带信号的最优清算:一般传播子情况

IF 2.4 3区 经济学 Q3 BUSINESS, FINANCE
Eduardo Abi Jaber, Eyal Neuman
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引用次数: 0

摘要

我们考虑了一类最优清算问题,其中代理人的交易产生了由voltera型传播因子驱动的瞬时价格影响以及临时价格影响。我们将这些问题表述为收益-风险函数的最大化,其中代理也利用可逐步测量的价格预测信号上的可用信息。利用无限维随机控制方法,我们用自由边界l2 $L^2$值倒向随机微分方程和算子值Riccati方程的解来表征值函数。然后推导出这些方程的解析解,得到最优交易策略的显式表达式。我们表明,我们的公式可以以一种简单有效的方式实现,适用于一类具有可能奇点的价格影响核,如幂律核。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Optimal Liquidation With Signals: The General Propagator Case

Optimal Liquidation With Signals: The General Propagator Case

We consider a class of optimal liquidation problems where the agent's transactions create transient price impact driven by a Volterra-type propagator along with temporary price impact. We formulate these problems as maximization of a revenue-risk functionals, where the agent also exploits available information on a progressively measurable price predicting signal. By using an infinite dimensional stochastic control approach, we characterize the value function in terms of a solution to a free-boundary L 2 $L^2$ -valued backward stochastic differential equation and an operator-valued Riccati equation. We then derive analytic solutions to these equations, which yields an explicit expression for the optimal trading strategy. We show that our formulas can be implemented in a straightforward and efficient way for a large class of price impact kernels with possible singularities such as the power-law kernel.

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来源期刊
Mathematical Finance
Mathematical Finance 数学-数学跨学科应用
CiteScore
4.10
自引率
6.20%
发文量
27
审稿时长
>12 weeks
期刊介绍: Mathematical Finance seeks to publish original research articles focused on the development and application of novel mathematical and statistical methods for the analysis of financial problems. The journal welcomes contributions on new statistical methods for the analysis of financial problems. Empirical results will be appropriate to the extent that they illustrate a statistical technique, validate a model or provide insight into a financial problem. Papers whose main contribution rests on empirical results derived with standard approaches will not be considered.
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