Journals / Bankacılar / 2016 / Cilt: 27 - Sayı: 98

The Analysis of the Relationship Between the BIST Banking Index (XBANK) and the Indices of VIX and MOVE

BIST Banka Endeksi'nin (XBANK) VIX ve MOVE Endeksleri ile İlişkisinin Analizi

Pages
57–72
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)

The results of Granger causality test indicate that there is causality from the VIX index to the XBANK index while there is no causality from the MOVE index to the XBANK index

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The aim of this study is to analyze the relationship between BIST Banking Index (XBANK) and indices of the VIX (Chicago Board Options Exchange Volatility Index) and MOVE (Merrill Lynch's Treasury Option Volatility Expectations Index) followed carefully in international financial markets.
📊
Key Findings & Data:Daily data was used within the period of 1998 - 2015.
💡
Conclusion & Impact:The results of Granger causality test indicate that there is causality from the VIX index to the XBANK index while there is no causality from the MOVE index to the XBANK index
🔬Design:Research Article
📍Scope:2015 Verileri
⚙️Variables:Domain Parameters

Abstract

The aim of this study is to analyze the relationship between BIST Banking Index (XBANK) and indices of the VIX (Chicago Board Options Exchange Volatility Index) and MOVE (Merrill Lynch's Treasury Option Volatility Expectations Index) followed carefully in international financial markets. Daily data was used within the period of 1998 - 2015. For this purpose firstly, ARDL / Bounds Test is performed which is allowing the series with different levels of stationarity in order to detect the long run relationship. Then, Toda and Yamamoto (1995) approach is used to test for Granger causality relationship between variables. The findings suggest that there is no long run relationship between the XBANK index and indices of VIX and MOVE. The graphs of CUSUM and CUSUMSQ tests also confirm that there is no long-term relationship among the variables. The results of Granger causality test indicate that there is causality from the VIX index to the XBANK index while there is no causality from the MOVE index to the XBANK index

Özet

Bu çalışmanın amacı, uluslararası mali piyasalarda dikkatli bir şekilde takip edilen VIX (Chicago Board Options Exchange Volatility Index) ve MOVE (Merrill Lynch Treasury Option Volatility Expectations Index) endeksleri ile Borsa İstanbul Banka Endeksi (XBANK) arasındaki ilişkinin,1998 - 2015 dönemi için, günlük veriler kullanılarak analiz edilmesidir. Bu amaçla, ilk olarak serilerin farklı düzeyde durağanlıklarına rağmen uzun dönem ilişkinin sınanmasına olanak tanıyan ARDL/Sınır testi uygulanmıştır. Sonrasında, Toda ve Yamamoto (1995) Granger nedensellik testi kullanılarak değişkenler arası nedensellik ilişkisi sınanmıştır. Elde edilen bulgulara göre, XBANK ile VIX ve MOVE endeksleri arasında bir uzun dönem ilişkisi bulunamamıştır. CUSUM ve CUSUMSQ testi grafiklerinin sonuçları da değişkenler arasında uzun dönemli bir ilişkinin olmadığını teyit etmektedir. Granger nedensellik testi sonuçlarına göre ise VIX endeksinden XBANK endeksine doğru bir nedenselliğe rastlanırken, MOVE endeksinden XBANK endeksine doğru bir nedensellik ilişkine rastlanılmamıştır

🔖

Cite This Article

ERDOĞDU, H., BAYKUT, E. (2016). The Analysis of the Relationship Between the BIST Banking Index (XBANK) and the Indices of VIX and MOVE. Bankacılar, Cilt: 27 - Sayı: 98, 57–72. https://ajindex.com/articles/145979

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (15)

  1. Baba, N. & Sakurai, Y. (2011). Predicting Regime Switches In The VIX Index With Macroeconomic Variables. Http://Dx.Doi.Org/10.1080/13504851.2010.539532. Economics Letters. 18, Pp. 1415-1419.
  2. Bagchi, D. (2012). Cross-Sectional Analysis of Emerging Market Volatility Index (India VIX) With Portfolio Returns. Int Ernational Journal of Emerging Market. 7(4): 383-396.
  3. Balcılar, M. & Demirer, R. (2015). Impact Of Global Shocks And Volatility on Herd Behavior In an Emerging Market: Evidence From Borsa Istanbul, Emerging Markets Finance and Trade Review. 51, Pp. 1-20.
  4. Bali, T. G. & Weinbaum, D. (2007). A Conditional Extreme Value Volatility Estimator Based On - Frequency Returns. Journal of Econometrics. 105, 5-26.
  5. Becker R., Clements A. E., & White, S. (2006). On The Informational Efficiency of S&P 500 Implied Volatility. North Am J Econ Finance. 17(2), 139-153.
  6. Black, F. & Scholes, M. (1973). The Pricing of Options and Corporate Liabilities. Journal of Political Economy. 81(3), 637-654.
  7. Black, F. (1976). Studies of Stock Price Volatility Changes. Proceedings of The 1976 Meetings of the American Statistical Association, Business And Economical Statistics Section. 177-181.
  8. Blair, B., Poon S. H., & Taylor, S. J. (2001). Forecasting S&P 100 Volatility: The Incremental Information Content of Implied Volatilities And High-Frequency Index Returns. Journal Of Econometrics. 105, 5-26. Chandra,A. And Thenmozhi M. (2015). On Asymmetric Relationship of India Volatility Index With Stock Market Return And Risk Management,.Decısıon.42 (1), 33-55.
  9. Christie, A.A. (1982). The Stochastic Behavior of Common Stock Variances: Value, Leverage, and İnterest Rate Effects. Journal Of Financial Economics. 10, 407-432.
  10. Dash, S., Moran, M.T. (2005). VIX As A Companion of Hedge Fund Portfolios. The Journal of Alternative Investments. 8, 75-82.
  11. Dickey, D. A. & Fuller W. A. (1981). Likelihood Ratio Statistics For Autoregressive Time Series With A Unit Root. Econometrica, 49(4), 1057-1072.
  12. Dickey, D. A. & Fuller, W. A. (1979). Distribution Of The Estimators For Autoregressive Time Series With A Unit Root. Journal of The American Statistical Association. 74(366), Pp. 427-431.
  13. Dowling, S. & Muthuswamy, J. (2005). The İmplied Volatility of Australian İndex Options. Rev Futur Mark. 14(1): Pp. 117-155.
  14. Engle, R. F. & Granger, C.J.W. (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica. 55(2), 251-276.
  15. Esqueda, O., Luo, Y. & Jackson D. (2013). The Linkag