Improved Parameter Estimation and Simple Trading Algorithm for Sparse, Mean-Reverting Portfolios
Annales Univ. Sci. Budapest., Sect. Comp. 37 (2012) 121–144
24 Pages Posted: 31 Jul 2017
Date Written: May 27, 2012
We examine the problem of finding sparse, mean reverting portfolios based on multivariate historical time series. After mapping optimal portfolio selection into a generalized eigenvalue problem, two different heuristic algorithms are referenced for finding the solution in a subspace which satisfies the cardinality constraint. Having identified the optimal portfolio, we outline the known methods for finding the long-term mean and introduce a novel approach based on pattern matching. Furthermore, we present a simple convergence trading algorithm with a decision theoretic approach, which can be used to compare the economic viability of the different methods and test the effectiveness of our end-to-end process by extensive simulations on generated and historical real market data.
Keywords: Mean reversion, sparse estimation, convergence trading, parameter estimation, VAR(1) model, covariance selection, financial time series
JEL Classification: G11
Suggested Citation: Suggested Citation