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首页> 外文期刊>Journal of Intelligent Learning Systems and Applications >Recurrent Support and Relevance Vector Machines Based Model with Application to Forecasting Volatility of Financial Returns
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Recurrent Support and Relevance Vector Machines Based Model with Application to Forecasting Volatility of Financial Returns

机译：基于经常性支持和相关矢量机的应用，以预测财务回报的波动性

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In the recent years, the use of GARCH type (especially, ARMA-GARCH) models and computational-intelligence-based techniques—Support Vector Machine (SVM) and Relevance Vector Machine (RVM) have been successfully used for financial forecasting. This paper deals with the application of ARMA-GARCH, recurrent SVM (RSVM) and recurrent RVM (RRVM) in volatility forecasting. Based on RSVM and RRVM, two GARCH methods are used and are compared with parametric GARCHs (Pure and ARMA-GARCH) in terms of their ability to forecast multi-periodically. These models are evaluated on four performance metrics: MSE, MAE, DS, and linear regression R squared. The real data in this study uses two Asian stock market composite indices of BSE SENSEX and NIKKEI225. This paper also examines the effects of outliers on modeling and forecasting volatility. Our experiment shows that both the RSVM and RRVM perform almost equally, but better than the GARCH type models in forecasting. The ARMA-GARCH model is superior to the pure GARCH and only the RRVM with RSVM hold the robustness properties in forecasting.
机译：近年来，使用GARCH型（特别是ARMA-GARCH）模型和计算智能技术 - 支持向量机（SVM）和相关矢量机（RVM）已成功地用于财务预测。本文涉及ARMA-GARCH，复发性SVM（RSVM）和复发性RVM（RRVM）在挥发性预测中的应用。基于RSVM和RRVM，使用了两种加法方法，并在其能够预测多定期预测的能力方面与参数加粗（纯和ARMA-GARCH）进行比较。这些模型在四个性能指标上进行评估：MSE，MAE，DS和线性回归R平方。本研究中的真实数据使用BSE Sensex和Nikkei225的两个亚洲股票市场综合指数。本文还研究了异常值对建模和预测波动性的影响。我们的实验表明，RSVM和RRVM都同样执行，但比GARCH型模型更好。 ARMA-GARCH型号优于纯GARCH，只有RRVM，RSVM均在预测中保持稳健性。

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