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A0596
Title: Sparse vector error correction models with application to cointegration-based trading Authors:  Philip Yu - The Education University of Hong Kong (Hong Kong) [presenting]
Renjie Lu - The University of Hong Kong (Hong Kong)
Xiaohang Wang - Fudan University and Zhuhai Fundan Innovation Institute (China)
Abstract: Inspired by constructing large-size cointegrated portfolios, a vector error correction model is considered. The adaptive Lasso estimator of the cointegrating vectors is developed. The asymptotic properties of the estimators and the oracle property of the adaptive Lasso are derived. An optimization algorithm for estimating the model parameters is proposed. The simulation study shows the effectiveness of the parameter estimation procedures and the forecasting performance of our model. In the empirical study, we apply the proposed method to construct the sparse cointegrated portfolios with or without market-neutral property. The trading performances of different types of cointegrated portfolios are evaluated using the Dow Jones Industrial Average composite stocks. The empirical findings reveal that the sparse cointegrated market-neutral portfolios of a number of securities can benefit the investors who wish to construct statistical arbitrage portfolios that are market-neutral.