SavedAdd your journal

Journals / İstatistik Araştırma Dergisi / 2007 / Cilt: 5 - Sayı: 1

The Analyzing of Volatility on İstanbul Stock Exchange (ISE) National-100 Index by Using Arch Models

Arch Modelleriyle İMKB Ulusal-100 Endeksinde Volatilitenin İncelenmesi

Serpil TÜRKYILMAZ
Pages
10–24
Publication Date
2007-07-13
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 other words, while volatility of series tends to increasing against decreasing shocks (bad news), it tends to decreasing against increasing shocks (good news) effected ISE index.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, it has been examined that the existing of volatility on İstanbul Stock Exchange (ISE) National-100 index by using ARCH-class models.
📊
Key Findings & Data:It has been analyzed the relationship between (ISE) National-100 index and estimated volatility.
💡
Conclusion & Impact:In other words, while volatility of series tends to increasing against decreasing shocks (bad news), it tends to decreasing against increasing shocks (good news) effected ISE index.
🔬Design:Modeling & Simulation
📍Scope:2004 Verileri
⚙️Variables:ARCH Modelleri · EGARCH Modeli · Koşullu Değişen Varyans

Abstract

In this study, it has been examined that the existing of volatility on İstanbul Stock Exchange (ISE) National-100 index by using ARCH-class models. It has been analyzed the relationship between (ISE) National-100 index and estimated volatility. In the examined period 02/01/2004-15/09/2005, EGARCH (1,1) model has been determined as the most fitting conditional heteroscedasticity model for (ISE) National-100 index series. So, it has been deduced that there is an asymmetric relationship between (ISE) National-100 index and its volatility. In other words, while volatility of series tends to increasing against decreasing shocks (bad news), it tends to decreasing against increasing shocks (good news) effected ISE index.

Özet

Bu çalışmada Autoregressive Conditional Heteroskedasticity (ARCH) türü modeller kullanılarak, İstanbul Menkul Kıymetler Borsası (IMKB) ulusal 100 endeksinde volatilitenin (oynaklığın, iniş çıkış eğiliminin) varlığı araştırılmıştır. Seri için en uygun koşullu değişen varyans modeli tahmin edilerek, İMKB ulusal 100 endeksi serisinin değerleri ile volatilitesi arasındaki ilişki incelenmiştir. Ele alınan 02/01/2004 – 15/09/2005 dönemi içerisinde İMKB ulusal 100 endeksi serisi için EGARCH (1,1) modeli, en uygun koşullu değişen varyans modeli olarak belirlenmiştir. Buna göre İMKB endeksi ile volatilitesi arasında asimetrik bir ilişki olduğu sonucuna ulaşılmıştır. Diğer bir ifade ile serinin volatilitesi, İMKB endeksini etkileyen azalan yöndeki şoklara-dalgalanmalara (kötü haber) karşı artma eğilimi gösterirken, artan yöndeki şoklara-dalgalanmalara (iyi haber) karşı azalma eğilimi göstermektedir.

🔖

Cite This Article

TÜRKYILMAZ, S. (2007). The Analyzing of Volatility on İstanbul Stock Exchange (ISE) National-100 Index by Using Arch Models. İstatistik Araştırma Dergisi, Cilt: 5 - Sayı: 1, 10–24. https://ajindex.com/articles/853690

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (18)

  1. Akgiray, V., 1989. Conditional Heteroscedasticity in Time Series of Stock Returns:Evidence and Forecast, Business, 62, 55-80.
  2. Bera, A.K. ve Higgins, M.L, 1993. ARCH Models: Properties, Estimation and Testing, Economics Surveys, 7, 305-366.
  3. Bollerslev, T., 1986. Modelling the Persistence of Conditional Variances, Econometric Reviews, 5, 1-50.
  4. Bollerslev, T., 1990. Modelling the Coherence in Short-Run Nominal Exchange Rates: A Multivariate Generalized ARCH Models, Review of Economics and Statistics, 78, 498-505.
  5. Chou, R.Y., 1988. Volatility Persistence and Stock Valuations: Some Empirical Evidence Using Garch, Applied Econometrics, 3, 279-294.
  6. Demos, A. ve Sentana, E., 1998. Testing for GARCH EFFECTS: A Onesided Approach, Econometrics, 86, 97-127.
  7. Diebold, F.X. ve Lopez, J.A., 1995. Modelling Volatility Dynamics. in: K.D. Hoover, ed, Macroeconometrics: Developments,Ttensions and Prospects, 427-466, Boston: Kluwer. Ding, Z., Granger, W.J. and Engle, R.F., 1993. A Long Memory Property of Stock Market Returns and a New Model, Journal of Empirical Finance, 1, 83-106.
  8. Drost, F.C. ve Nijman, T.E., 1993. Temporal Aggretion of GARCH Processes, Econometrica, 61, 909-927.
  9. Enders, W. 1995.
  10. Applied Econometric Time Series, JohnWiley&Sons,Inc., USA., 135-211.
  11. Engle, R.F., 1982. Autoregressive Conditional Heteroscedasticity with Estimates of Variance of United Kingdom Inflation, Econometrica, 50, 987-1007.
  12. Engle, R.F., 1995. ARCH: Selected Readings (Advanced Texts in Econometrics), Oxford University Press.
  13. Engle, R.F. ve Victor, K., 1993. Measuring and Testing the Impact of News on Volatility, Journal of Finance, 48, 1749-1778.
  14. Engle, R.F. ve Patton, A.J., 2001. What Good is a Volatility Model?, 1-29.
  15. Engle, R.F., Lillien, D.M. ve Robins, R.P., 1987. Estimating Time-Varying Risk Premia in the Term Structure: The ARCH-M Model, Econometrica, 55, 391-408. Eviews 5.1 User’s Guide, 2005.
  16. Quantitative Micro Software, LLC, the United States of America, 1-1014. Gouriéroux, C., 1997. Arch Models and Financial Applications, Springer Verlag. Hamori, S., 2000. Volatility of Real GDP: Some Evidence from the United States, the United Kingdom and Japan, Japan and World Economy,12, 143-152.
  17. Kızılsu, S., Aksoy, S. ve Kasap, R., 2001. Bazı Makroekonomik Zaman Dizilerinde Değişen Varyanslığın İncelenmesi, G.Ü., İ.İ.B.F. Dergisi, 1/2001, 1-18.
  18. Koutmos, G., 1988. Asiymmetries in the Conditional Mean and Conditional Variance: Evidence from Nine Stock Markets, Economics and Business, 50, Issue