This project provides groundbreaking insights into how carbon price shocks differentially impact green versus brown Exchange-Traded Funds (ETFs). Using advanced econometric methods including causal forest analysis, event studies, and machine learning approaches, we demonstrate that traditional energy ETFs are 3x more sensitive to carbon price volatility than clean energy ETFs.
- Brown ETFs (XLE) exhibit 13.5% average treatment effects vs. 4.2% for Green ETFs (ICLN)
- LSTM models outperform GARCH by 30% in volatility forecasting
- EU carbon policy announcements create measurable, asymmetric market responses
- Clear market differentiation exists between sustainable and traditional energy investments
Quantify and explain the heterogeneous responses of green and brown ETFs to carbon price shocks, providing empirical evidence for climate-finance market segmentation.
| ETF | Name | Focus | Holdings | ESG Profile |
|---|---|---|---|---|
| ICLN | iShares Global Clean Energy ETF | Green/Renewable Energy | First Solar, SSE, Iberdrola, Vestas | Mixed (Low to High ESG) |
| XLE | Energy Select Sector SPDR | Traditional Energy | ExxonMobil, Chevron, ConocoPhillips | High to Severe ESG Risk |
- Carbon Prices: EU Emissions Allowances (EUA) futures
- Market Data: Daily ETF prices and returns (2018-2024)
- Control Variables: VIX, Brent oil, 10-year Treasury rates
- ESG Data: Morningstar sustainability ratings
- Policy Events: EU carbon legislation timeline
Shock Detection: |AR(5) residual| > 1.5σ threshold
Validation: Cross-referenced with EU policy announcements
- Heterogeneous Treatment Effects: Conditional Average Treatment Effects (CATE)
- Robustness: Bootstrap confidence intervals and placebo tests
- Innovation: Machine learning approach to traditional finance problems
Event Windows: [-10, +10] days around carbon shocks
Abnormal Returns: Risk-adjusted performance relative to market
Statistical Testing: Cross-sectional t-tests for significance
- Traditional: GARCH(1,1) with maximum likelihood estimation
- Modern: LSTM neural networks with dropout and regularization
- Performance: RMSE, MAE, and directional accuracy metrics
Model: ETF_return_t = α + β₁×carbon_shock_t + β₂×VIX_t + β₃×Brent_t + β₄×DGS10_t + ε_t
Estimation: OLS with heteroscedasticity-robust standard errors (HC1)
| Metric | ICLN (Green) | XLE (Brown) | Interpretation |
|---|---|---|---|
| Mean CATE | 4.19% | 13.49% | Brown ETFs 3x more sensitive |
| Regression Coefficient | 0.0849*** | 0.1757*** | Highly significant effects |
| R-squared | 3.8% | 7.2% | Better model fit for brown ETFs |
| Volatility RMSE | 0.226 | 0.164 | Lower forecasting errors |
Note: *** indicates p < 0.001
- $1 carbon price shock → 17.6 basis points increase in XLE returns
- Market asymmetry: Brown assets more exposed to carbon transition risks
- Policy transmission: EU regulations create measurable US market impacts
- Investment implications: Clear risk differentiation for ESG portfolios
- Post-financial crisis stability
- COVID-19 period included for market stress testing
- Recent policy impacts captured
- European debt crisis provides additional market stress
- Longer-term trends validate findings
- Consistent coefficient signs across periods
# Required Python packages
pandas >= 1.3.0 # Data manipulation
numpy >= 1.21.0 # Numerical computing
scikit-learn >= 1.0.0 # Machine learning (Causal Forest)
tensorflow >= 2.6.0 # LSTM models
statsmodels >= 0.13.0 # Econometric analysis
matplotlib >= 3.4.0 # Visualization
seaborn >= 0.11.0 # Statistical plotting- Data Preparation:
03_Code/Main_Analysis/data_agg.ipynb - Main Analysis:
03_Code/Main_Analysis/Main_Code_Project.ipynb - Robustness Checks:
03_Code/Main_Analysis/Main_Code_Project_14Yr.ipynb - Visualization:
03_Code/Visualization/presentation_plots.ipynb
- Summary Statistics:
04_Results/Statistical_Analysis/Summary_Results/ - Regression Outputs:
04_Results/Statistical_Analysis/Regression_Results/ - Visualizations:
04_Results/Figures/Main_Figures/
- First comprehensive study comparing green/brown ETF responses to carbon shocks
- Novel application of causal forest methods to climate finance
- Machine learning integration with traditional econometric approaches
- Cross-Atlantic policy transmission evidence (EU → US markets)
- Portfolio Risk Management: Quantified climate transition risks
- ESG Investment Strategy: Data-driven green/brown asset allocation
- Policy Impact Assessment: Market response measurement tools
- Volatility Forecasting: Superior LSTM-based prediction models
- EU carbon policy creates measurable global market effects
- Market-based mechanisms effectively transmit climate signals
- Asset differentiation supports sustainable finance transition
- Risk disclosure needs differ across green/brown investments
# Causal Forest Implementation
from sklearn.ensemble import RandomForestRegressor
from causalml.inference.tree import CausalTreeRegressor
# LSTM Volatility Forecasting
model = Sequential([
LSTM(50, return_sequences=True, dropout=0.2),
LSTM(50, dropout=0.2),
Dense(1, activation='linear')
])- Heteroscedasticity-robust standard errors (HC1)
- Cross-sectional dependence testing
- Structural break analysis across time periods
- Placebo testing for causal identification
- Multiple time horizons (6-year vs 14-year samples)
- Alternative shock thresholds (1.0σ, 1.5σ, 2.0σ)
- Different event windows [-5,+5] to [-15,+15] days
- Subsample stability testing
- Data Dictionary: Variable definitions and data sources
- Analysis Workflow: Step-by-step methodology
- Technical Guide: Implementation details and troubleshooting
- Version-controlled analysis pipeline
- Relative file paths for cross-platform compatibility
- Documented dependencies and environment specifications
- Complete data provenance tracking
- Statistical validation of all model assumptions
- Cross-validation for machine learning components
- Sensitivity analysis for key parameters
- Independent verification of results
- Deep learning approaches to volatility clustering
- Network analysis of cross-asset contagion effects
- High-frequency intraday shock transmission
- International markets comparison (EU, Asia, US)
- Central bank climate stress testing
- Regulatory impact assessment frameworks
- Transition pathway modeling for different sectors
- Carbon border adjustment market effects
- Real-time risk monitoring systems
- Dynamic hedging strategies for climate risks
- ESG factor integration in portfolio optimization
- Climate scenario analysis for institutional investors
📧 Email: aditya.rohatgi11@gmail.com
💼 LinkedIn: https://linkedin.com/in/adityarohatgi
🔗 GitHub: https://github.com/adityarohatgi11
For questions regarding methodology, data access, or collaboration opportunities, please feel free to reach out.