Automated Volatility Forecasting

Sophia Zhengzi Li

doi:10.1287/mnsc.2023.01520

2024-10-29

PRE

Sophia Zhengzi Li *

Yushan Tang

DOI：10.1287/mnsc.2023.01520

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摘要

En 中文

我们开发了一个自动化系统，通过利用超过100个特征和五种机器学习算法来预测波动率。考虑到S&P 100股票的范围，我们的系统在所有预测期限内，其样本外波动率预测结果均优于现有的风险模型。我们进一步证明，该系统对不同规格保持稳健，并通过超参数迁移学习可扩展至更广泛的S&P 500股票范围。最后，波动率预测的统计改进转化为通过截面方差风险溢价策略获得的显著年度回报。

Keyword:

automation

machine learning

volatility forecasting

high-frequency data

transfer learning

期刊

Management Science

IF:

4.9

论文数:

794

被引数:

5.0W

机构

rutgers business sch

学者数:

论文数: 2

被引数: 1

Shanghai University of Finance and Economics

学者数:

2.0K

论文数: 2.5K

被引数: 4.0K

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