所需交易量不确定时的最优执行
文章 arXiv papers · 作者: Julien Vaes et al.
总结
本文将假设待清算数量固定的Almgren–Chriss最优执行框架，扩展到所需交易量不确定、且临近交付期限时才逐渐明朗的情形。电力市场是这类问题的一个例子。交易者必须随着估计值随时间变化而调整执行方式。
所提出的模型加入了待交易数量不确定性的风险项。模型认为，厌恶风险的交易者可能会受益于推迟部分交易，同时仍需权衡价格风险与交易量目标不准确的风险。研究提出，通过静态策略避免动态规划的计算负担，同时保持有竞争力的表现。摘录未提供数值比较或该方法表现最佳的条件，因此仅凭总结无法评估其表现主张。
核心观点
- 该模型将最优执行扩展到目标数量临近交付时仍不确定的情形。
- 交易者同时面临价格风险和最终交易量估计不准的风险。
- 所提策略在提前和延后交易之间进行权衡，厌恶风险时会倾向于推迟部分交易。
- 带交易量风险项的静态策略旨在避免动态规划的复杂计算。
标签
全文
# Optimal Trade Execution with Uncertain Volume Target # Optimal Trade Execution with Uncertain Volume Target In the seminal paper on optimal execution of portfolio transactions, Almgren and Chriss (2001) define the optimal trading strategy to liquidate a fixed volume of a single security under price uncertainty. Yet there exist situations, such as in the power market, in which the volume to be traded can only be estimated and becomes more accurate when approaching a specified delivery time. During the course of execution, a trader should then constantly adapt their trading strategy to meet their fluctuating volume target. In this paper, we develop a model that accounts for volume uncertainty and we show that a risk-averse trader has benefit in delaying their trades. More precisely, we argue that the optimal strategy is a trade-off between early and late trades in order to balance risk associated with both price and volume. By incorporating a risk term related to the volume to trade, the static optimal strategies suggested by our model avoid the explosion in the algorithmic complexity usually associated with dynamic programming solutions, all the while yielding competitive performance.
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