Multivariate Cointegration for Mean-Reversion Portfolios

Summary

This implementation outlines a workflow for building a mean-reversion spread from three or more assets. It fills missing price observations using forward filling or spline interpolation, converts prices to logarithms, and applies the Johansen procedure to estimate a cointegration vector. Users can select a significance level, and the fitting routine can warn when the eigenvalue or trace statistics do not support cointegration. The vector is intended to define the portfolio spread used to generate trading signals.

The module also provides helpers for price differences, return summaries, and cumulative-return plots. Its summary includes distribution statistics, Sharpe and Sortino ratios, and the proportion of positive and negative days. The document describes implementation mechanics rather than a complete trading rule: it does not specify entry, exit, or rebalancing logic, nor does it present empirical results. Cointegration may not persist out of sample, and imputation choices and statistical significance checks affect the resulting portfolio; reported metrics also do not account explicitly for costs or trading constraints.

Key ideas

• The method estimates a cointegration vector across multiple log-price series using the Johansen procedure.
• Missing prices can be filled by carrying prior observations forward or by spline interpolation.
• The fitting routine can warn when test statistics do not support cointegration at the selected significance level.
• The module supplies return statistics and cumulative-return plotting helpers but no full entry or exit strategy.
• The code provides no performance evidence, and live results may differ as relationships change.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.