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Simulating and Analyzing Order Book Data: The Queue-Reactive Model
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DOI：10.1080/01621459.2014.982278.png)
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Abstract
En 中文
Through the analysis of a dataset of ultra high frequency order book updates, we introduce a model which accommodates the empirical properties of the full order book together with the stylized facts of lower frequency financial data. To do so, we split the time interval of interest into periods in which a well chosen reference price, typically the midprice, remains constant. Within these periods, we view the limit order book as a Markov queuing system. Indeed, we assume that the intensities of the order flows only depend on the current state of the order book. We establish the limiting behavior of this model and estimate its parameters from market data. Then, to design a relevant model for the whole period of interest, we use a stochastic mechanism that allows to switch from one period of constant reference price to another. Beyond enabling to reproduce accurately the behavior of market data, we show that our framework can be very useful for practitioners, notably as a market simulator or as a tool for the transaction cost analysis of complex trading algorithms.
Keywords:
Ergodic properties
Execution probability
High frequency data
Jump Markov process
Limit order book
Mechanical volatility
Market impact
Market microstructure
Market simulator
Queuing model
Transaction costs analysis
Volatility
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