for Monetary and Economic Studies, 29, 107-142.
37. Nakajima, J. (2011). Time-Varying Parameter VAR Model with Stochastic Volatility: An Overview of Methodology and Empirical Applications. Monetary and Economic Studies, 29, 107–142.
38. Özkan, N. (2019). q-Faktör Modelinin Borsa İstanbul’da Geçerliliğinin Test Edilmesi. Eskişehir Osmangazi University Journal of Economics and Administrative Sciences, 14(2), 441-456. https://doi.org/10.17153/oguiibf.489738
39. Primiceri, G. E. (2005). Time varying structural vector autoregressions and monetary policy. The Review of Economic Studies, 72(3), 821-852. https://doi.org/10.1111/j.1467-937X.2005.00353.x
40. Sagim, K., & Reis, S. G. (2020). The Effects of Independent Audit Opinion on Stock Returns: Case of Borsa Istanbul. Eskişehir Osmangazi University Journal of Economics and Administrative Sciences, 15(2), 649-662. https://doi.org/10.17153/oguiibf.514686
41. Sanchez-Duque, J. A., Orozco-Hernandez, J. P., Marin-Medina, D. S., Arteaga-Livias, K., Pecho-Silva, S., Rodriguez-Morales, A. J., & Dhama, K. (2020). Economy or Health, Constant Dilemma in Times of Pandemic: The Case of Coronavirus Disease 2019 (COVID-19). Journal of Pure and Applied Microbiology, 14(1), 717-720. https://doi.org/10.22207/JPAM.14.SPL1.07
42. Tan, Ö. F. (2021). The Impact of News about Pandemic on Borsa Istanbul during the COVID-19 Financial Turmoil. Turkish Review of Communication Studies, 37, 109-124. https://doi.org/10.17829/turcom.859299
43. Tan, X., Ma, S., Wang, X., Zhao, Y., Wang, Z., & Xiang, L. (2022). The dynamic impact of COVID-19 pandemic on stock returns: A TVP-VAR-SV estimation for G7 countries. Frontiers in Public Health, 10, 859647. https://doi.org/10.3389/fpubh.2022.859647
44. Tao, C., Diao, G., & Cheng, B. (2021). The Dynamic Impacts of the COVID-19 Pandemic on Log Prices in China: An Analysis Based on the TVP-VAR Model. Forests, 12(4), 449. https://doi.org/10.3390/f12040449
45. Temel, F., & Eryiğit, M. (2021). Testing The Relationships Between Energy Prices and The Borsa Istanbul Indices. Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty, 8(1), 370-398. https://doi.org/10.30798/makuiibf.821611
46. Thorbecke, W. (2020). The impact of the COVID-19 pandemic on the U.S. economy: Evidence from the stock market. Journal of Risk and Financial Management, 13(10), 1-32. https://doi.org/10.3390/jrfm13100233
47. Xu, A., Qian, F., Pai, C-H., Yu, N., & Zhou, P. (2022). The Impact of COVID-19 Epidemic on the Development of the Digital Economy of China—Based on the Data of 31 Provinces in China. Frontiers in Public Health, 9. https://doi.org/10.3389/fpubh.2021.778671
48. Yildiz, M. E., & Erzurumlu, Y. O. (2018). Testing postmodern portfolio theory based on global and local single factor market model: Borsa Istanbul case. Borsa Istanbul Review, 18(4), 259-268. https://doi.org/10.1016/j.bir.2018.03.001