BIST 30 ENDEKSİ VE DOLAR-TL KURU İÇİN FUTURES KONTRATLARA DAYALI OPTİMAL HEDGE RASYOLARININ VE HEDGİNG ETKİNLİĞİNİN İNCELENMESİ: KAPSAMLI BİR ANALİZ

Yıl 2019, Cilt: 4 Sayı: 4, 514 - 544, 31.12.2019

Doç. Dr Önder Büberkökü

https://doi.org/10.29106/fesa.645626

Cited By: 1

Öz

Bu çalışmada BIST 30 endeksi
ile Dolar-TL kuru  üzerine yazılı
futures  kontratların sunduğu optimal hedge
rasyoları ve hedging etkinliği 
incelenmiştir. Çalışmada,  kapsamlı bir analiz sunulması amacıyla, hem
DBEKK, CCC-GARCH, DCC-GARCH, GOGARCH-ML ve GOGARCH-NLS modellerinden oluşan
dinamik hedging stratejilerine hem de OLS, VAR, ECM ile kısa ve uzun hafızalı
GARCH modellerine (GARCH, GJR-GARCH, FIGARCH, FIEGARCH) dayalı statik hedging
stratejilerine yer verilmiştir. En uygun modelin belirlenmesinde ise minumum
varyans yaklaşımı ile ortalama varyans yaklaşımına dayalı fayda fonksiyonundan
yararlanılmıştır.Çalışma bulguları, BIST30 endeksi için DBEKK modeli tarafından
sunulan optimal hedge rasyosunun; Dolar-TL kuru içinse GOGARCH-NLS modeli
tarafından sunulan optimal hedge rasyosunun hedging etkinliğinin daha iyi
olduğunu göstermektedir.

Anahtar Kelimeler

Optimal hedge rasyosu, Futures kontratlar, Çok değişkenli GARCH modelleri

Kaynakça

• Ai, C.,Chatrath,A. & Song, F. (2007), “A Semi-parametric Estimation of the Optimal Hedge Ratio”, The Quarterly Review of Economics and Finance, 47 (2), s. 366-381.
• Aksoy, G. & Olgun, O. (2009), “Optimal Hedge Oranı Tahminlemesi Üzerine Ampirik Bir Çalışma: VOB Örneği”, İktisat İşletme ve Finans Dergisi, 24 (274), s.33-53.
• Aragó, V.& Salvador, E. (2011), “Sudden Changes in Variance and Time Varying Hedge Ratios”, European Journal of Operational Research, 215 (2), s.393-403.
• Awang, N., Azizan, N.A., İbrahim, I. & Said, R.M. (2014), “Hedging Effectiveness Stock Index Futures Market: An Analysis on Malaysia and Singapore Futures Market”, International Conference on Economics, Management and Development, https://pdfs.semanticscholar.org/ 74d1/e4336edb66eccd7d9276 24a36657432 380f4.pdf.
• Bai, J., & Perron , P. (2003), “Computation and Analysis of Multiple Structural Change Models”, Journal of Applied Econometrics, 18, s.1–22.
• Bai, J., & Perron, P. (1998), “Estimating and Testing Linear Models with Multiple Structural Changes”, Econometrica, 66, s.47-78.
• Baillie, R.T., Bollerslev, T. & Mikkelsen, H.O. (1996), “Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity”, Journal of Econometrics, 74, s.3–30.
• Basher, S.A. & Sadorsky,P. (2016), “Hedging Emerging Market Stock Prices with Oil, Gold, VIX, and Bonds: A Comparison Between DCC, ADCC and GO-GARCH”, Energy Economics 54, 235–247.
• Bollerslev, T. & Mikkelsen, H.O. (1996), “Modeling and Pricing Long Memory in Stock Market Volatility”, Journal of Econometrics, 73, s.151-184.
• Bollerslev, T. (1986), “Generalized Autoregressive Conditional Heteroskedasticity”, Journal of Econometrics, 31,s.307-327.
• Bollerslev, T. (1990), “Modelling the Coherence in Short-Run Nominal Exchange Rate: A Multivariate Generalized ARCH approach”, Review of Economics and Statistics, 72, s.498–505.
• Boswijk, H.P. & van der Weide, R., (2006), “Wake Me up Before You GO-GARCH”, Tinbergen Institute Discussion Paper, No. 06-079/4, Tinbergen Institute, Amsterdam and Rotterdam.
• Chang, C.L., McAleer, M. & Tansuchat,R. (2011), “Crude Oil Hedging Strategies Using Dynamic Multivariate GARCH”, Energy Economics 33(5), s.912-923.
• Chang, C-L., González-Serrano, L. & Jimenez-Martin, J-A. (2013), “Currency Hedging Strategies Using Dynamic Multivariate GARCH ”, Mathematics and Computers in Simulation, 94, s.164–182.
• Chen, S-S., Lee,C-F. & Shrestha, K. (2003), “Futures Hedge Ratios: A Review”, The Quarterly Review of Economics and Finance, 43, s.433–465.
• Choudhry, T. (2003), “Short-Run Deviations and Optimal Hedge Ratio: Evidence from Stock Futures”, Journal of Multinational Financial Management, 13(2), s.171-192.
• Coakley, J., Dollery, J. & Kellard, N. (2008), “The Role of Long Memory in Hedging Effectiveness”, Computational Statistics & Data Analysis, 52, s.3075-3082.
• Çavuşoğlu, T. & Gökten, S. (2011), “NYMEX Ham Petrol Vadeli İşlem Sözleşmeleri Pazarında Piyasa ve Korunma Etkinlikleri”, İktisat İşletme ve Finans, 26 (308), 29-51.
• Çelik, İ.& Özdemir, A. (2014), “Vadeli İşlem Piyasalarında Optimal Hedge Rasyosunun Tahmini: 1979’dan Günümüze Bir Literatür Araştırması”, Mehmet Akif Ersoy Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 6 (10), s. 37-46.
• Çevik,İ. (2014), “Vadeli İşlem Piyasasında Optimal Hedge Rasyosunun Statik ve Dinamik Teknikler ile Hesaplanması”, Uluslararası Alanya İşletme Fakültesi Dergisi, 6(3), 1-13.
• Dickey, D. & Fuller, W. (1979), “Distribution of the Estimators for Autoregressive Time Series with Unit Root”, Journal of American Statistical Association, 74, s.427-431.
• Ederington, L. H. (1979), “The Hedging Performance of the New Futures Markets”, Journal of Finance, 34, 157–170.
• Engle, R. & Kroner, K. (1995). “Multivariate Simultaneous Generalized ARCH”, Econometric Theory, 11, s.122-150.
• Engle, R. (2002), “Dynamic Conditional Correlation. A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models , Journal of Business and Economic Statistics, 20, s.339–350.
• Engle,R.F. & Granger,C.W.J. (1987), “Cointegration and Error Correction: Represen-tation, Estimation and Testing”, Econometrica, 55, s.251-276.
• Ersoy, E. (2011), “Türkiye’de ve Dünyada Organize Türev Piyasaların Gelişimi”, Muhasebe ve Finansman Dergisi, 51, s. 63-80.
• Ghoddusi, H. & Emamzadehfard, S. (2017), “Optimal Hedging in the US Natural Gas Market: The Effect of Maturity and Cointegration”, Energy Economics, 63, s.92-105.
• Glosten, L.R., Jagannathan, R. & Runkle, D.E. (1993), “On the Relation Between the Expected Value and the Volatility of the Nominal Excess Return on Stocks”, Journal of Finance, 48, s. 1779–1801.
• Gök, İ.Y. ( 2016), “Türkiye Pay Endeks Futures Piyasasında Optimum Korunma Oranı ve Korunma Etkililiği”, Ege Akademik Bakış, 16(4), s. 719 -732.
• Gregory, A.W., & Hansen, B.E. (1996), “Residual-based Tests for Cointegration in Models with Regime Shifts”, Journal of Econometrics, 70, s.99-126.
• Hatmi-J, A. & Roca, E.(2006), “Calculating the Optimal Hedge Ratio: Constant, Time-Varying and the Kalman Filter Approach”, Applied Economics Letters, 13, s.293-299.
• Ji,Q. & Fan, Y. (2011), “A Dynamic Hedging Approach For Refineries in Multiproduct Oil Markets”, Energy, 36, s.881-887.
• Johnson, L. L. (1960), “The Theory of Hedging and Speculation in Commodity Futures”, Review of Economic Studies, 27, s.139–151.
• Kim, J.M. & Park, S.Y. (2016), “Optimal Conditional Hedge Ratio: A Simple Shrinkage Estimation Approach”, Journal of Emprical Finance, 38, s.139-156.
• Kotkatvuori-Örnberg, J. (2016), “Dynamic Conditional Copula Correletion and Optimal Hedge Ratios with Currency Futures”, International review of Financial analysis, 47, s.60-69.
• Kroner, K.F. & Sultan, J. (1993), “Time-Varying Distributions and Dynamic Hedging with Foreign Currency Futures”, The Journal of Financial and Quantitative Analysis, 28(4),s. 535-551.
• Kumar, B., Singh, P.& Pandey, A. (2008), “Hedging Effectiveness of Constant and Time Varying Hedge Ratio in Indian Stock and Commodity Futures Markets (August 6, 2008)”, https://ssrn.com/ abstract=1206555 or http:// dx.doi.org/ 10.2139/ssrn.1206555. (Erişim tarihi:12.09.2019).
• Lai, Y.S. (2019), “Evaluating the Hedging Performance of Multivariate GARCH Models”, Asia Pacific Management Review, 24 (1), s.86-95.
• Lo, A.W.(1991). "Long-Term Memory in Stock Market Prices”, Econometrica, 59 (5), s.1279-1313.
• McMillan, D. (2005), “Time-Varying Hedge Ratios for Non-Ferrous Metals Prices”, Resources Policy, 30 (3), s.186-193.
• Nelson, D. (1991), “Conditional Heteroskedasticity in Asset Return: A New Approach”, Econometrica, 59, s.2589–2598.
• Olgun, O.& Yetkiner, H. (2011), “Determination of Optimal Hedging Strategy for Index Futures: Evidence from Turkey”, Emerging Markets Finance and Trade, 47(6), s.68-79.
• Özaydın, O. (2018), “Vadeli BIST 30 Endeksi Kontratları Üzerine Koruma Oranı Tahmini ve Koruma Oranı Etkinliği”, Bankacılık ve Sermaye Piyasası Araştırmaları Dergisi-BSPAD, 2(6), s. 16-27.
• Phillips, P.C.B.& P. Perron. (1988), “Testing for a Unit Root in Time Series Regression”, Biometrika,75, 335-346.
• Robinson, P.M. & M. Henry (1999), “Long and Short Memory Conditional Heteroskedasticity in Estimating the Memory Parameter of Levels”, Econometric Theory, 15(3), 299-336.
• Schwert, W. (1989),“Tests for Unit-Roots: A Monte Carlo Investigation”, Journal of Business and Economic Statistics, 7 (2), s.147-159.
• Silber, W. (1985), “The Economic Role of Financial Futures. In: Peck, A.E. (Ed.), Futures Markets: Their Economic Role”,American Enterprise Institute for Public Policy Research, Washington, DC.
• Sing, G. (2017), “ Estimating Optimal Hedge Ratio and Hedging Effectiveness in the NSE Index Futures”, Journal of Business Research, 6(2), s.108-131.
• van der Weide, R. (2002), “GO-GARCH: A Multivariate Generalized Orthogonal GARCH Model”, Journal of Applied Econometrics, 17, s.549-564.
• Wang, Y. & Wu, C. (2012), “Forecasting Energy Market Volatility using GARCH Models: Can Multivariate Models Beat Univariate Models?, Energy Economics, 34, s.2167-2181.
• Wang, Y., Geng, Q.& Meng, F. (2019), “Futures Hedging in Crude Oil Markets: A Comparison between Minimum-Variance and Minimum-Risk Frameworks”, Energy, 181(15), s.815-826.
• Zakoian, J. (1994), “Threshold Heteroskedastic Models”, Journal of Economic Dynamics and Control, 18, s. 931-955.
• Zhou, J. (2016), “Hedging Performance of REIT Index Futures: A Comparison of Alternative Hedge Ratio Estimation Methods”, Economic Modelling, 52, s.690-698.
• Zivot, E., & Andrews, D.W.K. (1992), “Further Evidence on the Great Crash, the Oil Price Shocks, And Unit Root Hypothesis”, Journal of Business & Economic Statistics, 10, s.251-270.