Premium
Sparse vector error correction models with application to cointegration‐based trading
Author(s) -
Lu Renjie,
Yu Philip L.H.,
Wang Xiaohang
Publication year - 2020
Publication title -
australian and new zealand journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 1369-1473
DOI - 10.1111/anzs.12304
Subject(s) - estimator , cointegration , econometrics , statistical arbitrage , lasso (programming language) , mathematics , oracle , pairs trade , construct (python library) , computer science , statistics , algorithmic trading , economics , capital asset pricing model , financial economics , alternative trading system , software engineering , arbitrage pricing theory , world wide web , risk arbitrage , programming language
Summary Inspired by constructing large‐size cointegrated portfolios, this paper considers a vector error correction model and develops the adaptive Lasso estimator of the cointegrating vectors. The asymptotic properties of the estimators and the oracle property of the adaptive Lasso are derived. An optimisation 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 are capable to benefit the investors who wish to construct statistical arbitrage portfolios which are market‐neutral.