In this systematic literature review, we examine the existing studies predicting realized volatility and implied volatility indices using artificial intelligence and machine learning. We survey the literature in order to discover whether the proposed methods provide superior forecasts compared to traditional econometric models, how widespread the application of explainable AI is, and to outline potential areas for further research. Generally, we find the efficacy of AI and ML methods for volatility prediction to be highly promising, often providing comparative or better results than their econometric counterparts. Neural networks employing memory, such as Long–Short Term Memory and Gated Recurrent Units, consistently rank among the top performing models. However, traditional econometric models are still highly relevant, commonly yielding similar results as more advanced ML and AI models. In light of the success with ensemble methods, a promising area of research is the use of hybrid models, combining machine learning and econometric models. In spite of the common critique of many machine learning models being of a black-box nature, we find that very few papers apply XAI to analyze and support their empirical results. Thus, we recommend that researchers strive harder to employ XAI in future work. Similarly, we see potential for applications of probabilistic machine learning, effectively quantifying uncertainty in volatility forecasts from machine learning models.

中文翻译：

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使用人工智能和机器学习预测已实现波动率和隐含波动率指数：综述

在这篇系统文献综述中，我们研究了利用人工智能和机器学习预测已实现波动率和隐含波动率指数的现有研究。我们调查了文献，以了解所提出的方法是否比传统的计量经济模型提供更好的预测，可解释的人工智能的应用有多广泛，并概述了进一步研究的潜在领域。一般来说，我们发现人工智能和机器学习方法在波动率预测方面的功效非常有前途，通常比计量经济学方法提供可比较或更好的结果。使用记忆的神经网络，例如长短期记忆和门控循环单元，始终名列表现最佳的模型之列。然而，传统的计量经济学模型仍然高度相关，通常会产生与更先进的机器学习和人工智能模型相似的结果。鉴于集成方法的成功，一个有前途的研究领域是使用混合模型，将机器学习和计量经济学模型相结合。尽管人们普遍批评许多机器学习模型具有黑盒性质，但我们发现很少有论文应用 XAI 来分析和支持其实证结果。因此，我们建议研究人员在未来的工作中更加努力地使用 XAI。同样，我们看到了概率机器学习的应用潜力，可以有效地量化机器学习模型波动率预测的不确定性。