论文标题
基于杂交人工神经网络的堆叠模型的预测波动率
Forecasting volatility with a stacked model based on a hybridized Artificial Neural Network
论文作者
论文摘要
对波动性和市场风险的适当校准和预测是必须管理其投资或资金运营（例如银行，养老基金或保险公司）所固有的不确定性的公司所面临的一些主要挑战。在2007 - 2008年的金融危机之后，当评估市场风险和波动性失败的预测模型发生后，这变得更加明显。从那时起，大量的理论发展和方法似乎提高了波动性预测和市场风险评估的准确性。遵循这种思维方式，本文介绍了一个模型，基于使用一组机器学习技术，例如梯度下降，随机森林，支持向量机和人工神经网络，这些算法被堆叠以预测S＆P500的波动率。结果表明，我们的施工在预测波动水平的能力方面优于其他习惯模型，从而更准确地评估了市场风险。
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.
