讲座题目：Optimal Liquidation with Hidden Orders under Self-Exciting Dynamics

时间：2023年5月31日（周三）早上9:30 – 11:00

地点：博学楼925

主讲人：周超

主讲人简介：

周超博士毕业于法国巴黎九大和巴黎综合理工大学，现为新加坡国立大学数学系和风险管理研究院副教授。他同时任新加坡国立大学量化金融中心主任并负责量化金融硕士项目。周超博士的主要研究领域包括金融数学，随机控制，深度学习方法在金融中的应用。他在《The Annals of Probability》、《The Annals of Applied Probability》、《Mathematical Finance》、《Finance and Stochastics》、《Journal of Economic Dynamics & Control》、《SIAM Journal on Control and Optimization》、《SIAM Journal on Financial Mathematics》等多个国际权威的金融数学杂志上发表论文30余篇。

讲座内容简介：

Hidden orders are attracting higher usage in modern order-driven markets, providing exposure risk reduction and mitigating adverse selection costs. We develop an optimal liquidation strategy in a continuous-time framework, where a risk-neutral agent aims to maximize her terminal wealth with a combination of both hidden and display limit orders over a fixed period. All the remaining shares must be sold using market orders at termination. The agent controls the trading rate (order size) and order type (hidden and displayed) to balance execution cost and time pressure. When market order arrivals are modeled as a homogeneous Poisson process, we derive a closed-form solution that contains a switching time, at which the agent changes from a pure- hidden-order phase to a mixed-orders phase until termination. Under the Hawkes process with self-exciting dynamics, a numerical solution is provided. We show that the optimal strategy exhibits a similar two-phase pattern, except that the switching time becomes a function of the market order intensity. Simulation experiments show that the use of hidden order reduces liquidation cost, accompanied by an increase in liquidity. Given event-level limit order book data of 100 NASDAQ stocks, we implement the liquidation strategies. It shows that our strategy with mixed type under the self-exciting dynamics provides superior performance, with cost reduction up to 70% to the pure limit order strategy and 27% to the strategy with mixed type under the Poisson process. This is a joint work with Ying Chen, Zexin Wang and Ge Zhang.