C-Vine Copulas for Multi-Stock Statistical Arbitrage
Summary
The document explains a mean-reversion strategy that uses a C-vine copula to model dependence among a cohort of stocks. It converts returns into empirical quantiles, fits candidate vine structures and bivariate copulas, then uses conditional probabilities to estimate whether a target stock is relatively underpriced or overpriced. The resulting mispricing signal guides trades against the other stocks in the cohort. The target stock’s placement in the vine is discussed, including an alternative structure that makes it the central variable.
The article describes selecting structures by Akaike information criterion or likelihood and illustrates the approach with example cohorts and an equity curve. It notes that a poorly matched cohort, such as one that behaves differently from the broad market, can undermine results; filtering cohorts using their relationship to a market benchmark is suggested as a possible improvement. The article presents vine copulas as flexible dependence models, but does not establish broad out-of-sample performance. It also flags the method’s substantial learning curve and suggests combining copula signals with other time-series methods.
Key ideas
- C-vine copulas model multivariate dependence while allowing different marginal distributions.
- Conditional probabilities from the fitted model can serve as relative mispricing signals for mean-reversion trades.
- Candidate vine structures can be compared using information criteria or likelihood.
- Cohort selection matters, and stocks that diverge from the broader market may weaken the strategy.
- Copula-derived signals can be combined with other time-series trading methods.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.