Volatility forecasting from multiscale and high-dimensional market data

Neurocomputing

Volumn 55, Issue 1-2, 2003, Pages 285-305

  Gavrishchaka, Valeriy V ^a   Ganguli, Supriya B ^a  

^a SCIENCE APPLICATIONS INTERNATIONAL CORPORATION   (United States)

Author keywords

High frequency finance; Support vector machines; Volatility models

Indexed keywords

DATA REDUCTION; FINANCE; MATHEMATICAL MODELS; RISK MANAGEMENT;

SUPPORT VECTOR MACHINES (SVM);

NEURAL NETWORKS;

ANALYTIC METHOD; ARTICLE; COMMERCIAL PHENOMENA; CONTROLLED STUDY; CORRELATION ANALYSIS; FINANCIAL MANAGEMENT; INFORMATION SYSTEM; LINEAR SYSTEM; MARKETING; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; MULTIVARIATE ANALYSIS; PREDICTION AND FORECASTING; PRIORITY JOURNAL; RISK MANAGEMENT; STOCHASTIC MODEL; SYSTEM ANALYSIS;

EID: 0242288805     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0925-2312(03)00381-3     Document Type: Article

References (45)

1. T.G. Andersen, T. Bollerslev, F.X. Diebold, P. Labys, Exchange rate returns standardized by realised volatility are (nearly) Gaussian, NBER Working Paper No: 7488.
2. A. Arneodo, J.-F. Muzy, D. Sornette, Direct causal cascade in the stock market, Eur. Phys. J. B 2 (1998) 277.
3. R.T. Baillie, T. Bollerslev, H.-O. Mikkelsen, Fractionally integrated generalized autoregressive conditional heteroskedasticity, J. Econometrics 74 (1996) 3.
4. C.M. Bishop, Neural Networks for Pattern Recognition, Clarendon Press, Oxford, 1995.
5. T. Bollerslev, Generalized autoregressive conditional heteroskedasticity, J. Econometrics 31 (1986) 307.
6. J.-P. Bouchaud, A. Matacz, M. Potters, Leverage effect in financial markets: the retarded volatility model, Phys. Rev. Lett. 87 (2001) 228701.
7. J.-P. Bouchaud, M. Potters, Theory of Financial Risk: From Statistical Physics to Risk Management, Cambridge University Press, Cambridge, 1999.
8. M. Brown, W. Grundy, D. Lin, N. Cristianini, C. Sugnet, M. Ares Jr., D. Haussler, Support vector machine classification of microarray gene expression data, Technical report UCSC-CRL-99-09, University of California, Santa Cruz.
9. C.-C. Chang, C.-W. Hsu, C.-J. Lin, The analysis of decomposition methods for support vector machines, IEEE Trans. Neural Networks 11 (2000) 1003.
10. N. Cristianini, J. Shawe-Taylor, Introduction to Support Vector Machines and other Kernel-Based Learning Methods, Cambridge University Press, Cambridge, 2000.
11. M.M. Dacorogna, R. Gencay, U. Muller, R.B. Olsen, O.V. Pictet, An Introduction to High-Frequency Finance, Academic Press, San Diego, 2001.
12. R.G. Donaldson, M. Kamstra, An artificial neural network-GARCH model for international stock return volatility, J. Empirical Finance 4 (1997) 17.
13. D. Edelman, Enforced-denial support vector machines for noisy data with applications of financial time series forecasting, in: Proceedings of the International Conference on Statistics, Combinatorics and Related Areas and the Eighth International Conference of Forum for Interdisciplinary Mathematics, School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, NSW 2522, Australia, 19-21 December 2001.
14. R.F. Engle, Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. inflation, Econometrica 50 (1982) 987.
15. R.F. Engle, A.J. Patton, What good is a volatility model? Quantitative Finance 1 (2001) 237.
16. A. Fan, D. Hong, M. Palanaswami, C. Tan, A support vector machine approach to bankruptcy prediction: a case study, The 6th International Conference on Computational Finance, New York, January 1999.
17. A. Fisher, L. Calvet, B.B. Mandelbrot, Multifractality of the DM/US dollar exchange rate, Cowles Foundation Discussion Paper, 1997.
18. U. Frish, Turbulence, Cambridge University Press, Cambridge, 1995.
19. V.V. Gavrishchaka, S.B. Ganguli, Optimization of the neutral-network geomagnetic model for forecasting large-amplitude substorm events, J. Geophys. Res. 106 (2001) 6247.
20. V.V. Gavrishchaka, S.B. Ganguli, Support vector machine as an efficient tool for high-dimensional data processing: application to substorm forecasting, J. Geophys. Res. 106 (2001) 29911.
21. S. Ghashghaie, W. Breymann, J. Peinke, P. Talkner, Y. Dodge, Turbulent cascades in foreign exchange markets, Nature 381 (1996) 767.
22. T. Joachims, Text categorization with support vector machines, in: Proceedings of European Conference on Machine Learning (ECML), 1998.
23. N. Lordkipanidze, Modeling and estimation of long memory in stochastic volatility: application to options on futures contracts, Working Paper, Cornell University, 2001.
24. B.B. Mandelbrot, Sci. Am. 280 (1999) 70.
25. B.B. Mandelbrot, J.W. Van Ness, Fractional brownian motions, fractional noises and applications, SIAM Rev. 10 (1968) 422.
26. O.L. Mangasarian, W.N. Street, W.H. Wolberg, Breast cancer diagnosis and prognosis via linear programming, Oper. Res. 43 (4) (1995) 570.
27. R.N. Mantegna, H.E. Stanley, An Introduction to Econophysics: Correlation and Complexity in Finance, Cambridge University Press, Cambridge, 2000.
28. J. Masoliver, J. Perello, A correlated stochastic volatility model measuring leverage and other stylized facts, cond-mat/0111334 v1, November 2001.
29. J.F. Muzy, J. Delour, E. Bacry, Modelling fluctuations of financial time series: from cascade process to stochastic volatility model, cond-mat/0005400, May 2000.
30. E. Osuna, R. Freund, F. Girosi, Training support vector machines: an application to face detection, in: Proceedings of Computer Vision and Pattern Recognition, 1997, p. 130.
31. M. Pontil, S. Mukherjee, F. Girosi, On the Noise Model of Support Vector Machine Regression, CBCL Paper 168, AI Memo 1651, Massachusetts Institute of Technology, Cambridge, MA, 1998.
32. M. Pontil, A. Verri, Object recognition with support vector machines, IEEE Trans. Pattern Anal. Machine Intell. 20 (1998) 637.
33. J.C. Principe, N.R. Euliano, W.C. Lefebvre, Neural and adaptive systems, Wiley, New York, 2000.
34. C. Schittenkopf, G. Dorffner, E.J. Dockner, Volatility prediction with mixture density networks, in: L. Niklasson, M. Boden, T. Ziemke (Eds.), ICANN '98 - Proceedings of the 8th International Conference on Artificial Neural Networks, Springer, Berlin, 1998, p. 929.
35. C. Schittenkopf, G. Dorffner, E.J. Dockner, Fat tails and non-linearity in volatility models: what is more important?, in: Proc. IEEE/IAFE 1999 Conference on Computational Intelligence for Financial Engineering (CIFEr), NY, USA, 1999, p. 259.
36. F. Schmitt, D. Schertzer, S. Lovejoy, Multifractal analysis of foreign exchange data, Appl. Stochastic Models Data Anal. 15 (1999) 29.
37. F. Schmitt, D. Schertzer, S. Lovejoy, Multifractal fluctuations in finance, Int. J. Theor. Appl. Finance 3 (2000) 361.
38. G.P.J. Schmitz, C. Aldrich, F.S. Gouws, ANN-DT: an algorithm for extraction of decision trees from artificial neural networks, IEEE Trans. Neural Networks 10 (1999) 1392.
39. A.B. Tickle, R. Andrews, M. Golea, J. Diederich, The truth will come to light: directions and challenges in extracting the knowledge embedded within trained artificial neural networks, IEEE Trans. Neural Networks 9 (1998) 1057.
40. R.S. Tsay, Analysis of Financial Time Series, Wiley, New York, 2002.
41. T. Van Gestel, J. Suykens, D. Baestaens, A. Lambrechts, G. Lanckriet, B. Vandaele, B. De Moor, J. Vandewalle, Financial time series prediction using least squares support vector machines within the evidence framework, IEEE Trans. Neural Networks (Special Issue on Neural Networks in Financial Eng.) 12 (2001) 809.
42. P. Vannerem, K.-R. Mller, B. Schlkopf, A. Smola, S. Sldner-Rembold, Classifying LEP data with support vector algorithms, hep-ex/9905027, 1999.
43. V. Vapnik, The Nature of Statistical Learning Theory, Springer, Berlin, 1995.
44. V. Vapnik, Statistical Learning Theory, Wiley, New York, 1998.
45. J.-M. Zakoian, Threshold heteroskedastic models, J. Econ. Dynamics Control 18 (1994) 931.