Empirical research of price discovery for gold futures based on compound model combing wavelet frame with support vector regression

Authors:
Wensheng Dai;Chi-Jie Lu;Tingjen Chang
Affiliations:
China Financial policy Research Centre, Financial School, Renmin University of China, Beijing, China;Department of Industrial Engineering and Management, ChingYun University;Statistics School, Central University of Finance and Economics China, Beijing, China
Venue:
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part II
Year:
2010

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Abstract

In theory, a gold futures possesses function of price discovery. However, futures including information must be disclosed by some effective way. This paper proposes a forecasting model which combines wavelet frame with Support vector regression (SVR). Wavelet frame is first used to decompose the series of gold futures price into sub-series with different scales, the SVR then uses the sub-series to build the forecasting model. Empirical research shows that the gold futures has the function of price discovery, and the two steps model is a good tool for making the price information clear and forecasting spot price. further research can try different basis function or other methods of disclosing information.