Discussing the article: "Bivariate Copulae in MQL5: (Part 3): Implementation and Tuning of Mixed Copula Models in MQL5"
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Check out the new article: Bivariate Copulae in MQL5: (Part 3): Implementation and Tuning of Mixed Copula Models in MQL5.
The article extends our copula toolkit with mixed copulas implemented natively in MQL5. We construct Clayton–Frank–Gumbel and Clayton–Student–t–Gumbel mixtures, estimate them via EM, and enable sparsity control through SCAD with cross‑validation. Provided scripts tune hyperparameters, compare mixtures using information criteria, and save trained models. Practitioners can apply these components to capture asymmetric tail dependence and embed the selected model in indicators or Expert Advisors.
The first two articles in our exploration of copula functions covered the two most common classes: Elliptical and Archimedean copulas. Through that analysis, it was established that different copulas capture distinct types of data dependence. However, because financial data is inherently complex, a single copula family may not adequately capture the full spectrum of dependence structures within a dataset. In that regard, mixed copulas may be able to address this limitation by combining the strengths of individual copula families to model a broader range of dependencies. In this article, we describe the implementation of mixed copula models using the families introduced in our previous installments.
Author: Francis Dube