Volatility forecasting from multiscale and high-dimensional market data

被引:34
|
作者
Gavrishchaka, VV [1 ]
Ganguli, SB [1 ]
机构
[1] Sci Applicat Int Corp, Mclean, VA 22102 USA
关键词
volatility models; support vector machines; high-frequency finance;
D O I
10.1016/S0925-2312(03)00381-3
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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. (C) 2003 Elsevier B.V. All rights reserved.
引用
收藏
页码:285 / 305
页数:21
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