A support vector machine based MSM model for financial short-term volatility forecasting
A support vector machine based MSM model for financial short-term volatility forecasting
复制标题
基于支持向量机的金融短期波动预测MSM模型
DOI：
10.1007/s00521-011-0742-z
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Xiaolong Wang
中科院分区：
文献类型：
--
作者：
Baohua Wang（博士生）;Hejiao Huang;Xiaolong Wang
金融时间序列预测因其长记忆性、厚尾性和波动持久性而成为一种挑战。多重分形过程是最近被提出的一种新的形式化方法。在文献中引入了迭代马尔可夫切换多重分形(MSM)模型。它能够捕捉金融时间序列的许多重要的风格化特征，包括对波动率、波动率聚集性和回报异常值的长期记忆。在长期预测中，该模型在样本内和样本外都比GARCH类型的模型提供了更好的表现。为了提高MSM的短期预测精度，提出了一种基于支持向量机的MSM方法，该方法利用MSM模型对波动率进行预测，利用支持向量机对新息进行建模。为了验证该方法的有效性，本文选取了中国A股市场的两个股指作为预测对象。与现有的一些最新模型相比，本文提出的方法具有更好的效果。结果表明，该模型为金融短期波动率预测提供了一种很有前景的替代方法。
Financial time series forecasting has become a challenge because of its long-memory, thick tails and volatility persistence. Multifractal process has recently been proposed as a new formalism for this problem. An iterative Markov-Switching Multifractal (MSM) model was introduced to the literature. It is able to capture many of the important stylized features of the financial time series, including long-memory in volatility, volatility clustering, and return outliers. The model delivers stronger performance both in- and out-of-sample than GARCH-type models in long-term forecasts. To enhance MSM’s short-term prediction accuracy, this paper proposes a support vector machine (SVM) based MSM approach which exploits MSM model to forecast volatility and SVM to model the innovations. To verify the effectiveness of the proposed approach, two stock indexes in the Chinese A-share market are chosen as the forecasting targets. Comparing with some existing state-of-the-art models, the proposed approach gives superior results. It indicates that the proposed model provides a promising alternative to financial short-term volatility prediction.
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DOI：
--
发表时间：
2011
期刊：
--
影响因子：
--
作者：
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通讯作者：
J. Bouchaud;M. Potters
DOI：
--
发表时间：
1996-12
期刊：
--
影响因子：
--
作者：
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通讯作者：
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影响因子：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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