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Optimal trading with frictions
File | Description | Size | Format | |
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Wang-Z-2023-PhD-Thesis.pdf | Thesis | 14.34 MB | Adobe PDF | View/Open |
Title: | Optimal trading with frictions |
Authors: | Wang, Zexin |
Item Type: | Thesis or dissertation |
Abstract: | This thesis studies the optimal trading problem with particular attention to frictions, taking alpha signals as given in several practical settings in modern financial markets. Chapter 2 provides a reduced-form model for price impact of market orders. As a scaling limit of the econo-physics propagator model, it has both tractability for optimization and good empirical fit. The nonlinearity in propagator model is explained as a effect of intraday stochasticity of the market activity. Optimal trading strategies are given for the case of stochastic alpha signal and volume signals in closed-form solutions. Moreoever, concrete bounds for the absence of price manipulation strategies are provided. Chapter 3 derives an actionable derivatives hedging strategy with both market and limit orders from the perspective of a central risk book. It is found that limit order is only beneficial for delta-hedging when the gamma of the risky position is negative. Additionally, the interaction between transaction cost, adverse selection and risk aversion can be characterized by a nonlinear PDE that describes the option price. According to empirical analysis, tactical liquidity provision is beneficial for non-competitive market makers for reasonable trading frequencies. Chapter 4 studies the usage of display and nondisplay limit orders for order execution. A price impact model is postulated and the corresponding scheduling algorithm is derived. In the case where nondisplay limit order (hidden order) is used, there is a time which separates the trading horizon into two regimes: the former only uses hidden order, and the latter uses the mixture of limit and hidden orders. The effectiveness and robustness of the algorithm is shown via numerical testing in both simulated data and NASDAQ 100 Index data. |
Content Version: | Open Access |
Issue Date: | Apr-2023 |
Date Awarded: | Jul-2023 |
URI: | http://hdl.handle.net/10044/1/105831 |
DOI: | https://doi.org/10.25560/105831 |
Copyright Statement: | Creative Commons Attribution NonCommercial Licence |
Supervisor: | Muhle-Karbe, Johannes |
Sponsor/Funder: | Imperial College London |
Department: | Mathematics |
Publisher: | Imperial College London |
Qualification Level: | Doctoral |
Qualification Name: | Doctor of Philosophy (PhD) |
Appears in Collections: | Mathematics PhD theses |
This item is licensed under a Creative Commons License