The Department of
Statistics and Operations Research
The University of North Carolina at Chapel Hill
Ph.D. Thesis Defense
Wednesday, November 15th
3:30 pm
Dey Hall 204
Jiaying Li
Statistical Arbitrage and Algorithmic Trading
Under the direction of Chuanshu Ji
Pairs trading strategy takes advantage of diversification across stocks, to produce a lowvolatility investment strategy which is uncorrelated with the market. In this dissertation, we
propose an OU-GARCH model to model the spread. We develop three trading strategies under
GARCH volatility assumption. The first trading strategy is based on the percentile of spread, the
second trading strategy maximizes the expected profit per-unit-time, and the third trading
strategy maximizes the expected profit per-unit-time for a single trade given a finite investment
horizon. Backtesting on both synthetic data and real daily data verifies the necessity of replacing
constant volatility assumption with GARCH volatility assumption. OU-GARCH model plays the
central role in this work. However, the key assumption for our OU-GARCH is that the spread
needs to be mean-reverting and in reality, the mean reversion level can change after we open the
trade, we then explore the calculation for adjusted optimal threshold when a single change point
is detected after opening the trade. We also develop a trend-based trading strategy which is
particularly suitable to be applied to the trending market condition

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