Volatility forecasting from multiscale and high-dimensional market data

Abstract Advantages and limitations of the existing volatility models for forecasting foreign-exchange and stock market volatility from multiscale and high-dimensional data have been identified. Support vector machines (SVM) have been proposed as a complimentary volatility model that is capable of effectively extracting information from multiscale and high-dimensional market data. SVM-based models can handle both long memory and multiscale effects of inhomogeneous markets without restrictive assumptions and approximations required by other models. Preliminary results with foreign-exchange data suggest that SVM can effectively work with high-dimensional inputs to account for volatility long-memory and multiscale effects. Advantages of the SVM-based models are expected to be of the utmost importance in the emerging field of high-frequency finance and in multivariate models for portfolio risk management.

[1]  Jaume Masoliver,et al.  A CORRELATED STOCHASTIC VOLATILITY MODEL MEASURING LEVERAGE AND OTHER STYLIZED FACTS , 2001 .

[2]  William Nick Street,et al.  Breast Cancer Diagnosis and Prognosis Via Linear Programming , 1995, Oper. Res..

[3]  E. Dockner,et al.  Volatility Prediction with Mixture Density Networks , 1998 .

[4]  Joachim Diederich,et al.  The truth will come to light: directions and challenges in extracting the knowledge embedded within trained artificial neural networks , 1998, IEEE Trans. Neural Networks.

[5]  F. Schmitt,et al.  Multifractal analysis of foreign exchange data , 1999 .

[6]  Chih-Jen Lin,et al.  The analysis of decomposition methods for support vector machines , 2000, IEEE Trans. Neural Networks Learn. Syst..

[7]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[8]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[9]  P. Vannerem,et al.  Classifying LEP data with support vector algorithms. , 1999 .

[10]  J. Bouchaud,et al.  Leverage effect in financial markets: the retarded volatility model. , 2001, Physical review letters.

[11]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[12]  R. Donaldson,et al.  An artificial neural network-GARCH model for international stock return volatility , 1997 .

[13]  D. Sornette,et al.  ”Direct” causal cascade in the stock market , 1998 .

[14]  Georg Dorffner,et al.  Fat tails and non-linearity in volatility models: what is more important? , 1999, Proceedings of the IEEE/IAFE 1999 Conference on Computational Intelligence for Financial Engineering (CIFEr) (IEEE Cat. No.99TH8408).

[15]  J. Peinke,et al.  Turbulent cascades in foreign exchange markets , 1996, Nature.

[16]  Valeriy V. Gavrishchaka,et al.  Support vector machine as an efficient tool for high‐dimensional data processing: Application to substorm forecasting , 2001 .

[17]  B. Mandelbrot,et al.  Fractional Brownian Motions, Fractional Noises and Applications , 1968 .

[18]  Ruey S. Tsay,et al.  Analysis of Financial Time Series , 2005 .

[19]  F. Schmitt,et al.  MULTIFRACTAL FLUCTUATIONS IN FINANCE , 2000, cond-mat/0102369.

[20]  Laurent E. Calvet,et al.  Multifractality of Deutschemark / Us Dollar Exchange Rates , 1997 .

[21]  Federico Girosi,et al.  Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[22]  Andrew J. Patton,et al.  What good is a volatility model? , 2001 .

[23]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[24]  E. Bacry,et al.  Modelling fluctuations of financial time series: from cascade process to stochastic volatility model , 2000, cond-mat/0005400.

[25]  Massimiliano Pontil,et al.  On the Noise Model of Support Vector Machines Regression , 2000, ALT.

[26]  Massimiliano Pontil,et al.  Support Vector Machines for 3D Object Recognition , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  J. Zakoian.  Threshold heteroskedastic models , 1994 .

[28]  R. Engle.  Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[29]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[30]  Jose C. Principe,et al.  Neural and adaptive systems , 2000 .

[31]  Valeriy V. Gavrishchaka,et al.  Optimization of the neural-network geomagnetic model for forecasting large-amplitude substorm events , 2001 .

[32]  J. Bouchaud,et al.  Leverage Effect in Financial Markets , 2001 .

[33]  B. Mandelbrot.  A Multifractal Walk down Wall Street , 1999 .

[34]  R. Gencay,et al.  An Introduc-tion to High-Frequency Finance , 2001 .

[35]  Johan A. K. Suykens,et al.  Financial time series prediction using least squares support vector machines within the evidence framework , 2001, IEEE Trans. Neural Networks.

[36]  Chris Aldrich,et al.  ANN-DT: an algorithm for extraction of decision trees from artificial neural networks , 1999, IEEE Trans. Neural Networks.

[37]  Rosario N. Mantegna,et al.  Book Review: An Introduction to Econophysics, Correlations, and Complexity in Finance, N. Rosario, H. Mantegna, and H. E. Stanley, Cambridge University Press, Cambridge, 2000. , 2000 .

[38]  Thorsten Joachims,et al.  Text categorization with support vector machines , 1999 .

[39]  J. Bouchaud,et al.  Theory of financial risks : from statistical physics to risk management , 2000 .

[40]  B. TickleA.,et al.  The truth will come to light , 1998 .

[41]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[42]  R. Baillie,et al.  Fractionally integrated generalized autoregressive conditional heteroskedasticity , 1996 .