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fix: corriger les majuscules dans les statuts des projets et mettre à jour les descriptions des projets

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Arthur Danjou
2026-02-16 20:22:11 +01:00
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content/projects/glm-financial-assets.md
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@@ -6,7 +6,7 @@ description: A large-scale statistical study comparing Generalized Linear Models
shortDescription: Predicting the SPX volatility surface using GLMs and black-box models on 1.2 million observations.
publishedAt: 2026-02-28
readingTime: 3
status: in progress
status: In progress
tags:
- R
- GLM
@@ -17,7 +17,7 @@ icon: i-ph-graph-duotone
This project targets high-precision calibration of the **Implied Volatility Surface** using a large-scale dataset of S&P 500 (SPX) European options.
The core objective is to stress-test classic statistical models against modern predictive algorithms. **Generalized Linear Models (GLMs)** provide a transparent baseline, while more complex "black-box" architectures are evaluated on whether their accuracy gains justify the loss of interpretability in a risk management setting.
The core objective is to stress-test classic statistical models against modern predictive algorithms. **Generalized Linear Models (GLMs)** provide a transparent baseline, while more complex "black-box" architectures are evaluated on whether their accuracy gains justify reduced interpretability in a risk management context.
## 📊 Dataset & Scale
@@ -44,7 +44,7 @@ Key financial indicators are derived from the raw data:
## 📈 Evaluation & Reproducibility
Performance is measured strictly via RMSE on the original scale of the target variable. To ensure reproducibility and precise comparisons across model iterations, a fixed random seed is maintained throughout the entire workflow.
Performance is measured strictly via RMSE on the original scale of the target variable. To ensure reproducibility and precise comparisons across model iterations, a fixed random seed is maintained throughout the workflow.
```r
set.seed(2025)