Research map / Graph learning / Graph algorithms and clustering
Graphical Models in Heavy-Tailed Markets
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace Gaussian (NGL) with Student-t likelihood | Laplacian graph learning · S&P500 connected | modularity: 0.49 -> 0.54 | Helped |
| replace Gaussian (NGL) with Student-t likelihood | Laplacian graph learning · S&P500 COVID period | modularity: 0.46 -> 0.56 | Helped |
| replace Gaussian (NGL) with Student-t likelihood | Laplacian graph learning · Currencies connected | modularity: 0.46 -> 0.58 | Helped |
| replace Gaussian (NGL) with Student-t likelihood | Laplacian graph learning · Cryptocurrencies connected | modularity: 0.40 -> 0.52 | Helped |
| replace Gaussian CLR with Student-t k-component formulation | k-component graph learning · S&P500 3-component | modularity: 0.33 -> 0.56 | Helped |
| replace Gaussian CLR with Student-t k-component formulation | k-component graph learning · Currencies 9-component | modularity: 0.79 -> 0.84 | Helped |
| replace Gaussian CLR with Student-t k-component formulation | k-component graph learning · Cryptocurrencies 7-component | modularity: 0.66 -> 0.79 | Helped |
| add degree constraint d_w = d (avoid isolated nodes) (ablation) | k-component Student-t · Currencies and crypto | modularity: 0.62 -> 0.84 | Helped |
| initialize with uniform fully connected graph instead of default initialization (ablation) | Algorithm 2 · S&P500, currencies, crypto | modularity: 0.56 -> 0.56; 0.84 -> 0.80; 0.79 -> 0.78 | No clear effect |
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