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Chao Zhou: Optimal Liquidation with Hidden Orders under Self-Exciting Dynamics
发布时间:2022-12-01 10:15:00 浏览次数:990

The 264th Wenlan Financial Forum

Topic:

Optimal Liquidation with Hidden Orders under Self-Exciting Dynamics

Speaker:

Chao Zhou, Researcher

Host:

Xianming Sun, Associate Professor

School of Finance, Zhongnan University of Economics and Law

Innovation and Talent Base for Digital Technology and Finance

Time:

13:30 -- 15:00 on Friday, December 6, 2022

Location:

VooV Meeting (890-4387-0665)

 

Abstract:

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 56% to the pure limit order strategy and 13% to the strategy with mixed type under the Poisson  process.

This is a joint work with Ying Chen, Zexin Wang and Ge Zhang.

Speaker Introduction:

Chao Zhou,Associate Professor in the Department of Mathematics and Risk Management Institute (joint appointment),  NUS. He got his PhD in Applied Mathematics from CMAP,  Ecole Polytechnique. His research interests include mathematical finance,  stochastic control and deep learning in finance. He published several papers in MF, AOP, AAP and JCP,  etc. He is now the director of the Master in Quantitative Finance Programme at NUS.

 

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