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

Yıl 2022, , 35 - 53, 28.03.2022

Keziban Yılmaz , Ayça Hatice Atlı

https://doi.org/10.31671/doujournal.937296

Cited By: 1

Öz

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.

Anahtar Kelimeler

Değişen Varyans, Getiri Serisi, Zaman Serileri Analizi

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

Öz

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.

Anahtar Kelimeler

Heteroscedasticity, Return Series, Time Series Analysis

Kaynakça

• Akhtar, S. ve Khan, N. U. (2016). Modeling volatility on the Karachi Stock Exchange, Pakistan. Journal of Asia Business Studies, 10(3), 253-275.
• 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.
• 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.
• Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
• Bollerslev, T. (2008). Glossary to ARCH (GARCH). CREATES Research Paper, 49, 1-46.
• Brooks, C. (1996). Testing for non-linearity in daily sterling exchange rates. Applied Financial Economics, 6(4), 307-317.
• 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.
• Cox, D. R. ve Stuart, A. (1955). Some quick sign tests for trend in location and dispersion. Biometrika, 42(1/2), 80-95.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• Ghalanos, A. (2020). Introduction to the Rugarch package. (Version 1.4-3). Erişim adresi https://cran.r-project.org/web/packages/rugarch/vignettes/Introduction_to_the_rugarch_package.pdf
• Gil-Alana, L. A. ve Tripathy, T. (2014). Modelling volatility persistence and asymmetry: A study on selected Indian non-ferrous metals markets. Resources Policy, 41, 31-39.
• Glosten, L. R., Jagannathan, R. ve Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The Journal of Finance, 48(5), 1779-1801.
• Gulay, E. ve Emec, H. (2018). Comparison of forecasting performances: Does normalization and variance stabilization method beat GARCH(1,1)‐type models? Empirical Evidence from the Stock Markets. Journal of Forecasting, 37(2), 133-150.
• Güçlü, F. (2020). İslami ve konvansiyonel hisse senedi endekslerinin oynaklıkları üzerine bir inceleme. MANAS Sosyal Araştırmalar Dergisi, 9(2), 1070-1088.
• Gümüş, U. T. ve Can Öziç, H. (2020). Investigation of the volatility structure of the BIST100 index before Covid 19 and the struggle process of Covid 19. Journal of Current Researches on Business and Economics, 10(1), 43-58.
• Hammoudeh, S. ve Yuan, Y. (2008). Metal volatility in presence of oil and interest rate shocks. Energy Economics, 30(2), 606-620.
• Hamner, B., Frasco, M. ve LeDell, E. (2018). Package ‘Metrics’. Erişim adresi https://cran.r-project.org/web/packages/Metrics/Metrics.pdf
• Hatipoğlu, M. (2015). Doğrusal olmayan zaman serisi modelleri ve gelişmekte olan ülke borsaları üzerine bir uygulama. Doktora Tezi, Eskişehir Osmangazi Üniversitesi, Sosyal Bilimler Enstitüsü, Eskişehir.
• Hellström, T. ve Holmström, K. (1998). Predicting the stock market. Technical Report Series IMa-TOM-1997-07.
• Hipel, K. W. ve McLeod, A. I. (1994). Time series modelling of water resources and environmental systems. Amsterdam, London, New York, Tokyo: Elsevier.
• Inglada-Perez, L. (2020). A Comprehensive framework for uncovering non-linearity and chaos in financial markets: Empirical evidence for four major stock market indices. Entropy, 22(12), 1435.
• Jánský, I. ve Rippel, M. (2011). Value at risk forecasting with the ARMA-GARCH family of models in times of increased volatility. IES Working Paper: 27/2011.
• Jasic, T. ve Wood, D. (2004). The profitability of daily stock market indices trades based on neural network predictions: Case study for the S&P 500, the DAX, the TOPIX and the FTSE in the period 1965–1999. Applied Financial Economics, 14(4), 285-297.
• Jiang, W. (2012). Using the GARCH model to analyse and predict the different stock markets. Master Thesis, Uppsala University, Department of Statistics, Sweden.
• Karabacak, M., Meçik, O. ve Genç, E. (2014). Koşullu değişen varyans modelleri ile BİST 100 endeks getirisi ve altın getiri serisi volatilitesinin tahmini. Uluslararası Alanya İsletme Fakültesi Dergisi/International Journal of Alanya Faculty of Business, 6(1), 79-90.
• Kruskal, W. H. ve Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47(260), 583-621.
• Kutlar, A. ve Torun, P. (2013). İMKB 100 endeksi günlük getirileri için uygun genelleştirilmiş farklı varyans modelinin seçimi. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 42, 1-24.
• Małecka, M. (2014). GARCH class models performance in context of high market volatility. Acta Universitatis Lodziensis Folia Oeconomica, 3(302), 253-266.
• McKenzie, M. D. (1997). ARCH modelling of Australian bilateral exchange rate data. Applied Financial Economics, 7(2), 147-164.
• McLeod, A. I. ve Li, W. K. (1983). Diagnostic checking ARMA time series models using squared‐residual autocorrelations. Journal of Time Series Analysis, 4(4), 269-273.
• Mills, T. C. ve Markellos, R. N. (2008). The econometric modelling of financial time series. Cambridge University Press.
• Montgomery, D. C., Jennings, C. L. ve Kulahci, M. (2015). Introduction to time series analysis and forecasting. USA: John Wiley & Sons.
• Mustapa, F. H. ve Ismail, M. T. (2019). Modelling and forecasting S&P 500 stock prices using hybrid Arima-Garch model. Journal of Physics: Conference Series, 1366, 012130.
• Muthukumar, I. ve Subramaniam, G. (2020). Efficacy of time series forecasting (ARIMA) in post-COVID econometric analysis. International Journal of Statistics and Applied Mathematics, 5(6), 20-27.
• Mutunga, T. N., Islam, A. S. ve Orawo, L. A. O. (2015). Implementation of the estimating functions approach in asset returns volatility forecasting using first order asymmetric GARCH models. Open Journal of Statistics, 5(05), 455-463.
• Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 59(2), 347-370.
• Neokosmidis, I. (2009). Econometric analysis of realized volatility: Evidence of financial crisis. Erişim adresi https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.336.5228&rep= rep1&type=pdf
• Özden, Ü. H. (2008). İMKB bileşik 100 endeksi getiri volatilitesinin analizi. İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, 7(13), 339-350.
• Özmen, A. (1986). Zaman serisi analizinde Box-Jenkins yöntemi ve banka mevduat tahmininde uygulama denemesi. Doktora Tezi, Anadolu Üniversitesi, Sosyal Bilimler Enstitüsü, Eskişehir.
• Peters, J. P. (2001). Estimating and forecasting volatility of stock indices using asymmetric GARCH models and (skewed) student-t densities. Erişim adresi http://citeseerx.ist.psu.edu/ viewdoc/download;jsessionid=AEE3119AF4DCDA0F66DFED86D9AA6874?doi=10.1.1.465.87&rep=rep1&type=pdf
• Qiu, D. (2015). Package ‘aTSA’. Erişim adresi https://cran.r-project.org/web/packages/aTSA/aTSA.pdf
• Rostan, P., Rostan, A. ve Nurunnabi, M. (2020). Options trading strategy based on ARIMA forecasting. PSU Research Review, 4(2), 111-127.
• Sekmen, T. ve Hatipoğlu, M. (2015). Effect of the subprime crisis on return and volatility of the Turkish stock market. Journal of Economics and Behavioral Studies, 7(3), 23-29.
• Sevüktekin, M. ve Nargeleçekenler, M. (2010). Ekonometrik zaman serileri analizi EViews uygulamalı. Nobel Akademik Yayıncılık.
• Song, W. (2012). The financial returns to US public agricultural research: A time series analysis. University of Wyoming.
• Srinivasan, P. (2011). Modeling and forecasting the stock market volatility of S&P 500 index using GARCH models. IUP Journal of Behavioral Finance, 8(1), 51-69.
• Stoitsova-Stoykova, A. (2017). Relationship between public expectations and financial market dynamics in South-East Europe capital markets. Economic Alternatives, 2, 237-250.
• Tsay, R.S. (2010). Analysis of finacial time series. John Wiley & Sons.
• Wang, W., Guo, Y., Niu, Z. ve Cao, Y. (2009). Stock indices analysis based on ARMA-GARCH model. IEEE International Conference on Industrial Engineering and Engineering Management (s. 2143-2147). Hong Kong, China.
• Xu, H. ve Hamori, S. (2010). Dynamic linkages of stock prices among G7 countries: Effects of the American financial crisis. Economics Bulletin, 30(4), 2656-2667.
• Yılmaz, Ö. (2006). Finansal zaman serilerinde varyans modellemesi. Yüksek Lisans Tezi, Mimar Sinan Güzel Sanatlar Üniversitesi, Fen Bilimleri Enstitüsü, İstanbul.
• Zhong, X. ve Enke, D. (2017). A comprehensive cluster and classification mining procedure for daily stock market return forecasting. Neurocomputing, 267, 152-168.
• Zivot, E ve Wang, J. (2006). Modelling financial time series with S-PLUS. New York, NY: Springer.