论文标题
关于波动性形成过程的普遍性：当机器学习和粗糙波动同意时
On the universality of the volatility formation process: when machine learning and rough volatility agree
论文作者
论文摘要
我们根据由数百个液体库存制成的合并数据集培训LSTM网络，旨在预测所有股票的下一个每日实现的波动性。我们显示了这种通用LSTM相对于其他资产特定参数模型的持续超越表现，我们发现了与过去的市场实现相关的普遍波动形成机制的非参数证据，包括每日回报和波动率，与当前的挥发性相关。结合了粗糙的分数随机波动率和二次粗糙的赫斯顿模型的简约参数预测设备与固定参数相同的性能水平与通用LSTM相同，从而证实了从参数角度来证实波动率形成过程的普遍性。
We train an LSTM network based on a pooled dataset made of hundreds of liquid stocks aiming to forecast the next daily realized volatility for all stocks. Showing the consistent outperformance of this universal LSTM relative to other asset-specific parametric models, we uncover nonparametric evidences of a universal volatility formation mechanism across assets relating past market realizations, including daily returns and volatilities, to current volatilities. A parsimonious parametric forecasting device combining the rough fractional stochastic volatility and quadratic rough Heston models with fixed parameters results in the same level of performance as the universal LSTM, which confirms the universality of the volatility formation process from a parametric perspective.
