期刊
INTERNATIONAL JOURNAL OF FORECASTING
卷 35, 期 2, 页码 601-615出版社
ELSEVIER
DOI: 10.1016/j.ijforecast.2018.11.008
关键词
Real-time forecasting; Discrete wavelet transform; Support vector regression; Trading rule
This paper combines the discrete wavelet transform with support vector regression for forecasting gold-price dynamics. The advantages of this approach are investigated using a relatively small set of economic and financial predictors. I measure model performance by differentiating between a statistically-motivated out-of-sample forecasting exercise and an economically-motivated trading strategy. Disentangling the predictors with respect to their time and frequency domains leads to improved forecasting performance. The results are robust compared to alternative forecasting approaches. My findings on the relative importances of such wavelet decompositions suggest that the influences of short-term and long-term trends are not stable over the full evaluation period. (C) 2019 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
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