Multi-Scale Volatility Feature Analysis and Prediction of Gold Price

Fenghua Wen, Xin Yang, Xu Gong and Kin Keung Lai
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Fenghua Wen: Business School of Central South University, Changsha 410081, P. R. China†Center for Computational Finance and Economic Agents, University of Essex, Colchester CO4 3SQ, UK‡Institute of Financial, WenZhou University, Wenzhou 325035, P. R. China
Xin Yang: Business School of Central South University, Changsha 410081, P. R. China
Xu Gong: Business School of Central South University, Changsha 410081, P. R. China
Kin Keung Lai: #xA7;International Business School, Shaanxi Normal University, Xian, P. R. China¶Department of Management Sciences, City University of Hong Kong, Kowloon, Hong Kong

International Journal of Information Technology & Decision Making (IJITDM), 2017, vol. 16, issue 01, 205-223

Abstract: Volatility of gold price is of great significance for avoiding the risk of gold investment. It is necessary to understand the effect of external events and intrinsic regularities to make accurate price predictions. This paper first compared EMD with CEEMD algorithm, and the results find that CEEMD algorithm performance is better than that of EMD in analysis gold price volatility. Then this paper uses the complementary ensemble empirical mode decomposition (CEEMD) to decompose the historical price of international gold into price components at different frequencies, and extracts a short-term fluctuation, a shock from significant events and a long-term price. In addition, this paper combines the Iterative cumulative sum of squares (ICSS) with Chow test to test the three event prices for structural breaks, and analyzes the effect of external events on volatility of gold price by comparing the external events with the test results for structural breaks. Finally, this paper constructs support vector machine (SVM) models and artificial neural network (ANN) on three series for prediction, and finds that the SVM performed better in gold price prediction in one-step-ahead and five-step-ahead, and when we combine the SVMs and ANNs with price components to make predictions, the error of the combined prediction is smaller than SVMs and ANNs with separate terms of series extracted.

Keywords: Gold price; CEEMD; ICSS algorithm; SVM; ANN (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (18)

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DOI: 10.1142/S0219622016500504

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