This thesis is concerned with addressing operational issues in two types of dynamic markets where queueing plays an important role: limit order books (financial industry), and dynamic matching markets (residential real estate).
We first study the smart order routing decisions of investors in fragmented limit order book markets and the implications on the market dynamics. In modern equity markets, participants have a choice of many exchanges at which to trade. Exchanges typically operate as electronic limit order books operating under a “price-time” priority rule and, in turn, can be modeled as multi-class FIFO queueing systems. A market with multiple exchanges can be thought as a decentralized, parallel queueing system. Heterogeneous traders that submit limit orders select the exchange to place their orders by trading off delays until their order may fill against financial considerations. Simultaneously, traders that submit market orders select the exchange to direct their orders. These market orders trigger instantaneous service completions of queued limit orders. Taking into account the effect of investors’ order routing decisions, we find that the equilibrium of this decentralized market exhibits a state space collapse property. The predicted dimension reduction is the result of high-frequency order routing decisions that essentially couple the dynamics across exchanges. Analyzing a TAQ dataset for a sample of stocks over a one month period, we find empirical support for the predicted state space collapse.
In the second part of this thesis, we model an electronic limit order book as a multi-class queueing system under fluid dynamics, and formulate and solve a problem of limit and market order placement to optimally buy a block of shares over a short, predetermined time horizon. Using the structure of the optimal execution policy, we identify microstructure variables that affect trading costs over short time horizons and propose a resulting microstructure-based model of market impact costs. We use a proprietary data set to estimate this cost model, and highlight its insightful structure and increased accuracy over conventional (macroscopic) market impact models that estimate the cost of a trade based on its normalized size but disregarding measurements of limit order book variables.
In the third part of this thesis, we study the residential real estate markets as dynamic matching systems with an emphasis on their microstructure. We propose a stylized microstructure model and analyze the market dynamics and its equilibrium under the simplifying approximation where buyers and sellers use linear bidding strategies. We motivate and characterize this near closed-form approximation of the market equilibrium, and show that it is asymptotically accurate. We also provide numerical evidence in support of this approximation. Then with the gained tractability, we characterize steady-state properties such as market depth, price dispersion, and anticipated delays in selling or buying a unit. We characterize congestion and matching patterns for sellers and buyers, taking into account market dynamics, heterogeneity, and supply and demand imbalance manifested in the competition among buyers and sellers. Furthermore, we show the effects of market primitives with comparative statics results.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/D82V2FXH |
| Date | January 2016 |
| Creators | Zheng, Hua |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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