[1] AKGİRAY, V. (1989), “Conditional Heteroscedasticity in Time Series of Stock Returns: Evidence and
Forecasts”, The Journal of Business, 62(1), 55-80.
[2] ANDERSEN, T.G. ve T. BOLLERSLEV (1998), “Answering the Skeptics:Yes, Standard Volatility
Models Do provide Accurate Forecasts”, International Economic Review, 39(4), 885-905.
[3] BİLDİRİCİ, M. ve Ö.Ö.ERSİN (2009), “Improving Forecasts of GARCH Family Models with The
Artificial Neural Networks: An Application to The Daily Returns in Istanbul Stock Exchange”, Expert
Systems with Applications, 36, 7355-7362.
GÜRSOY, BALABAN | Volatility Forecasting in Stock Returns Using Support Vector Machines Based...
Kafkas University Journal of Economics and Administrative Sciences Faculty
KAU EASF Journal | Vol 5 * Issue 8 * Year 2014
184
[4] BİLDİRİCİ, M. ve Ö.Ö.ERSİN (2012), “Nonlinear Volatility Models in Economics: Smooth Transition
and Neural Network Augmented GARCH, APGARCH, FIGARCH and FIAPGARCH Models”,
Munich Personal RePEc Archieve, MPRA Paper No: 40330.
[5] BLACK, F. (1976), “Studies in Stock Price Volatility Changes”, in Proceedings of the 1976 Meeting of
the Business and Economic Statistics Section, American Statistical Association, 177-181.
[6] BOLLERSLEV, Tim (1986), “Generalized Autoregressive Conditional Heteroskedasticity”, Journal of
Econometrics, 31, 307-327.
[7] BOLLERSLEV, T. ve J.M.WOOLDRIDGE (1992), “Quasi-Maximum Likelihood Estimation and
Inference in Dynamic Models with Time-Varying Covariances”, Econometric Reviews, 11, 143-172.
[8] BURGES, Christopher J.C. (1998), “A Tutorial of Support Vector Machines for Pattern Recognition”,
Data Mining and Knowledge Discovery, 2, 121-167.
[9] CHAMPBELL, J.Y. ve A.S.KYLE (1993), “Smart Money, Noise Trading and Stock Price Behaviour”,
Review of Economic Studies, 60, 1-34.
[10] CHOU, R.Y. (1988), “Volatility Persistence and Stock Valuations: Some Emprical Evidence Using
GARCH”, Journal of Applied Econometrics, 3(4), 279-294.
[11] CONT, Rama (2001), “Emprical Properties of Asset Returns: Stylized Facts and Statistical Issues”,
Quantitative Finance, 1, 223-236.
[12] DONALDSON, R.G. ve Mark KAMSTRA (1997), “An Artificial Neural Network –GARCH
Model for International Stock Return Volatility”, Journal of Emprical Finance, 4(1), 17-46.
[13] ENGLE, R.F. (1982), “Autoregressive Conditional Heteroscedasticity with Estimates of the
Variance of United Kingdom Inflation”, Econometrica, 50(4), 987-1007.
[14] ENGLE, R.F. ve T.P. BOLLERSLEV (1986), “Modelling the Persistence of Conditional Variances”,
Econometric Reviews, 5(1), 1-50.
[15] ENGLE, R.F. ve V.K. NG (1993), “Measuring and Testing the Impact of News on Volatility”, The
Journal of Finance, 48(5), 1749-1778.
[16] FRANSES, F.H. ve D.V.DIJK (1996), “Forecasting Stock Market Volatility Using (Non-Linear)
GARCH Models”, Journal of Forecasting, 15, 229-235.
[17] GAVRISHCHAKA, V.V. ve S.BANERJEE (2006), “Support Vector Machine as an Efficient
Framework for Stock Market Volatility”, Computational Management Science, 3(2), 147-160.
[18] GLOSTEN, L.R., JAGANNATHAN, R. ve D.E.RUNKLE (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.
[19] GÖKCAN, S. (2000), “Forecasting Volatility of Emerging Stock Markets: Linear versus Nonlinear
GARCH models”, Journal of Forecasting, 19(6), 499-504.
[20] GRANGER, C.W.J. (1992), “Forecasting Stock Market Prices: Lessons for Forecasters”,
International Journal of Forecasting, 8, 3-13.
Hisse Senedi Getirilerindeki Volatilitenin Tahminlenmesinde Destek Vektör Makinele... | GÜRSOY, BALABAN
Kafkas Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi
KAÜ İİBF Dergisi | Cilt 5 * Sayı 8 * Yıl 2014
185
[21] İNCE, H. (2005), “Non Parametric Regression Methods and an Application to Istanbul Stock
Exchange 100 Index”, Yapı Kredi Economic Review, 16(1), 17-28.
[22] KARA, Yakup, BOYACIOĞLU, Melek A. ve Ö.Kaan BAYKAN (2011), “Prediction Direction of
Stock Price Index Movement Using Artificial Neural Networks and Support Vector Machines: The
Sample of the Istanbul Stock Exchange”, Expert Systems With Applications, 38, 5311-5319.
[23] KIM, Kyoung-Jae (2003), “Financial Time Series Forecasting Using Support Vector Machines”,
Neurocomputing, 55, 307-319.
[24] McMILLAN, D. ve R.Q.GARCIA (2009), “Intra-day Volatility Forecasts”, Applied Financial
Economics, 19(8), 611-623.
[25] NELSON, Daniel B. (1991), ”Conditional Heteroskedasticity in Asset Returns: A New Approach”,
Econometrica, 59(2), 347-370.
[26] OU, Phichhang ve H.WANG (2010), “Financial Volatility Forecasting by Least Square Support
Vector Machine Based on GARCH, EGARCH and GJR Models: Evidence from ASEAN Stock
Markets”, International Journal of Economics and Finance, 2(1), 51-64.
[27] ÖĞÜT, H., DOĞANAY, M.M. ve R.AKTAŞ (2009), “Detecting Stock Price Manipulation in an
Emerging Market: The Case of Turkey”, Expert Systems with Applications, 36, 11944-11949.
[28] ÖZDEMİR, A.K., TOLUN, S. ve E.DEMİRCİ (2011), “Endeks Getirisi Yönünün İkili
Sınıflandırma Yöntemiyle Tahmin Edilmesi: İMKB-100 Endeksi Örneği”, Niğde Üniversitesi İİBF
Dergisi, 4(2), 45-59.
[29] PEREZ-CRUZ, F., AFONSO-RODRIGUEZ, J.A. ve J.GINER (2003), “Estimating GARCH
Models Using Support Vector Machines”, Quantitative Finance, 3, 1-10.
[30] POON, Ser-Huang, ve C.W.J. GRANGER (2003), “Forecasting Volatility in Financial Markets: A
Review”, Journal of Economic Literature, 41, 478-539.
[31] SCHÖLKOPF, B. ve A.J.SMOLA (2002), Learning With Kernels, MIT Press.
[32] SCHÖLKOPF, B., SMOLA, A.J., WILLIAMSON, R. ve P.L.BARTLETT (2000), “New Support
Vector Algorithms”, Neural Computation, 12, 1207-1245.
[33] SMOLA, A.J. ve B.SCHÖLKOPF (2004), “A Tutorial of Support Vector Regression”, Statistics and
Computing, 14, 199-222.
[34] TANG, Ling-Bing, SHENG, Huan-Ye ve Ling-Xiao TANG (2009), “GARCH Prediction Using
Spline Wavelet Support Vector Machine”, Neural Computing & Applications, 18, 913-917.
[35] TANG, Ling-Bing, TANG, Ling-Xiao ve Huan-Ye SHENG (2009), “Forecasting Volatility Based
On Wavelet Support Vector Machine”, Expert Systems with Applications, 36, 2901-2909.
[36] TAY, Francis E.H. ve Lijuan CAO (2001), “Application of Support Vector Machines in Financial
Time Series Forecasting”, The International Journal of Management Science, 29, 309-317.
[37] TİMOR, M., DİNÇER, H. ve Ş.EMİR (2012), “Performance Comparison of Artificial Neural
Networks and Support Vector Machines Models for the Stock Selection Problem: An Application on
the ISE-30 Index in Turkey”, African Journal of Business Management, 6(3), 1191-1198.
GÜRSOY, BALABAN | Volatility Forecasting in Stock Returns Using Support Vector Machines Based...
Kafkas University Journal of Economics and Administrative Sciences Faculty
KAU EASF Journal | Vol 5 * Issue 8 * Year 2014
186
[38] TRAFALIS, T.B. ve Hüseyin İNCE (2000), “Support Vector Machine for Regression and
Applications to Financial Forecasting”, in Proceedings of the IEEE-INNS-ENNS International Joint
Conference on Neural Networks, 348-353.
[39] Van GESTEL, T., SUYKENS, J.A.K., BAESTAENS, D.E., LAMBRECHTS, A., LANCKRIET,
G., VANDAELE, B., De MOOR, B. ve J.VANDEWALLE (2001), “Financial Time Series Prediction
Using Least Square Support Vector Machines within the Evidence Framework”, IEEE Transactions
on Neural Networks, 12(4), 809-821.
[40] VAPNIK, V. (1995), The Nature of Statistical Learning Theory, Second Edition, Springer Verlag.
[41] VAPNIK, V. (1998), Statistical Learning Theory, John Wiley & Sons.
[42] WEST, K.D. ve D. CHO (1995), “The Predictive Ability of Several Models of Exchange Rate
Volatility”, Journal of Econometrics, 69, 367-391.
[43] YÜMLÜ, S., GÜRGEN, F.S. ve N.OKAY (2005), “A Comparison of Global, Recurrent and
Smoothed-Piecewise Neural Models for Istanbul Stock Exchange Prediction”, Pattern Recognition
Letters, 26, 2093-2103.
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