artsite/content/projects/glm-implied-volatility.md

slug, title, type, description, shortDescription, publishedAt, readingTime, status, tags, icon

slug	title	type	description	shortDescription	publishedAt	readingTime	status	tags	icon
implied-volatility-prediction-from-options-data	Implied Volatility Prediction from Options Data	Academic Project	A large-scale statistical study comparing Generalized Linear Models (GLMs) and black-box machine learning architectures to predict the implied volatility of S&P 500 options.	Predicting implied volatility using advanced regression techniques and machine learning models on financial options data.	2026-02-28	3	Completed	R GLM Finance Machine Learning Statistical Modeling	i-ph-graph-duotone

M2 Master's Project – Predicting implied volatility using advanced regression techniques and machine learning models on financial options data.

This project explores the prediction of implied volatility from options market data, combining classical statistical methods with modern machine learning approaches. The analysis covers data preprocessing, feature engineering, model benchmarking, and interpretability analysis using real-world financial panel data.

• GitHub Repository: Implied-Volatility-from-Options-Data

---

::BackgroundTitle{title="Project Overview"} ::

Problem Statement

Implied volatility represents the market's forward-looking expectation of an asset's future volatility. Accurate prediction is crucial for:

• Option pricing and valuation
• Risk management and hedging strategies
• Trading strategies based on volatility arbitrage

Dataset

The project uses a comprehensive panel dataset tracking 3,887 assets across 544 observation dates (2019-2022):

File	Description	Shape
Train_ISF.csv	Training data with target variable	1,909,465 rows × 21 columns
Test_ISF.csv	Test data for prediction	1,251,308 rows × 18 columns
hat_y.csv	Final predictions from both models	1,251,308 rows × 2 columns

Key Variables

Target Variable:

• implied_vol_ref – The implied volatility to predict

Feature Categories:

• Identifiers: asset_id, obs_date
• Market Activity: call_volume, put_volume, call_oi, put_oi, total_contracts
• Volatility Metrics: realized_vol_short, realized_vol_mid1-3, realized_vol_long1-4, market_vol_index
• Option Structure: strike_dispersion, maturity_count

---

::BackgroundTitle{title="Methodology"} ::

Data Pipeline

Raw Data
    ↓
┌─────────────────────────────────────────────────────────┐
│  Data Splitting (Chronological 80/20)                   │
│  - Training: 2019-10 to 2021-07                         │
│  - Validation: 2021-07 to 2022-03                       │
└─────────────────────────────────────────────────────────┘
    ↓
┌─────────────────────────────────────────────────────────┐
│  Feature Engineering                                    │
│  - Aggregation of volatility horizons                   │
│  - Creation of financial indicators                     │
└─────────────────────────────────────────────────────────┘
    ↓
┌─────────────────────────────────────────────────────────┐
│  Data Preprocessing (tidymodels)                        │
│  - Winsorization (99.5th percentile)                    │
│  - Log/Yeo-Johnson transformations                      │
│  - Z-score normalization                                │
│  - PCA (95% variance retention)                         │
└─────────────────────────────────────────────────────────┘
    ↓
Three Datasets Generated:
├── Tree-based (raw, scale-invariant)
├── Linear (normalized, winsorized)
└── PCA (dimensionality-reduced)

Feature Engineering

New financial indicators created to capture market dynamics:

Feature	Description	Formula
pulse_ratio	Volatility trend direction	RV_short / RV_long
stress_spread	Asset vs market stress	RV_short - Market_VIX
put_call_ratio_volume	Immediate market stress	Put_Volume / Call_Volume
put_call_ratio_oi	Long-term risk structure	Put_OI / Call_OI
liquidity_ratio	Market depth	Total_Volume / Total_OI
option_dispersion	Market uncertainty	Strike_Dispersion / Total_Contracts
put_low_strike	Downside protection density	Strike_Dispersion / Put_OI
put_proportion	Hedging vs speculation	Put_Volume / Total_Volume

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::BackgroundTitle{title="Models Implemented"} ::

Linear Models

Model	Description	Best RMSE
OLS	Ordinary Least Squares	11.26
Ridge	L2 regularization	12.48
Lasso	L1 regularization (variable selection)	12.03
Elastic Net	L1 + L2 combined	~12.03
PLS	Partial Least Squares (on PCA)	12.79

Linear Mixed-Effects Models (LMM)

Advanced panel data models accounting for asset-specific effects:

Model	Features	RMSE
LMM Baseline	All variables + Random Intercept	8.77
LMM Reduced	Collinearity removal	~8.77
LMM Interactions	Financial interaction terms	~8.77
LMM + Quadratic	Convexity terms (vol of vol)	8.41
LMM + Random Slopes (mod_lmm_5)	Asset-specific betas	8.10 ⭐

Tree-Based Models

Model	Strategy	Validation RMSE	Training RMSE
XGBoost	Level-wise, Bayesian tuning	10.70	0.57
LightGBM	Leaf-wise, feature regularization	10.61 ⭐	10.90
Random Forest	Bagging	DNF*	-

*DNF: Did Not Finish (computational constraints)

Neural Networks

Model	Architecture	Status
MLP	128-64 units, tanh activation	Failed to converge

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::BackgroundTitle{title="Results Summary"} ::

Model Comparison

RMSE Performance (Lower is Better)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Linear Mixed-Effects (LMM5)     8.38 ████████████████████ Best Linear
Linear Mixed-Effects (LMM4)     8.41 ███████████████████
Linear Mixed-Effects (Baseline) 8.77 ██████████████████
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
LightGBM                       10.61 ███████████████ Best Non-Linear
XGBoost                        10.70 ██████████████
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
OLS (with interactions)        11.26 █████████████
Lasso                          12.03 ███████████
OLS (baseline)                 12.01 ███████████
Ridge                          12.48 ██████████
PLS                            12.79 █████████
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Key Findings

1. Best Linear Model: LMM with Random Slopes (RMSE = 8.38)

   • Captures asset-specific volatility sensitivities
   • Includes quadratic terms for convexity effects

2. Best Non-Linear Model: LightGBM (RMSE = 10.61)

   • Superior generalization vs XGBoost
   • Feature regularization prevents overfitting

3. Interpretability Insights (SHAP Analysis):

   • realized_vol_mid dominates (57% of gain)
   • Volatility clustering confirmed as primary driver
   • Non-linear regime switching in stress_spread

---

::BackgroundTitle{title="Repository Structure"} ::

PROJECT/
├── Projet_MRC_DANJOU_LEGRAND_MERIC_VONSIEMENS.qmd     # Main analysis (Quarto)
├── Projet_MRC_DANJOU_LEGRAND_MERIC_VONSIEMENS.html    # Rendered report
├── packages.R                                         # R dependencies installer
├── Train_ISF.csv                                      # Training data (~1.9M rows)
├── Test_ISF.csv                                       # Test data (~1.25M rows)
├── hat_y.csv                                          # Final predictions
├── README.md                                          # This file
└── results/
    ├── lightgbm/                                      # LightGBM model outputs
    └── xgboost/                                       # XGBoost model outputs

---

::BackgroundTitle{title="Getting Started"} ::

Prerequisites

• R ≥ 4.0
• Required packages (auto-installed via packages.R)

Installation

# Install all dependencies
source("packages.R")

Or manually install key packages:

install.packages(c(
  "tidyverse", "tidymodels", "caret", "glmnet",
  "lme4", "lmerTest", "xgboost", "lightgbm",
  "ranger", "pls", "shapviz", "rBayesianOptimization"
))

Running the Analysis

1. Open the Quarto document:

   # In RStudio
   rstudioapi::navigateToFile("Projet_MRC_DANJOU_LEGRAND_MERIC_VONSIEMENS.qmd")
   

2. Render the document:

   quarto::quarto_render("Projet_MRC_DANJOU_LEGRAND_MERIC_VONSIEMENS.qmd")
   

3. Or run specific sections interactively using the code chunks in the .qmd file

---

::BackgroundTitle{title="Technical Details"} ::

Data Split Strategy

• Chronological split at 80th percentile of dates
• Prevents look-ahead bias and data leakage
• Training: ~1.53M observations
• Validation: ~376K observations

Hyperparameter Tuning

• Method: Bayesian Optimization (Gaussian Processes)
• Acquisition: Expected Improvement (UCB)
• Goal: Maximize negative RMSE

Evaluation Metric

Exponential RMSE on original scale:

RMSE_{real} = \sqrt{\frac{1}{n} \sum_{i=1}^{n} \left( \exp(\hat{y}_{\log, i}) - y_i \right)^2}

Models trained on log-transformed target for variance stabilization.

---

::BackgroundTitle{title="Key Concepts"} ::

Financial Theories Applied

1. Volatility Clustering – Past volatility predicts future volatility
2. Variance Risk Premium – Spread between implied and realized volatility
3. Fear Gauge – Put-call ratio as sentiment indicator
4. Mean Reversion – Volatility tends to return to long-term average
5. Liquidity Premium – Illiquid assets command higher volatility

Statistical Methods

• Panel data modeling with fixed and random effects
• Principal Component Analysis (PCA)
• Bayesian hyperparameter optimization
• SHAP values for model interpretability

---

::BackgroundTitle{title="Authors"} ::

Team:

• Arthur DANJOU
• Camille LEGRAND
• Axelle MERIC
• Moritz VON SIEMENS

Course: Classification and Regression (M2) Academic Year: 2025-2026

---

::BackgroundTitle{title="Notes"} ::

• Computational Constraints: Some models (Random Forest, MLP) failed due to hardware limitations (16GB RAM, CPU-only)
• Reproducibility: Set seed = 2025 for consistent results
• Language: Analysis documented in English, course materials in French

---

::BackgroundTitle{title="References"} ::

Key R packages used:

• tidymodels – Modern modeling framework
• glmnet – Regularized regression
• lme4 / lmerTest – Mixed-effects models
• xgboost / lightgbm – Gradient boosting
• shapviz – Model interpretability
• rBayesianOptimization – Hyperparameter tuning