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结合因子模型与随机控制的高维统计套利

文章 arXiv papers · 作者: Jorge Guijarro-Ordonez
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总结

本文结合统计构建的因子模型与随机控制，研究高维统计套利。模型假设残差均值回归，并展示如何构建解析意义上的市场中性投资组合。随后，在指数效用和均值方差效用标准下，推导有限期限连续时间投资的闭式最优策略。

该框架还考虑美元中性约束和临时二次交易成本。作者通过涉及100项资产的蒙特卡洛模拟展示这些策略，并描述了可能的扩展。摘录没有给出模拟结果、模型校准细节或实盘市场证据，因此报告的支持来自计算，而非实证。

核心观点

  • 因子模型将残差收益表示为均值回归过程。
  • 解析构造可在高维场景中生成市场中性投资组合。
  • 随机控制可在指数效用和均值方差效用下推导有限期限闭式策略。
  • 该框架可纳入美元中性和临时二次交易成本。
  • 蒙特卡洛模拟展示了涉及100项资产的策略。

标签

套利均值回归因子投资投资组合构建统计学
全文
# High-dimensional statistical arbitrage with factor models and stochastic control


# High-dimensional statistical arbitrage with factor models and stochastic control









The present paper provides a study of high-dimensional statistical arbitrage that combines factor models with the tools from stochastic control, obtaining closed-form optimal strategies which are both interpretable and computationally implementable in a high-dimensional setting. Our setup is based on a general statistically-constructed factor model with mean-reverting residuals, in which we show how to construct analytically market-neutral portfolios and we analyze the problem of investing optimally in continuous time and finite horizon under exponential and mean-variance utilities. We also extend our model to incorporate constraints on the investor's portfolio like dollar-neutrality and market frictions in the form of temporary quadratic transaction costs, provide extensive Monte Carlo simulations of the previous strategies with 100 assets, and describe further possible extensions of our work.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。