SavedAdd your journal

Journals / Doğuş Üniversitesi Dergisi / 2022 / Cilt: 23 - Sayı: COVID-19 Özel Sayısı

AN APPLICATION ON BIST 100, FTSE 100, NIKKEI 225 AND S&P 500 INDICES DURING THE COVID-19 PANDEMIC

COVID-19 PANDEMİSİ SIRASINDA BIST 100, FTSE 100, NIKKEI 225 VE S&P 500 ENDEKSLERİ ÜZERİNE BİR UYGULAMA

Keziban YILMAZ
Ayça Hatice ATLI
Pages
35–53
Publication Date
2022-03-28
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In addition, optimum model parameters for each index were used to enable out-of-sample performance evaluation.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The COVID-19 pandemic is one of the most significant crises of the 21st century.
📊
Key Findings & Data:This study aimed to examine the behavior of the BIST 100, FTSE 100, NIKKEI 225 and S&P 500 stock indices during the pandemic period between March 11, 2020, and December 31, 2020 and investigate the reaction of indices to the pandemic.
💡
Conclusion & Impact:In addition, optimum model parameters for each index were used to enable out-of-sample performance evaluation.
🔬Design:Modeling & Simulation
📍Scope:2020 Verileri
⚙️Variables:Değişen Varyans · Getiri Serisi · Zaman Serileri Analizi

Abstract

The COVID-19 pandemic is one of the most significant crises of the 21st century. This study aimed to examine the behavior of the BIST 100, FTSE 100, NIKKEI 225 and S&P 500 stock indices during the pandemic period between March 11, 2020, and December 31, 2020 and investigate the reaction of indices to the pandemic. In this context, Box-Jenkins models and five models from the ARCH/GARCH family were utilized for the stock indices return. According to performance evaluations, the most appropriate model for BIST 100 and NIKKEI 225 indices was ARCH and the one for FTSE 100 and S&P 500 indices was EGARCH. In addition, optimum model parameters for each index were used to enable out-of-sample performance evaluation.

Özet

COVID-19 salgını, 21. yüzyılın en önemli krizlerinden biridir. Bu çalışma, 11 Mart 2020 ile 31 Aralık 2020 arasındaki salgın döneminde BIST 100, FTSE 100, NIKKEI 225 ve S&P 500 borsa endekslerinin davranışlarını incelemeyi, endekslerin salgına nasıl tepki verdiğini araştırmayı amaçlamıştır. Bu bağlamda, borsa endeksleri getirileri için Box-Jenkins modelleri ile ARCH/GARCH ailesinden beş model kullanılmıştır. Performans değerlendirmelerine göre, BIST 100 ve NIKKEI 225 endeksleri için ARCH; FTSE 100 ve S&P 500 endeksleri için EGARCH modeli en uygun model olarak belirlenmiştir. Ayrıca, her endekse ilişkin optimum model parametreleri kullanılarak örneklem dışı performans değerlendirmesi de sağlanmıştır.

🔖

Cite This Article

YILMAZ, K., ATLI, A. H. (2022). AN APPLICATION ON BIST 100, FTSE 100, NIKKEI 225 AND S&P 500 INDICES DURING THE COVID-19 PANDEMIC. Doğuş Üniversitesi Dergisi, Cilt: 23 - Sayı: COVID-19 Özel Sayısı, 35–53. https://ajindex.com/articles/658407

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (15)

  1. Akhtar, S. ve Khan, N. U. (2016). Modeling volatility on the Karachi Stock Exchange, Pakistan. Journal of Asia Business Studies, 10(3), 253-275.
  2. Aliyu, S. U. (2011). Reactions of stock market to monetary policy shocks during the global financial crisis: The Nigerian case. CBN Journal of Applied Statistics, 3(1), 17-41.
  3. Andersen, T. G. ve Bollerslev, T. (1998). Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review, 39(4), 885-905.
  4. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
  5. Bollerslev, T. (2008). Glossary to ARCH (GARCH). CREATES Research Paper, 49, 1-46.
  6. Brooks, C. (1996). Testing for non-linearity in daily sterling exchange rates. Applied Financial Economics, 6(4), 307-317.
  7. Chen, H., Zhang, J., Tao, Y. ve Tan, F. (2019). Asymmetric GARCH type models for asymmetric volatility characteristics analysis and wind power forecasting. Protection and Control of Modern Power Systems, 4(1), 1-11.
  8. Cox, D. R. ve Stuart, A. (1955). Some quick sign tests for trend in location and dispersion. Biometrika, 42(1/2), 80-95.
  9. Cryer, J. D. ve Chan, K. (2008). Time series analysis with applications in R. USA: Springer Science & Business Media. Değirmenci, N. ve Abdioğlu, Z. (2017). Finansal piyasalar arasındaki oynaklık yayılımı. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, 54, 104-125.
  10. Değirmenci, N. ve Akay, A. (2017). Finansal verilerin ARIMA ve ARCH modelleriyle öngörüsü: Türkiye örneği. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi, 12(3), 15-36.
  11. Ding, Z., Granger, C. W. ve Engle, R. F. (1993). A long memory property of stock market returns and a new model. Journal of Empirical Finance, 1(1), 83-106.
  12. Enders, W. (1995). Applied econometric time series. John Wiley & Sons. Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007.
  13. Eryılmaz, F. (2015). Modelling stock market volatility: The case of BIST-100. Annals of The Constantin Brancusi University of Targu Jiu, Economy Series, 5, 37-47.
  14. Friedman, M. (1937). The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association, 32(200), 675-701.
  15. Ghalanos, A. (2020). Introduction to the Rugarch package. (Version 1.4-3). Erişim adresi https://cran.r-project.org/web/packages/rugarch/vignettes/Int