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.
机构:
Allianz Global Investors Frankfurt, Global R&D Multi Asset, Frankfurt, GermanyAllianz Global Investors Frankfurt, Global R&D Multi Asset, Frankfurt, Germany