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Support vector machine as an efficient framework for stock market volatility forecasting. (English) Zbl 1142.91718
Summary: Advantages and limitations of the existing models for practical forecasting of stock market volatility have been identified. Support vector machine (SVM) have been proposed as a complimentary volatility model that is capable to extract information from multiscale and high-dimensional market data. Presented results for SP500 index suggest that SVM can efficiently work with high-dimensional inputs to account for volatility long-memory and multiscale effects and is often superior to the main-stream volatility models. SVM-based framework for volatility forecasting is expected to be important in the development of the novel strategies for volatility trading, advanced risk management systems, and other applications dealing with multi-scale and high-dimensional market data.
MSC:
91B84Economic time series analysis
62M10Time series, auto-correlation, regression, etc. (statistics)
62P05Applications of statistics to actuarial sciences and financial mathematics
References:
[1]Andersen TG, Bollerslev T, Diebold FX, Labys P (2000) Exchange rate returns standardized by realised volatility are (nearly) Gaussian. NBER Working Paper No: 7488
[2]Arneodo A, Muzy JF, Sornette D (1998) Direct causal cascade in the stock market. Eur Phys J B 2:277 · doi:10.1007/s100510050250
[3]Baillie RT, Bollerslev T, Mikkelsen HO (1996) Fractionally integrated generalized autoregressive conditional heteroskedasticity. J Econom 74:3 · Zbl 0865.62085 · doi:10.1016/S0304-4076(95)01749-6
[4]Bishop CM (1995) Neural networks for pattern recognition. Clarendon Press, Oxford
[5]Bollerslev T (1986) Generalized autoregressive conditional heteroskedasticity. J Econom 31:307 · Zbl 0616.62119 · doi:10.1016/0304-4076(86)90063-1
[6]Bouchaud JP, Potters M (1999) Theory of financial risk: from statistical physics to risk management. Cambridge University Press, Cambridge
[7]Bouchaud JP, Matacz A, Potters M (2001) Leverage effect in financial markets: The retarded volatility model. Phys Rev Lett 87:228701 · doi:10.1103/PhysRevLett.87.228701
[8]Brown M, Grundy W, Lin D, Cristianini N, Sugnet C, Ares M Jr, Haussler D (1999) Support vector machine classification of microarray gene expression data. University of California, Santa Cruz, technical report UCSC-CRL-99-09
[9]Chang CC, Hsu CW, Lin CJ (2000) The analysis of decomposition methods for support vector machines. IEEE Trans Neural Netw 11:1003 · doi:10.1109/72.857780
[10]Chapelle O, Vapnik V (2001) Choosing multiple parameters for support vector machines. Adv Neural Inf Process Syst 03(5)
[11]Chen YT (2002) On the robustness of Ljung–Box and McLeod–Li Q tests: a simulation study. Econ Bull 3:1
[12]Cristianini N, Shawe-Taylor J (2000) Introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge
[13]Dacorogna MM, Gencay R, Muller U, Olsen RB, Pictet OV (2001) An introduction to high-frequency finance. Academic Press, San Diego
[14]Ding Z, Granger CW, Engle RF (1993) A long memory property of stock market returns and a new model. J Empir Finance 1:83 · doi:10.1016/0927-5398(93)90006-D
[15]Donaldson RG, Kamstra M (1997) An artificial neural network-GARCH model for international stock return volatility. J Empir Finance 4:17 · doi:10.1016/S0927-5398(96)00011-4
[16]Edelman D (2001) Enforced-denial support vector machines for noisy data with applications to financial time series forecasting. In: Proceedings of the international conference on statistics, combinatorics and related areas and the 8th international conference of forum for interdisciplinary mathematics, school of mathematics and applied statistics, University of Wollongong, Wollongong, NSW 2522, Australia
[17]Engle RF (1982) Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. inflation. Econometrica 50:987 · Zbl 0491.62099 · doi:10.2307/1912773
[18]Engle RF, Patton AJ (2001) What good is a volatility model. Quant Finance 1:237 · doi:10.1088/1469-7688/1/2/305
[19]Fan A, Hong D, Palanaswami M, Tan C (1999) A support vector machine approach to bankruptcy prediction: a case study. In: Proceedings of the 6th international conference on computational finance, New York
[20]Gavrishchaka VV, Ganguli SB (2001) Support vector machine as an efficient tool for high-dimensional data processing: application to substorm forecasting. J Geophys Res 106:29911 · doi:10.1029/2001JA900118
[21]Gavrishchaka VV, Ganguli SB (2003) Volatility forecasting from multiscale and high-dimensional market data. Neurocomputing 55:285 · Zbl 02060378 · doi:10.1016/S0925-2312(03)00381-3
[22]Joachims T (1998) Text categorization with support vector machines. In: Nedellec C, Rouveirol C (eds) Proceedings of the 10th European conference on machine learning (ECML). vol 1398, pp 137–142
[23]Mangasarian OL, Street WN, Wolberg WH (1995) Breast cancer diagnosis and prognosis via linear programming. Oper Res 43(4):570 · Zbl 0857.90073 · doi:10.1287/opre.43.4.570
[24]Mantegna RN, Stanley HE (2000) An introduction to econophysics: correlation and complexity in finance. Cambridge University Press, Cambridge
[25]Masoliver J, Perello J (2001) A correlated stochastic volatility model measuring leverage and other stylized facts. cond-mat/0111334 v1
[26]Nelson DB (1991) Conditional heteroskedasticity in asset returns: a new approach. Econometrica 59:347 · Zbl 0722.62069 · doi:10.2307/2938260
[27]Osuna E, Freund R, Girosi F (1997) Training support vector machines: an application to face detection. In: Proceedings of computer vision and pattern recognition, pp 130
[28]Pontil M, Verri A (1998) Object recognition with support vector machines. IEEE Trans PAMI 20:637
[29]Pontil M, Mukherjee S, Girosi F (1998) On the noise model of support vector machine regression. CBCL Paper 168, AI Memo 1651, Massachusetts Institute of Technology, Cambridge
[30]Schittenkopf C, Dorffner G, Dockner EJ (1998) Volatility prediction with mixture density networks. In: Niklasson L, Boden M, and Ziemke T, ICANN ’98 – proceedings of the 8th international conference on artificial neural networks, Springer, Berlin Heidelberg New York, pp 929
[31]Tsay RS (2002) Analysis of financial time series. Wiley, New York
[32]Van Gestel T, Suykens J, Baestaens D, Lambrechts A, Lanckriet G, Vandaele B, De Moor B, Vandewalle J (2001) Financial time series prediction using least squares support vector machines within the evidence framework. IEEE Tran Neural Netw 12:809 (Special Issue on Neural Networks in Financial Engineering) · doi:10.1109/72.935093
[33]Vannerem P, Mller KR, Schlkopf B, Smola A, Sldner-Rembold S (1999) Classifying LEP data with support vector algorithms. hep-ex/9905027
[34]Vapnik V (1995) The nature of statistical learning theory. Springer Verlag, Berlin Heidelberg New York
[35]Vapnik V (1998) Statistical learning theory. Wiley, New York
[36]Zakoian JM (1994) Threshold heteroskedastic models. J Econ Dyn Control 18:931 · Zbl 0875.90197 · doi:10.1016/0165-1889(94)90039-6