NONPARAMETRIC FINANCIAL VOLATILITY MODELLING BASED ON THE RELEVANCE VECTOR MACHINES

Phichhang Ou and Hengshan Wang

References

  1. [1] S. Haykin, Neural networks: a comprehensive foundation(Englewood Cliffs, NJ: Prentice Hall, 1999).
  2. [2] F. Perez-Cruz, J.A. Afonso-Rodriguez, and J. Giner, Estimat-ing GARCH models using support vector machines, Quantita-tive Finance, 3(3), 2003, 163–172.
  3. [3] V.V. Gavrishchaka and S.B. Ganguli, Volatility forecastingfrom multiscale and high-dimensional market data, Neurocom-puting, 55(1), 2003, 285–305.
  4. [4] L.B. Tang, H.Y. Sheng, and L.X. Tang, Forecasting volatilitybased on wavelet support vector machine, Expert Systems withApplications, 36(2), 2009, 2901–2909.
  5. [5] S. Chen, K. Jeong, and W. Hardle, Forecasting volatilitywith support vector machine-based GARCH model, Journalof Forecasting, 29(4), 2010, 406–433.
  6. [6] M.E. Tipping, Sparse Bayesian learning and the relevancevector machine, Journal of Machine Learning Research, 1,2001, 211–244.
  7. [7] S. Ghosh and P.P Mujumdar, Statistical downscaling of GCMsimulations to stream flow using relevance vector machine,Advances in Water Resources, 31(1), 2008, 132–146.
  8. [8] J. Flakea, T.K. Moona, M. McKeeb, and J.H. Gunthera, Appli-cation of the relevance vector machine to canal flow predictionin the Sevier River, Basin Agricultural Water Management,97(2), 2010, 208–214.
  9. [9] W. Caesarendra, A. Widodob, and B.S. Yang, Applicationof relevance vector machine and logistic regression for ma-chine degradation assessment, Mechanical Systems and SignalProcessing, 24(4), 2010, 1161–1171.
  10. [10] P.H. Ou and H.S. Wang, Predicting GARCH, EGARCH andGJR based volatility by the relevance vector machine: Evidencefrom the Hang Seng index, International Research Journal ofFinance and Economics, 39, 2010, 46–63.
  11. [11] P. B¨uhlmann and A.J. McNeil, An algorithm for nonparametricGARCH modelling, Computational Statistics & Data Analysis,40(4), 2002, 665–683.
  12. [12] R.C. Merton, An intertemporal capital asset pricing model,Econometrica, 41(5), 1973, 867–887.
  13. [13] A. Karatzoglou, A. Smola, A. Hornik, and A. Zeileis, AnS4 package for kernel methods in R, Journal of StatisticalSoftware, 11(9), 2004, 1–20.
  14. [14] K.D. West, Asymptotic inference about predictive ability,Econometrica, 64(5), 1996, 1067–1084.
  15. [15] A.J. Patton, Volatility forecast comparison using imperfectvolatility proxies, Journal of Econometrics, 160(1), 2011,246–256.
  16. [16] Y. Wei, Y.D. Wang, and D.S. Huang, Forecasting crudeoil market volatility: Further evidence using GARCH-classmodels, Energy Economics, 32(6), 2010, 1477–1484.
  17. [17] T.J. Brailsford and R.W. Faff, An evaluation of volatilityforecasting techniques, Journal of Banking and Finance, 20(3),1996, 419–438.
  18. [18] B.M.A. Awartani and V. Corradi, Predicting the volatilityof the S&P-500 stock index via GARCH models: The roleof asymmetries, International Journal of Forecasting, 21(1),2005, 167–183.
  19. [19] P. Sadorsky, Modeling and forecasting petroleum futuresvolatility, Energy Economics, 28(4), 2006, 467–488.
  20. [20] F.X. Diebold and R.S. Mariano, Comparing predictive accu-racy, Journal of Business and Economic Statistics, 13(3), 1995,253–263.

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