文档介绍：Statistical Arbitrage in High Frequency Trading Based
on Limit Order Book Dynamics
Murat Ahmed, Anwei Chai, Xiaowei Ding, Yunjiang Jiang, Yunting Sun
June 11, 2009
1 Introduction
Classic asset pricing theory assumes prices will eventually adjust to and reflect the fair value,
the route and speed of transition is not specified. Market Microstructure studies how prices
adjust to reflect new information. Recent years have seen the widely available high frequency
data enabled by the rapid advance in information technology. Using high frequency data,
it’s interesting to study the roles played by the informed traders and noise traders and how
the prices are adjusted to reflect information flow. It’s also interesting to study whether
returns are more predictable in the high frequency setting and whether one could exploit
limit order book dynamics in trading.
Broadly speaking, the traditional approach to statistical arbitrage is through attempting
to bet on the temporal convergence and divergence of price movements of pairs and baskets
of assets, using statistical methods. A more academic definition of statistical arbitrage is to
spread the risk among thousands to millions of trades in very short holding time, hoping to
gain profit in expectation through the law of large numbers. Following this line, recently,
a model based approach has been proposed by Rama Cont and coauthors [1], based on a
simple birth-death markov chain model. After the model is calibrated to the order book
data, various types of odds can puted. For example, if a trader could estimate the
probability of mid-price uptick movement conditional on the current orderbook status and if
the odds are in his/her favor, the trader could submit an order to capitalize the odds. When
the trade is carefully executed with a judicious stop-loss, the trader should be able to make
profit in expectation.
In this project, we adopted a data-driven approach. We first built an ”simulated” ex-
change order