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不同市场状态下的广义统计套利

文章 arXiv papers · 作者: Christian Rein et al.
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总结

本文通过将收益定义为相对于特定市场状态的收益，拓展了统计套利概念；市场状态由诸如 σ-代数的信息系统表示。该框架将经典套利作为特例，也涵盖归于 Bondarenko 的统计套利概念。作者还提出广义盈利策略，其中可包括静态和半静态头寸。

本文刻画了这些广义统计无套利概念，并针对不同的信息系统构造策略。示例包括嵌入式二项式策略、趋势跟随策略和基于划分的策略。作者利用模拟数据比较这些策略的表现，并报告了它们在市场数据上的良好表现。这些结果表明，在选定情景下具有正平均收益的策略可能与标准无套利条件并存。摘要没有提供实施细节、交易成本估算，也没有充分信息评估所报告市场表现能否在实盘交易中保持稳健。

核心观点

  • 该框架定义特定市场状态下的平均收益，而不要求收益几乎处处为正。
  • 经典套利和 Bondarenko 式统计套利是这一更广泛框架中的特例。
  • 广义盈利策略可包括静态和半静态头寸。
  • 本文通过模拟和市场数据研究嵌入式二项式、趋势跟随及基于划分的策略。
  • 在明确界定的情景集合中，正平均收益可能与标准无套利条件并存。

标签

套利统计学趋势跟踪回测
全文
# Generalized statistical arbitrage concepts and related gain strategies


# Generalized statistical arbitrage concepts and related gain strategies









Generalized statistical arbitrage concepts are introduced corresponding to trading strategies which yield positive gains on average in a class of scenarios rather than almost surely. The relevant scenarios or market states are specified via an information system given by a $σ$-algebra and so this notion contains classical arbitrage as a special case. It also covers the notion of statistical arbitrage introduced in Bondarenko (2003). Relaxing these notions further we introduce generalized profitable strategies which include also static or semi-static strategies. Under standard no-arbitrage there may exist generalized gain strategies yielding positive gains on average under the specified scenarios. In the first part of the paper we characterize these generalized statistical no-arbitrage notions. In the second part of the paper we construct several profitable generalized strategies with respect to various choices of the information system. In particular, we consider several forms of embedded binomial strategies and follow-the-trend strategies as well as partition-type strategies. We study and compare their behaviour on simulated data. Additionally, we find good performance on market data of these simple strategies which makes them profitable candidates for real applications.

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。