Forecasting volatility with support vector machine-based GARCH model

Journal of Forecasting

Volumn 29, Issue 4, 2010, Pages 406-433

  Chen, Shiyi ^a   Härdle, Wolfgang K ^b   Jeong, Kiho ^c  

^a FUDAN UNIVERSITY   (China)

^b HUMBOLDT UNIVERSITY   (Germany)

^c Kyungpook National University   (South Korea)

Author keywords

(Recurrent) support vector machine; Diebold Mariano test; GARCH model; Volatility forecasting

Indexed keywords

FORECASTING; NEURAL NETWORKS; SENSITIVITY ANALYSIS;

(RECURRENT) SUPPORT VECTOR MACHINE; DIEBOLD; DIEBOLD-MARIANO TEST; FINANCIAL FORECASTING; FORECASTING VOLATILITY; GARCH MODELLING; PERFORMANCE; RECURRENT SUPPORT VECTOR MACHINES; SUPPORT VECTORS MACHINE; VOLATILITY FORECASTING;

SUPPORT VECTOR MACHINES;

EID: 77953931277     PISSN: 02776693     EISSN: 1099131X     Source Type: Journal    
DOI: 10.1002/for.1134     Document Type: Article

References (116)

1. Akgiray V. 1989. Conditional heteroskedasticity in time series models of stock returns: evidence and forecasts. Journal of Business 62(1):55-80.
2. Andersen T, Bollerslev T. 1998. Answering the skeptics: yes, standard volatility models do provide accurate forecasts. International Economic Review 39:885-905.
3. Andersen T, Bollerslev T, Diebold F, Labys P. 2003. Modeling and forecasting realized volatility. Econometrica 71:579-625.
4. Andersson J. 2001. On the normal inverse gaussian stochastic volatility model. Journal of Business and Economic Statistics 19:44-54.
5. Andrews D. 1991. Heteroskedasticity and autocorrelation consistent covariance matrix estimation. Econometrica 59:817-858.
6. Ané T, Ureche-Rangau L, Gambet J, Bouverot J. 2008. Robust outlier detection for Asia-Pacific stock index returns. Journal of International Financial Markets, Institutions and Money 18(4):326-343.
7. Awartani B, Corradi V. 2005. Predicting the volatility of the S&P-500 stock index via GARCH models: the role of asymmetries. International Journal of Forecasting 21(1):167-183.
8. Balaban E. 2004. Comparative forecasting performance of symmetric and asymmetric conditional volatility models of an exchange rate. Economics Letters 83(1):99-105.
9. Bauwens L, Sebastien L, Jeroen R. 2006. Multivariate GARCH models: a survey. Journal of Applied Econometrics 21:79-109.
10. Becker R, Clements A, White S. 2007. Does implied volatility provide any information beyond that captured in model-based volatility forecasts? Journal of Banking and Finance 31(8):2535-2549.
11. Becker R, Clements A, McClelland A. 2009. The jump component of S&P 500 volatility and the VIX index. Journal of Banking and Finance 33(6):1033-1038.
12. Bekiros S, Georgoutsos D. 2008. Direction-of-change forecasting using a volatility-based recurrent neural network. Journal of Forecasting 27(5):407-417.
13. Bera A, Jarque C. 1981. An efficient large-sample test for normality of observations and regression residuals. Australian National University Working Papers in Econometrics, 40. Canberra.
14. Blair B, Poon S, H, Taylor S. 2001. Forecasting S&P 100 volatility: the incremental information content of implied volatilities and high-frequency index returns. Journal of Econometrics 105:5-26.
15. Bollerslev T. 1986. Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31:307-327.
16. Bollerslev T, Chou R, Kroner K. 1992. Arch modeling in finance: a review of the theory and empirical evidence. Journal of Econometrics 52:5-59.
17. Box G, Jenkins G, Reinsel G. 1994. Time Series Analysis: Forecasting and Control. Prentice Hall: Englewood Cliffs, NJ.
18. Brailsford T, Faff R. 1996. An evaluation of volatility forecasting techniques. Journal of Banking and Finance 20:419-438.
19. Brooks C. 1998. Predicting stock index volatility: can market volume help? Journal of Forecasting 17:59-80.
20. Brooks C. 2001. A double-threshold GARCH model for the french franc/deutschmark exchange rate. Journal of Forecasting 20:135-143.
21. Brooks C, Persand G. 2003. Volatility forecasting for risk management. Journal of Forecasting 22:1-22.
22. Cao C, Tsay R. 1992. Nonlinear time-series analysis of stock volatilities. Journal of Applied Econometrics 1:165-185.
23. Cao L, Tay F. 2001. Financial forecasting using support vector machines. Neural Computation and Application 10:184-192.
24. Chan K, Christie W, Schultz P. 1995. Market structure and the intraday pattern of bid-ask spreads for NASDAQ securities. Journal of Business 68(1):35-40.
25. Chen G, Choi Y, Zhou Y. 2008. Detections of changes in return by a wavelet smoother with conditional heteroscedastic volatility. Journal of Econometrics 143(2):227-262.
26. Chen S, Härdle W, Moro R. 2009. Modeling default risk with support vector machines. Quantitative Finance (accepted for publication).
27. Chib S, Nardari F, Shephard N. 2002. Markov chain Monte Carlo methods for generalized stochastic volatility models. Journal of Econometrics 108:281-316.
28. Chong C, Ahmad M, Abdullah M. 1999. Performance of GARCH models in forecasting stock market volatility. Journal of Forecasting 18:333-343.
29. Choudhry T, Wu H. 2008. Forecasting ability of GARCH vs kalman filter method: evidence from daily UK timevarying beta. Journal of Forecasting 27(8):670-689.
30. Clements M, Smith J. 1999. A Monte Carlo study of the forecasting performance of empirical SETAR models. Journal of Applied Econometrics 14:123-141.
31. Clements M, Smith J. 2001. Evaluating forecasts from SETAR models of exchange rates. Journal of International Money and Finance 20:133-148.
32. Corradi V, Swanson N. 2004. Some recent developments in predictive accuracy testing with nested models and (generic) nonlinear alternatives. International Journal of Forecasting 20(2):185-199.
33. Corradi V, Distaso W, Swanson N. 2009. Predictive density estimators for daily volatility based on the use of realized measures. Journal of Econometrics 150(2):119-138.
34. Cumby R, Figlewski S, Hasbrouck J. 1993. Forecasting volatility and correlations with EGARCH models. Journal of Derivatives Winter: 51-63.
35. Day T, Lewis C. 1992. Stock market volatility and the information content of stock index options. Journal of Econometrics 52:267-287.
36. Deng N, Tian Y. 2004. New Methods in Data Mining: Support Vector Machine. Science Press: Beijing.
37. Diebold F, Mariano R. 1995. Comparing predictive accuracy. Journal of Business and Economic Statistics 13:253-265.
38. Dimson E, Marsh P. 1990. Volatility forecasting without data-snooping. Journal of Banking and Finance 44:399-421.
39. Donaldson R, Kamstra M. 1997. An artificial neural network-GARCH model for international stock return volatility. Journal of Empirical Finance 4:17-46.
40. Dotsis G, Psychoyios D, Skiadopoulos G. 2007. An empirical comparison of continuous-time models of implied volatility indices. Journal of Banking and Finance 31:3584-3603.
41. Dunis C, Huang X. 2002. Forecasting and trading currency volatility: an application of recurrent neural regression and model combination. Journal of Forecasting 21:317-354.
42. Enders W. 2004. Applied Econometric Time Series (2nd edn). Wiley: New York.
43. Engle R. 1982. Autoregressive conditional heteroskedasticity with estimates of the variance of UK inflation. Econometrica 50:957-1008.
44. Engle R, Hong C-H, Kane A, Noh J. 1993. Advances in Futures and Options Research, Vol. 6. JAI Press: Greenwich, CT; 393-415.
45. Feng Y, McNeil A. 2008. Modelling of scale change, periodicity and conditional heteroskedasticity in return volatility. Economic Modelling 25(5):850-867.
46. Ferland R, Lalancette S. 2006. Dynamics of realized volatilities and correlations: an empirical study. Journal of Banking and Finance 30(7):2109-2130.
47. Fernandez-Rodriguez F, Gonzalez-Martel C, Sosvilla-Rivero S. 2000. On the profitability of technical trading rules based on artificial neural networks: evidence from the Madrid stock market. Economics Letters 69(1):89-94.
48. Figlewski S. 1997. Forecasting volatility. Financial Markets, Institutions and Instruments 6:1-88.
49. Fleming J. 1998. The quality of market volatility forecasts implied by S&P 100 index option prices. Journal of Empirical Finance 5:317-345.
50. Franke J, Neumann M, Stockis J. 2004. Bootstrapping nonparametric estimators of the volatility function. Journal of Econometrics 118:189-218.
51. Franses P, Dijk DV. 1996. Forecasting stock market volatility using (non-linear) GARCH models. Journal of Forecasting 15(3):229-235.
52. Franses P, Dijk DV. 2000. Nonlinear Time Series Models in Empirical Finance. Cambridge University Press: Cambridge, UK.
53. Franses P, McAleer M. 2002. Financial volatility: an introduction. Journal of Applied Econometrics 17:419-424.
54. Galbraith J, Kisinbay T. 2005. Content horizons for conditional variance forecasts. International Journal of Forecasting 21:249-260.
55. Gerlach R, Tuyl F. 2006. MCMC methods for comparing stochastic volatility and GARCH models. International Journal of Forecasting 22(1):91-107.
56. Ghysels E, Harvey A, Rebault E. 1996. Handbook of Statistics: Statistical Methods in Finance, Vol. 14. Elsevier Science: Amsterdam; 119-191.
57. Ghysels E, Santa-Clara P, Valkanov R. 2006. Predicting volatility: how to get most out of returns data sampled at different frequencies. Journal of Econometrics 131:59-95.
58. Glosten L, Jagannathan R, Runkle D. 1992. On the relation between the expected value and the volatility of nominal excess return on stocks. Journal of Finance 46:1779-1801.
59. Gokcan S. 2000. Forecasting volatility of emerging stock markets: linear versus non-linear GARCH models. Journal of Forecasting 19(6):499-504.
60. Gospodinov N, Gavala A, Jiang D. 2006. Forecasting volatility. Journal of Forecasting 25(6):381-340.
61. Gray S. 1996. Modeling the conditional distribution of interest rates as a regime-switching process. Journal of Financial Economics 42:27-62.
62. Groen J, Kapetanios G, Price S. 2009. A real time evaluation of bank of England forecasts of inflation and growth. International Journal of Forecasting 25(1):74-80.
63. Gunn S. 1998. Support vector machines for classification and regression. Isis-1-98. Technical report, Image Speech and Intelligent Systems Group, University of Southampton, UK.
64. Hamid S, Iqbal Z. 2004. Using neural networks for forecasting volatility of S&P 500 index futures prices. Journal of Business Research 57:1116-1125.
65. Hamilton J. 1989. A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57:357-384.
66. Hamilton J. 1997. Time Series Analysis. Princeton University Press: Princeton, NJ.
67. Härdle, W, Moro R, Schäfer D. 2005. Statistical Tools for Finance and Insurance. Springer: Berlin.
68. Härdle W, Moro R, Schäfer D. 2007. Handbook for Data Visualization. Springer: Berlin.
69. Haykin S. 1999. Neural Networks: A Comprehensive Foundations (2nd edn). Prentice Hall: Englewood Chiffs, NJ.
70. Heynen R, Kat H. 1994. Volatility prediction: a comparison of stochastic volatility, GARCH (1, 1) and EGARCH (1, 1) models. Journal of Derivatives 50-65.
71. Hu M, Tsoukalas C, 1999. Combining conditional volatility forecasts using neural networks: an application to the EMS exchange rates. Journal of International Financial Markets, Institution and Money 9:407-422.
72. Jorion P. 1995. Predicting volatility in the foreign exchange market. Journal of Finance 50:507-528.
73. Jorion P. 1996. The Microstructure of Foreign Exchange Markets. Chicago University Press: Chicago, IL.
74. Klaassen F. 2002. Improving GARCH volatility forecasts with regime-switching GARCH. Empirical Economics 27:363-394.
75. Koopman S, Jungbacker B, Hol E. 2005. Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements. Journal of Empirical Finance 12:445-475.
76. Kuan C, Liu T. 1995. Forecasting exchange rates using feedforward and recurrent neural networks. Journal of Applied Econometrics 10:347-364.
77. Lamoureux C, Lastrapes W. 1993. Forecasting stock-return variance: understanding of stochastic implied volatilities. Review of Financial Studies 6:293-326.
78. Lehar A, Scheicher M, Schittenkopf C. 2002. GARCH vs. stochastic volatility: option pricing and risk management. Journal of Banking and Finance 26:323-345.
79. Li W, Ling S, McAleer M. 2002. Recent theoretical results for time series models with GARCH errors. Journal of Economic Surveys 16:245-269.
80. Lux T, Schornstein S. 2005. Genetic learning as an explanation of stylized facts of foreign exchange markets. Journal of Mathematical Economics 41:169-196.
81. Marcucci J. 2005. Studies in Nonlinear Dynamics and Econometrics, Vol. 9. Berkeley Electronic Press: Berkeley, CA; 1145.
82. McMillan D, Speight A. 2004. Daily volatility forecasts: reassessing the performance of GARCH models. Journal of Forecasting 23(6):449-460.
83. McMillan D, Speight A, Gwilym O. 2000. Forecasting UK stock market volatility: a comparative analysis of alternate methods. Applied Financial Economics 10:435-448.
84. Meddahi N. 2003. ARMA representations of integrated and realized variances. Econometrics Journal 6:334-355.
85. Moosa I. 2000. Exchange Rate Forecasting: Techniques and Applications. Macmillan Press: London.
86. Neely C. 2009. Forecasting foreign exchange volatility: why is implied volatility biased and inefficient? And does it matter? Journal of International Financial Markets, Institutions and Money 19(1):188-205.
87. Nelson D. 1991. Conditional heteroskedasticity in asset returns: a new approach. Econometrica 59:347-370.
88. Newey W, West K. 1987. A simple positive, semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica 55(3):703-708.
89. Niemira M, Klein P. 1994. Forecasting Financial and Economic Cycles. Wiley: New York.
90. Pagan A, Schwert G. 1990. Alternative models for conditional stock volatility. Journal of Econometrics 45:267-290.
91. Pantelidaki S, Bunn D. 2005. Development of a multifunctional sales response model with the diagnostic aid of artificial neural networks. Journal of Forecasting 24:505-521.
92. Park B. 2002. An outlier robust GARCH model and forecasting volatility of exchange rate returns. Journal of Forecasting 21(5):381-393.
93. Pérez-Cruz F, Afonso-Rodriguez J, Giner J. 2003. Estimating GARCH models using SVM. Quantitative Finance 3:163-172.
94. Pong S, Shackleton M, Taylor S, Xu X. 2004. Forecasting currency volatility: a comparison of implied volatilities and AR (FI) MA models. Journal of Banking and Finance 28:2541-2563.
95. Poon S-H, Granger C. 2003. Forecasting volatility in financial markets: a review. Journal of Economic Literature 41:478-539.
96. Preminger A, Franck R. 2007. Forecasting exchange rates: a robust regression approach. International Journal of Forecasting 23(1):71-84.
97. Qi M, Wu Y. 2003. Nonlinear prediction of exchange rates with monetary fundamentals. Journal of Empirical Finance 10:623-640.
98. Renò R. 2006. Nonparametric estimation of stochastic volatility models. Economics Letters 90(3):390-395.
99. Rosenow B. 2008. Determining the optimal dimensionality of multivariate volatility models with tools from random matrix theory. Journal of Economic Dynamics and Control 32(1):279-302.
100. Schittenkopf C, Dorffner G, Dockner E. 2000. Forecasting time-dependent conditional densities: a semi-nonparametric neural network approach. Journal of Forecasting 19:355-374.
101. Scholkopf B, Smola A. 2001. Learning with Kernels. MIT Press: Cambridge, MA.
102. Sentana E. 1995. Quadratic ARCH models. Review of Economic Studies 62:639-661.
103. Suykens J, Vandewalle J. 2000. Recurrent least squares support vector machines. IEEE Transactions on Circuits and Systems I 47(7):1109-1114.
104. Tay F, Cao L. 2001. Application of support vector machines in financial time series forecasting. Omega 29:309-317.
105. Taylor J. 1999. Evaluating volatility and interval forecasts. Journal of Forecasting 18:111-128.
106. Taylor J. 2000. A quantile regression neural network approach to estimating the conditional density of multiperiod returns. Journal of Forecasting 19:299-311.
107. Taylor N. 2008. Can idiosyncratic volatility help forecast stock market volatility? International Journal of Forecasting 24(3):462-479.
108. Taylor S. 1986. Modelling Financial Time Series. Wiley: Chichester.
109. Tse Y, Tung S. 1992. Forecasting volatility in the singapore stock market. Asia Pacific Journal of Management 9:1-13.
110. Tseng C, Cheng S, Wang Y, Peng J. 2008. Artificial neural network model of the hybrid EGARCH volatility of the taiwan stock index option prices. Physica A: Statistical Mechanics and its Applications 387(13):3192-3200.
111. Vapnik V. 1995. The Nature of Statistical Learning Theory. Springer: New York.
112. Vapnik V. 1997. Statistical Learning Theory. Wiley: New York.
113. West K. 1996. Asymptotic inference about predictive ability. Econometrica 64:1067-1084.
114. West K, Cho D. 1995. The predictive ability of several models of exchange rate volatility. Journal of Econometrics 69:367-391.
115. Wong W, Tu A. 2009. Market imperfections and the information content of implied and realized volatility. Pacific Basin Finance Journal 17(1):58-79.
116. Zhang X, King M. 2005. Influence diagnostics in generalized autoregressive conditional heteroscedasticity processes. Journal of Business and Economic Statistics 23:118-129.