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首页> 外文期刊>Chaos, Solitons and Fractals: Applications in Science and Engineering: An Interdisciplinary Journal of Nonlinear Science >Volatility forecasting for interbank offered rate using grey extreme learning machine: The case of China
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Volatility forecasting for interbank offered rate using grey extreme learning machine: The case of China

机译：灰色极端学习机对银行间同业拆借利率的波动预测：以中国为例

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摘要

Interbank Offered rate is the only direct market rate in China's currency market. Volatility forecasting of China Interbank Offered Rate (IBOR) has a very important theoretical and practical significance for financial asset pricing and financial risk measure or management. However, IBOR is a dynamics and non-steady time series whose developmental changes have stronger random fluctuation, so it is difficult to forecast the volatility of IBOR. This paper offers a hybrid algorithm using grey model and extreme learning machine (ELM) to forecast volatility of IBOR. The proposed algorithm is composed of three phases. In the first, grey model is used to deal with the original IBOR time series by accumulated generating operation (AGO) and weaken the stochastic volatility in original series. And then, a forecasting model is founded by using ELM to analyze the new IBOR series. Lastly, the predictive value of the original IBOR series can be obtained by inverse accumulated generating operation (IAGO). The new model is applied to forecasting Interbank Offered Rate of China. Compared with the forecasting results of BP and classical ELM, the new model is more efficient to forecasting short- and middle-term volatility of IBOR. (C) 2015 Elsevier Ltd. All rights reserved.
机译：银行同业拆放利率是中国货币市场上唯一的直接市场利率。中国银行间同业拆借利率的波动性预测对金融资产定价和金融风险计量或管理具有重要的理论和实践意义。但是，IBOR是一个动态的非稳态时间序列，其发展变化具有较强的随机波动性，因此很难预测IBOR的波动性。本文提供了一种使用灰色模型和极限学习机（ELM）的混合算法来预测IBOR的波动性。所提出的算法由三个阶段组成。首先，使用灰色模型通过累积发电操作（AGO）处理原始IBOR时间序列，并减弱原始序列中的随机波动性。然后，通过使用ELM分析新的IBOR系列，建立了预测模型。最后，可以通过逆累积生成操作（IAGO）获得原始IBOR序列的预测值。该模型用于预测中国银行间同业拆借利率。与BP和经典ELM的预测结果相比，新模型在预测IBOR的中短期波动方面更为有效。 （C）2015 Elsevier Ltd.保留所有权利。

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