Forecasting volatility with a stacked model based on a hybridized Artificial Neural Network
Forecasting volatility with a stacked model based on a hybridized Artificial Neural Network
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DOI：
10.1016/j.eswa.2019.03.046
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发表时间：
2019-09
期刊：
影响因子：
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通讯作者：
Eduardo Ramos-Pérez;P. Alonso-González;J. J. Núñez-Velázquez-J.
中科院分区：
文献类型：
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作者：
Eduardo Ramos-Pérez;P. Alonso-González;J. J. Núñez-Velázquez-J.
对波动性和市场风险进行适当的校准和预测是银行、养老基金或保险公司等必须管理其投资或融资业务固有的不确定性的公司所面临的一些主要挑战。这在2007-2008年金融危机后变得更加明显，当时评估市场风险和波动性的预测模型失败了。从那时起，大量的理论发展和方法似乎提高了波动预测和市场风险评估的准确性。基于这一思路，本文介绍了一个基于使用一组机器学习技术的模型，如梯度下降助推，随机森林，支持向量机和人工神经网络，这些算法堆叠在一起来预测标准普尔500指数的波动率。结果表明，我们的建设优于其他习惯模型的能力，预测波动水平，从而更准确地评估市场风险。
An appropriate calibration and forecasting of volatility and market risk are some of the main challenges faced by companies that have to manage the uncertainty inherent to their investments or funding operations such as banks, pension funds or insurance companies. This has become even more evident after the 2007–2008 Financial Crisis, when the forecasting models assessing the market risk and volatility failed. Since then, a significant number of theoretical developments and methodologies have appeared to improve the accuracy of the volatility forecasts and market risk assessments. Following this line of thinking, this paper introduces a model based on using a set of Machine Learning techniques, such as Gradient Descent Boosting, Random Forest, Support Vector Machine and Artificial Neural Network, where those algorithms are stacked to predict S&P500 volatility. The results suggest that our construction outperforms other habitual models on the ability to forecast the level of volatility, leading to a more accurate assessment of the market risk.