![Sample our Mathematics & Statistics journals, sign in here to start your FREE access for 14 days]({"type":"image" , "src" : "/sda/5943/TOC-Subject-Sample-2021-Mathematics-Statistics.jpg"})
Abstract
VWAP stands for volume-weighted average price during a certain trading period, and a VWAP trade refers a trade that uses VWAP as a benchmark. This article provides on-line execution strategies for a VWAP trade. We first propose a simulation-based statistical price-volume model that enables the VWAP to be reformulated as a combination of two Brownian motions. Then we introduce the dynamic programming algorithm as the comparison of our trading strategies. Since the dynamic programming algorithm is model dependent and computationally intensive, we present some simple alternative strategies for VWAP trading. Among these, we first propose a modified cross-boundary strategy which can be shown as an asymptotic approximation of the dynamic programming strategy. Next, we introduce a relative rank strategy that ignores the actual stock price and considers only the stock price's relative rank. Our simulation results show that the (modified) cross-boundary strategy is better for stock prices with negative drift, whereas the relative rank strategy performs well for stock prices with positive drift. As a result, when a trader faces a long trading horizon with multi-periods and variate drifts, a hybrid algorithm based on the intra-day drift trend is proposed. Finally, to demonstrate the robustness of the trading strategies relative to the price-volume model, and evaluate the effect of VWAP trading, we present an empirical study of the trading strategies on the top 20 liquidity stocks on the Taiwan Stock Exchange Corporation.
ACKNOWLEDGMENTS
The research of Cheng-Der Fuh was supported in part by NSC 97-2118-M-008-001-MY3. We thank Prof. Robert Chen of Miami University for useful discussions. We also thank the anonymous referee, whose comments on the article greatly improved its quality.
Notes
*The best strategy in terms of WR or ER.
Recommended by A. G. Tartakovsky
