THE NUMBER OF CASES, EXCHANGE RATE AND THE EFFECT OF VIX INDEX ON EMERGİNG MARKETS DURİNG THE COVID-19 PANDEMİC PERİOD: AN ANALYSİS ON BIST 100 INDEX

Yıl 2022, Cilt: 23 Sayı: COVID-19 ÖZEL SAYISI, 221 - 242, 28.03.2022

Özgür Ömer Ersin , Tuğçe Acar , Özgür Kıyak

https://doi.org/10.31671/doujournal.1016083

Cited By: 3

Öz

The study aims at the investigation of the pandemic and its effects on the way the exchange rates and global risk influences an emerging stock market. For this purpose, the effects of the active cases and the new cases are utilized in addition to the exchange rates and the VIX index on the BIST100 stock index in Turkey are investigated. By using a sample that covers daily series to starting from 11.3.2020, the day of declaration of the pandemic in Turkey, and that ends at 11.5.2021, the empirical findings obtained from GARCH, GJR, TGARCH, and nonlinear GARCH models suggest significant. According to the empirical findings, the negative and positive news shocks have important effects on the stock market in Turkey. The empirical findinds reveal that in addition to the negative impacts of the Covid-19 cases on the BIST100 daily returns in Turkey, the nominal Dolar/TL exchange rate increases have a strong negative effect on the stock market. Further, empirical findings also point at the negative effects of the inclines in the VIX index, considered as a proxy representing the international financial risk.

Anahtar Kelimeler

COVID-19, International Financial Markets, Time Series Analysis

COVID-19 PANDEMİ DÖNEMİNDE VAKA SAYILARI, DÖVİZ KURU VE VIX ENDEKSİNİN GELİŞMEKTE OLAN PİYASALAR ÜZERİNDEKİ ETKİSİ: BİST 100 ENDEKSİ ÜZERİNE BİR ANALİZ

Öz

Çalışmada, pandemi sürecinin ve özellikle vaka sayılarındaki değişimlerin döviz kurları ve küresel riskin yerel borsa getirilerine etki ediş sürecine yansımalarının incelenmesi amaçlanmıştır. Bu amaçla döviz kuru ve VIX endeksine ek olarak aktif vakalar ve yeni vakaların Türkiye'deki BİST100 hisse senedi endeksi üzerindeki etkileri araştırılmıştır. GARCH, GJR, TGARCH ve doğrusal olmayan GARCH modellerinden elde edilen ampirik bulgular, Türkiye'de pandeminin ilan edildiği 11.3.2020'den başlayarak ve 11.5.2021'de sona eren günlük serileri kapsayan bir örneklemin kullanılmasıyla anlamlı sonuçlar ortaya koymaktadır. Elde edilen ampirik bulgular doğrultusunda, negatif ve pozitif haber şoklarının Türkiye’de borsada ciddi etkilere sahip olduğuna işaret etmektedir. Elde edilen bulgular, BIST 100 getirileri üzerinde Covid-19 vaka sayılarındaki artışların negatif etkilerine ek olarak, özellikle nominal Dolar/TL artışlarının önemli negatif etkileri olduğunu ortaya koymakta, uluslararası finansal riskin bir göstergesi olarak alınan VIX’teki artışların da Türkiye’deki finansal getiriler üzerindeki negatif etkilerine işaret etmektedir.

Anahtar Kelimeler

COVID-19, Uluslararası Finansal Piyasalar, Zaman Serileri Analizi

Kaynakça

• Abuzayed, B., Bouri, E., Al-Fayoumi, N., Jalkh, N. (2021). Systemic risk spillover across global and country stock markets during the COVID-19 pandemic. Economic Analysis and Policy, 71, 180-197.
• Akhtaruzzaman, M., Boubaker, S., Sensoy, A. (2021). Financial contagion during COVID–19 crisis. Finance Research Letters, 38, 101604.
• AlAli, M. S. (2020). Risk velocity and financial markets performance: Measuring the early effect of COVID-19 pandemic on major stock markets performance. International Journal of Economic and Financial Research. 6(4), 76-81.
• Albulescu, C. T. (2021). COVID-19 and the United States financial markets volatility. Finance Research Letters, 38, 101699.
• Ali, M., Alam, N., Rizvi, S. A. R. (2020). Coronavirus (COVID-19)—An epidemic or pandemic for financial markets. Journal of Behavioral and Experimental Finance, 27, 100341.
• Alzyadat, J. A., Asfoura, E. (2021). The effect of COVID-19 pandemic on stock market: An empirical study in Saudi Arabia. The Journal of Asian Finance, Economics and Business, 8(5), 913-921.
• Al-Awadhi, A. M., Alsaifi, K., Al-Awadhi, A., Alhammadi, S. (2020). Death and contagious infectious diseases: Impact of the COVID-19 virus on stock market returns. Journal of Behavioral and Experimental Finance, 27, 100326.
• Bildirici, M., Ersin, Ö.Ö. (2009). Improving forecasts of GARCH family models with the artificial neural networks: An application to the daily returns in Istanbul stock exchange. Expert Systems with Applications, 36(4), 7355-7362.
• Bildirici M. E., Ersin Ö.Ö. (2014). Modeling markov switching ARMA-GARCH neural networks models and an application to forecasting stock returns. The Scientific World Journal, 1-21.
• Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31, 307-327.
• Engle, R.F., Ng., V.K. (1993). Measuring and testing the impact of news on volatility. Journal of Finance, 48, 1749-1778
• Engle, R. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007.
• Fakhfekh, M., Jeribi, A., Ben Salem, M. (2021). Volatility dynamics of the Tunisian stock market before and during the COVID‐19 outbreak: Evidence from the GARCH family models. International Journal of Finance & Economics, 1-14. https://doi.org/10.1002/ijfe.2499
• Geweke, J. (1986). Modelling the persistence of conditional variances: A comment. Econometric Reviews, 5, 57-61.
• Gherghina, Ș. C., Armeanu, D. Ș., Joldeș, C. C. (2020). Stock market reactions to Covid-19 pandemic outbreak: Quantitative evidence from ARDL bounds tests and granger causality analysis. International Journal of Environmental Research and Public Health, 17(18), 6729.
• Glosten, L. R., Jagannathan, R., Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48, 1779-1801
• Gülhan, Ü. (2020). Covid-19 pandemisine BIST 100 reaksiyonu: Ekonometrik bir analiz. Electronic Turkish Studies, 15(4), 497-509.
• Higgins, M. L., Bera, A. K. (1992). A class of nonlinear ARCH models. International Economic Review, 33, 137–158
• Kartal, M. T., Depren, Ö., ve Depren, S. K. (2020). The determinants of main stock exchange index changes in emerging countries: Evidence from Turkey in COVID-19 pandemic age. Quantitative Finance and Economics, 4(4), 526-541.
• Kayral, İ. E., Tandoğan, N. Ş. (2020). BİST100, döviz kurları ve altının getiri ve volatilitesinde COVID-19 etkisi. Gaziantep University Journal of Social Sciences, 19, 687-701. Klaassen F. (2002). Improving GARCH volatility forecasts with regime-switching GARCH. Empirical Economics, 27, 363-394.
• Kramer, W. (2006). Long memory with Markov-switching GARCH. Working papers 6, Business and Social Statistics Department, University Dortmund.
• Nelson, D. (1992). Filtering and forecasting with misspecified ARCH models I: Getting the right variance with the wrong model. Journal of Econometrics, 52, 61-90.
• Nelson, D. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347-370
• Onali, E. (2020). Covid-19 and stock market volatility. SSRN Electron Journal, 37, 1-10.
• Rabemananjara, R., Zakoian, J. (1993). Threshold Arch models and asymmetries in volatility. Journal of Applied Econometrics, 8(1), 31-49.
• Ruiz, E., Mario, A., Koutronas, E. ve Lee, M. (2020). Stagpression: The economic and financial impact of COVID-19 pandemic. Contemporary Economics 15(1), 19-33.
• Sharif, A., Aloui, C., Yarovaya, L. (2020). COVID-19 Pandemic, oil prices, stock market, geopolitical risk and policy uncertainty nexus in the US economy: Fresh evidence from the wavelet-based approach. International Review of Financial Analysis, 70, 1-9.
• Singh, B., Dhall, R., Narang, S., Rawat, S. (2020). The outbreak of COVID-19 and stock market responses: An event study and panel data analysis for G-20 countries. Global Business Review, 1-26.
• Telçeken, N., Topçu, M., Kadıoğlu, E. (2019). Volatilite endeksleri: Gelişimi, türleri, uygulamalari ve trvix önerisi. Ekonomi, Politika & Finans Araştırmaları Dergisi, 4(2), 204-228.
• Topcu, M., Gulal, O. S. (2020). The impact of COVID-19 on emerging stock markets. Finance Research Letters, 36, 101691.
• Zakoian, J. M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931–955.
• Zhang, D., Hu, M., Ji, Q. (2020). Financial markets under the global pandemic of COVID-19. Financial Research Letters, 30, 1-6.
• Zhang, W., Hamori, S. (2021). Crude oil market and stock markets during the COVID-19 pandemic: Evidence from the US, Japan, and Germany, International Review of Financial Analysis, 74, 1-13.
• World Health Organization. (2021). COVID-2019 situation reports. Erişim adresi https://www.who.int/publications/m/item/weekly-epidemiological-update-on-covid-19--18-may-2021.