Küresel Ekonomik Politik Belirsizliğin Türkiye’de BİST Endeksi ve Döviz Kuru Dalgalanmalarındaki Rolü: GARCH-MIDAS Yaklaşımı

Yıl 2023, , 508 - 534, 30.06.2023

Yağmur Tokatlıoğlu

https://doi.org/10.18074/ckuiibfd.1286397

Öz

Oynaklık tahmini uygulayıcılar ve politika yapıcılar için önemli bir araçtır. Bu çalışmada, düşük ve yüksek frekanslı değişkenlerin aynı modelde yer almasına imkan tanıyan GARCH-MIDAS yöntemi kullanılmış ve GEPU Endeksinin Türkiye’de hisse senetleri fiyatları ve döviz kuru oynaklığını tahmin etmedeki rolünün ortaya çıkarılması hedeflenmiştir. Hisse senetleri fiyatlarını temsilen seçilmiş BİST Endeksleri ile ABD Dolar ve Euro kuru yüksek frekanslı bağımlı değişken ve her modelde düşük frekanslı bağımsız değişken olarak GEPU Endeksinin alındığı dokuz model kurulmuştur. Modellerinin hepsinde kısa dönem parametrelerinin istikrarlı ve kısa dönem oynaklıkların kalıcı olduğu; GEPU Endeksinin BİST100, BİST30, BİST Tüm-100, BİST Hizmet, BİST Mali ve BİST Sınai endekslerinde yer alan hisse senetleri fiyatları ile ABD Dolar ve Euro kurlarının uzun dönem oynaklığı üzerinde önemli bir pozitif etkisi olduğu sonucuna ulaşılmıştır. Küresel ekonomik-politik belirsizlikte yaşanan bir artış, BİST endekslerini ve döviz kurlarını değişken hale getirecektir. Bu durum, Türk Borsası ve döviz piyasası küresel ekonomik-siyasi olaylara entegre olduğunu göstermektedir.

Anahtar Kelimeler

BİST Endeksi, Döviz Kuru, GEPU Endeksi, Oynaklık Tahmini, GARCH-MIDAS

Kaynakça

• Ağaslan, E., ve Gayaker S. (2020). Türkiye Ekonomisindeki Emisyon Hacminin Düşük ve Yüksek Frekanslı Modeller ile Öngörüsü. Bankacılar, 31(114), 30-49.
• Altay, E. (2015). Knight Belirsizliği: Risk ve Muğlaklığın Borsa İstanbul Aşırı Getiri Oranları Üzerindeki Etkisi. BDDK Bankacılık ve Finansal Piyasalar, 9(2), 45-72.
• Amendola, A., Candila, V., ve Scognomillo, A. (2017). On the Influence of US Monetary on Crude Oil Price Volatility. Empirical Economics, 52(1), 155-178.
• Antonakakis, N., Chatziantoniou, I., ve Filis, G. (2013). Dynamic co-movements of stock market returns, implied volatility and policy uncertainty. Economics Letters, 120(1), 87-92.
• Angelini, G., Bacchiocchi, E., Caggiano, G., ve Fanelli, L. (2019). Uncertainty across volatility regimes. J. Appl. Econometrics, 34(3), 437–455.
• Asgharian, H., Hou, A. J., ve Javed, F. (2013). The importance of the macroeconomic variables in forecasting stock return variance: A GARCH-MIDAS approach. Journal of Forecasting, 32(7), 600–612.
• Baker, S.R. Bloom, N., ve Davis, S. (2012). Has Economic Policy Uncertainty Hampered the Recovery? Chicago Booth Research Paper No. 12-06, SSRN: https://ssrn.com/abstract=2000734.
• Baker, S.R., Bloom, N., ve Davis, S.J. (2016). Measuring Economic Policy Uncertainty. The Quarterly Journal of Economics, 131(4), 1593-1636.
• Başarır, Ç. (2018). Korku endeksi (VIX) ile BIST 100 arasındaki ilişki: Frekans alanı nedensellik analizi. İşletme Fakültesi Dergisi, 19(2), 177-191.
• Bernanke, B. S. (1983). Irreversibility, Uncertainty, and Cyclical Investment. Quarterly Journal of Economics, 98(1), 85-106.
• Bloom, N. (2006). The impact of uncertainty shocks: Firm-level estimation and a 9/11 simulation. CEP Discussion Papers, No:718, Centre for Economic Performance, LSE.
• Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
• Bollerslev, T., Hood, B., Huss, J., ve Pedersen, L.H. (2018). Risk everywhere: modeling and managing volatility. The Review of Financial Studies, 31(7), 2729–2773.
• Borsa İstanbul. (2023). BIST Endeksleri, https://www.borsaistanbul.com/tr/sayfa/2915/bist-endeksleri
• Brogaard, J., ve Detzel, A. (2015). The asset-pricing implications of government economic policy uncertainty. Management Science, 61(1), 3-18.
• Cochran, S.J., Mansur, I., ve Odusami, B. (2012). Volatility persistence in metal returns: A FIGARCH approach. Journal of Economics and Business, 64(4), 287–305.
• Conrad, C., Custovic, A., ve Ghysels, E. (2018). Long-and short-term cryptocurrency volatility components: A GARCH-MIDAS analysis. Journal of Risk and Financial Management, 11(2), 23.
• Davis, S. J. (2016). An Index of Global Economic Policy Uncertainty. National Bureau Of Economic Research, NBER Working Paper No. 22740.
• Dickey, D. A., ve Fuller, W. A. (1979). Distribution of the Estimators for Autoregressive Time-Series with a Unit Root. Journal of the American Statistical Association, 74(366), 427-431.
• Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4), 987-1007.
• Engle, R. F., ve Rangel, J. G. (2008). The spline-GARCH model for low-frequency volatility and its global macroeconomic causes. Review of Financial Studies, 21(3), 1187-1222.
• Engle, R. F., Ghysels, E., ve Sohn, B. (2013). Stock Market Volatility and Macroeconomic Fundamentals. The Review of Economics and Statistics, 95(3), 776-797.
• EPU. (2022). Economic Policy Uncertainty. https://www.policyuncertainty.com/index.html (Erişim Tarihi: 05.03.2023).
• Fan, T., Khaskheli, A., Raza, S. A., ve Shah, N. (2022). The role of economic policy uncertainty in forecasting housing prices volatility in developed economies: Evidence from a GARCH-MIDAS approach. International Journal of Housing Markets and Analysis, ahead-of-print, https://doi.org/ 10.1108/IJHMA-03-2022-0040.
• Fang, L., Chen, B., Yu, H., ve Qian, Y. (2018). The importance of global economic policy uncertainty in predicting gold futures market volatility: A GARCH-MIDAS approach. Journal of Futures Markets, 38(3), 413-422.
• Fang, T., Lee, T. H., ve Su, Z. (2020). Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection. Journal of Empirical Finance, 58, 36-49.
• Ghysels, E. (2017). MIDAS Matlab Toolbox. https://www.mathworks.com/matlabcentral/fileexchange/45150-midas-matlabtoolbox. (Erişim tarihi: 19.08.2022).
• Ghysels, E., Santa-Clara, P., ve Valkanov. R. (2004). The MIDAS Touch: Mixed Data Sampling Regression Models. CIRANO, ISSN: 1198-8177, https://escholarship.org/uc/item/9mf223rs. (Erişim Tarihi: .20.04.2023).
• Girardin, E., ve Joyeux, R. (2013). Macro Fundamentals as a Source of Stock Market Volatility in China: A GARCH-MIDAS Approach. Economic Modelling, 34, 59-68.
• Gürsoy S., ve Kılıç E. (2021), Küresel Ekonomik Politik Belirsizliğin Türkiye CDS Primi ve BİST Bankacılık Endeksi Üzerindeki Volatilite Etkileşimi: DCC-GARCH Modeli Uygulaması. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 35(4), 1323-1334.
• Gürsoy, S. (2021). Küresel Ekonomik Politik Belirsizliğin (GEPU) Döviz Kuru, Enflasyon ve Borsa Etkisi: Türkiye’den Kanıtlar. Journal of Vocational and Social Sciences of Turkey, 3(5), 120-131.
• IMF. (2012). Coping with High Debt and Sluggish Growth. World Economic Outlook October 2012, Washington, DC: International Monetary Fund.
• Korkmaz, Ö., ve Güngör, S. (2018). Küresel Ekonomi Politika Belirsizliğinin Borsa İstanbul’da İşlem Gören Seçilmiş Endeks Getirileri Üzerindeki Etkisi. Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi, 6(ICEESS’ 18), 211-219.
• Kök, D., ve Nazlıoğlu, E. H. (2020). Finansal Piyasalarda Asimetrik Nedensellik: Bıst100, VIX ve Döviz Kuru Örneği. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 55, 245-262.
• Li, T., Ma, F., Zhang, X., ve Zhang, Y. (2020). Economic policy uncertainty and the Chinese stock market volatility: Novel evidence. Economic Modelling, 87, 24–33.
• Liow, K.H., Liao, W.C., ve Huang, Y. (2018). Dynamics of international spillovers and interaction: Evidence from financial market stress and economic policy uncertainty. Economic Modelling, 68, 96–116.
• Liu, J., Ma, F., Tang, Y.,ve Zhang, Y. (2019). Geopolitical risk and oil volatility: A new insight. Energy Economics, 84, 104548.
• Liu, J., Zhang, Z., Yan, L., ve Wen, F. (2021). Forecasting the volatility of EUA futures with economic policy uncertainty using the GARCH-MIDAS model. Financial Innovation, 7, 76.
• Ma, R., Zhou, C., Cai, H., ve Deng, C. (2019). The forecasting power of EPU for crude oil return volatility. Energy Reports, 5, 866–873.
• Marcellino, M. (1999). Some Consequences of Temporal Aggregation In Empirical Analysis. Journal of Business & Economic Statistics, 17(1), 129-136.
• Mo, D., Gupta, R., Li, B., ve Singh, T. (2018). The macroeconomic determinants of commodity futures volatility: evidence from Chinese and Indian markets. Economic Modelling, 70, 543–560.
• Ndako, U. B., Salisu, A. A., ve Ogunsiji, M. O. (2021). Geopolitical risk and the return volatility of Islamic stocks in Indonesia and Malaysia: A GARCH-MIDAS approach. Asian Economics Letters, 2(3), 1–5.
• Oloko, T. F., Adediran, I. A., ve Fadiya, O. T. (2022). Climate Change and Asian Stock Markets: A GARCH-MIDAS Approach. Asian Economics Letters, 4(Early View).
• Özsoy, F., ve Doğan, N. (2022). Deterministic Effects of Volatility on Mixed Frequency GARCH in Means MIDAS Model: Evidence from Turkey. International Econometric Review, 14(1), 1-20.
• Pan, Z., Wang, Y., Wu, C., ve Yin, L. (2017). Oil price volatility and macroeconomic fundamentals: A regime switching GARCH-MIDAS model. Journal of Empirical Finance, 43, 130–142.
• Pástor, L., ve Veronesi, P. (2013). Political uncertainty and risk premia. Journal of Financial Economics, 110(3), 520-545.
• Phillips, P.C.B., ve Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346.
• Poon, S.H., ve Granger, C.W. (2003). Forecasting volatility in financial markets: A review. Journal of Economic Literature, 41 (2), 478e539.
• Sadeghzadeh Emsen, H., ve Aksu, L. (2020). Borsa İstanbul ve Belirsizlik Endeksi Arasındaki İlişkilerin Doğrusal Olup Olmadığına Dair İncelemeler (1998:01- 2018:12). Atatürk Üniversitesi SBE Dergisi, 24 (1), 445-462.
• Sadegzadeh, K., ve Elmas, B. (2018). Makro-ekonomik Faktörlerin Hisse Senedi Getirilerine Etkilerinin BIST’de Araştırılması. Muhasebe ve Finansman Dergisi, 80, 207-232.
• Salisu, A. A., ve Gupta, R. (2021). Oil shocks and stock market volatility of the BRICS: A GARCH-MIDAS approach. Global Finance Journal, 48, 100546.
• Salisu, A. A., Gupta, R., ve Demirer, R. (2022). Global financial cycle and the predictability of oil market volatility: Evidence from a GARCH-MIDAS model. Energy Economics, 108, 105934.
• Salisu, A. A., Gupta, R., Bouri, E., ve Ji, Q. (2020). The role of global economic conditions in forecasting gold market volatility: Evidence from a GARCH-MIDAS approach. Research in International Business and Finance, 54, 101308.
• Sum, V. (2012). Economic policy uncertainty and stock market performance: Evidence from the European Union, Croatia, Norway, Russia, Switzerland, Turkey and Ukraine. Journal of Money, Investment and Banking, 25, 99-104. https://ssrn.com/abstract=2094175.
• Tiryaki, H. N., ve Tiryaki, A. (2019). Determinants of Turkish Stock Returns under the Impact of Economic Policy Uncertainty. International Journal of Economic and Administrative Studies, (22), 147-162.
• Tokatlıoğlu, Y. (2022). Türkiye’de Döviz Kuru Oynaklığının Kısa ve Uzun Dönem Bileşenlerinin Tahmini, E. Ağaslan, Y. Tokatlıoğlu içinde, İktisadi Gelişmelerin Ekonometrik Analizi Paket Program Uygulamaları İle birlikte (R, Matlab, Stata) (s. 197-244), Ankara: Gazi Kitabevi.
• Walther, T., Klein, T., ve Bouri, E. (2019). Exogenous drivers of Bitcoin and Cryptocurrency volatility–A mixed data sampling approach to forecasting. Journal of International Financial Markets, Institutions and Money, 63, 101133. Wang, L., Ma, F., ve Liu, G. (2020). Forecasting stock volatility in the presence of extreme shocks: short-term and long-term effects. Journal of Forecasting, 39, 797–810.
• Wang, L., Ma, F., Liu, G., ve Lang, Q. (2021). Do extreme shocks help forecast oil price volatility? The augmented GARCH-MIDAS approach. International Journal of Finance & Economics. Early View. Doi: https://doi.org/10.1002/ijfe.2525.
• Wei, Y., Liu, J., Lai, X., ve Hu, Y. (2017). Which determinant is the most informative in forecasting crude oil market volatility: Fundamental, speculation, or uncertainty? Energy Economics, 68, 141–150.
• Wei, Y., Yu, Q., Liu, J., ve Cao, Y. (2018). Hot money and China’s stock market volatility: further evidence using the GARCH–MIDAS model. Physica A: Statistical Mechanics and its Applications, 492, 923–930.
• Xia, Y., Sang, C., He, L., ve Wang, Z. (2022). The role of uncertainty index in forecasting volatility of Bitcoin: fresh evidence from GARCH-MIDAS approach. Finance Research Letters, 52, 103391.
• Xu, L., Zou, Z., ve Zhou, S. (2022). The influence of COVID-19 epidemic on BDI volatility: an evidence from GARCH-MIDAS model. Ocean & Coastal Management, 229, 106330.
• Xu, Q., Bo, Z., Jiang, J., ve Liu, Y. (2019). Does Google Search Index Really Help Predicting Stock Market Volatility? Evidence from the Modified Mixed Sampling Model on Volatility. Knowledge-Based Systems, 166, 170-185.
• Yang, M., Zhang, Q., Yi, A., ve Peng, P. (2021). Geopolitical risk and stock market volatility in emerging economies: Evidence from GARCH-MIDAS model. Discrete Dynamics in Nature and Society, 2021, 1159358.
• Yaya, O. O. S., Ogbonna, A. E., ve Vo, X. V. (2022). Oil shocks and volatility of green investments: GARCH-MIDAS analyses. Resources Policy, 78(February), 102789.
• You, Y., ve Liu, X. (2020). Forecasting short-run exchange rate volatility with monetary fundamentals: A GARCH-MIDAS approach. Journal of Banking & Finance, 116, 105849.
• Yu, H., Fang, L., ve Sun, B. (2018a). The role of global economic policy uncertainty in long-run volatilities and correlations of U.S. industry-level stock returns and crude oil. Plos One, 13(2), 1-17.
• Yu, H., Fang, L., ve Sun, W. (2018b). Forecasting performance of global economic policy uncertainty for volatility of Chinese stock market. Physica A: Statistical Mechanics and Its Applications, 505, 931–940.
• Yu, X., ve Huang, Y. (2021). The impact of economic policy uncertainty on stock volatility: Evidence from GARCH-MIDAS approach. Physica A: Statistical Mechanics and its Applications, 570, 125794.
• Zhou, Z., Fu, Z., Jiang, Y., Zeng, X., ve Lin, L. (2020). Can economic policy uncertainty predict exchange rate volatility? New evidence from the GARCH-MIDAS model. Finance Research Letters, 34, 101258.