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多资产投资组合执行的蒙特卡洛优化

文章 arXiv papers · 作者: Nico Achtsis et al.
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总结

该文将大额订单执行描述为流动性成本与价格风险之间的权衡。流动性有限时，快速交易可能加大市场冲击；缓慢交易则会使订单在执行期间暴露于不利价格变动。文中介绍了该问题的一种均值方差方法，并指出，早期一个包含随机流动性和波动率的单资产模型会产生非线性偏微分方程，必须通过数值方法求解。

所提出的替代方法使用准蒙特卡洛方法，优化任意数量资产的执行。作者还称该方法适用于实时使用，并可在执行过程中调整随机过程参数。所提供的摘要介绍了方法及其所声称的灵活性，但没有给出数值结果、验证细节或实现说明，因此仅凭本文无法评估其相对表现和实际局限。

核心观点

  • 大额订单需要权衡市场冲击与价格变动带来的风险。
  • 所述目标是在均值方差框架下平衡流动性成本与价格风险。
  • 先前的单资产随机模型需要通过数值方法求解非线性偏微分方程。
  • 所提出的准蒙特卡洛方法旨在支持多资产执行和实时参数调整。

标签

多资产交易执行风险管理统计学
全文
# A Monte Carlo method for optimal portfolio executions


# A Monte Carlo method for optimal portfolio executions









Traders are often faced with large block orders in markets with limited liquidity and varying volatility. Executing the entire order at once usually incurs a large trading cost because of this limited liquidity. In order to minimize this cost traders split up large orders over time. Varying volatility however implies that they now take on price risk, as the underlying assets' prices can move against the traders over the execution period. This execution problem therefore requires a careful balancing between trading slow to reduce liquidity cost and trading fast to reduce the volatility cost. R. Almgren solved this problem for a market with one asset and stochastic liquidity and volatility parameters, using a mean-variance framework. This leads to a nonlinear PDE that needs to be solved numerically. We propose a different approach using (quasi-)Monte Carlo which can handle any number of assets. Furthermore, our method can be run in real-time and allows the trader to change the parameters of the underlying stochastic processes on-the-fly.

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。