🔥 FIRE EU Analysis

Quantitative FIRE analysis for European markets

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🧩 Project Overview

This repository contains the code, processed data (when allowed), and statistical analyses developed for the thesis "Retirement-oriented wealth management: the 4% rule and the FIRE (Financial Independence, Retire Early) movement. A European and Italian case study."

The goal is to assess the sustainability of withdrawal rate strategies—especially the 4% rule—using historical simulations, stochastic modelling, and risk metrics applied to European markets.

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📈 Key Results

Summary of Monte Carlo simulation results (100,000 runs, 30-year horizon, block bootstrap):

Success Rates by Index and Withdrawal Rate (60/40 with Bund)

Equity Index	3% WR	3.5% WR	4% WR
MSCI World	95.8%	89.1%	78.3%
MSCI ACWI	95.3%	88.1%	77.0%
MSCI Europe	90.1%	79.2%	65.0%
MSCI EMU	86.2%	74.7%	60.9%

Bond Strategy Comparison (60% Equity, 4% WR)

Bond Type	MSCI World	MSCI Europe	Advantage
Bund	78.3%	65.0%	Baseline
BTP	80.5%	67.9%	+2%
Bund+BTP Mix	79.9%	66.8%	+1.5%

Key findings:

1. The 4% rule doesn't work well in Europe - success rates are 65-80% vs US ~95%
2. 3-3.5% WR is more appropriate for European investors
3. Global diversification matters - World/ACWI outperform Europe/EMU by 10-15%
4. BTP outperforms Bund at higher WRs due to yield advantage (+2%)
5. Allocation matters less than WR - 60/40 to 80/20 have similar outcomes
6. Home bias is costly - European-only portfolios have significantly lower success rates

For complete results, see ANALYSIS_REGISTRY.md.

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📖 Documentation

Document	Description
src/README.md	Complete list of Jupyter notebooks with descriptions and naming conventions
ANALYSIS_REGISTRY.md	Summary table of simulation results (success rates, final values, depletion years)
ANALYSIS_REGISTRY.csv	Same data in CSV format for further analysis
plots/README.md	Documentation for 34 analysis plots with key insights

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📁 Repository Structure

fire-eu-analysis/
│
├── data/                    # Raw and processed datasets
│   ├── msci/                # MSCI index data
│   ├── bdi/                 # Banca d'Italia (BTP yields)
│   ├── bund/                # Bundesbank (Bund yields)
│   ├── oat/                 # French OAT yields
│   ├── eurostat/            # HICP inflation data
│   ├── istat/               # Italian inflation (FOI, NIC)
│   └── stoxx/               # STOXX Europe 600 data
│
├── src/                     # Jupyter notebooks
│   ├── 00xx_*.ipynb         # Data extraction and preprocessing
│   ├── 01xx_*.ipynb         # MSCI World simulations (4% WR)
│   ├── 02xx_*.ipynb         # MSCI ACWI simulations (4% WR)
│   ├── 03xx_*.ipynb         # STOXX Europe 600 simulations (4% WR)
│   ├── 04xx_*.ipynb         # MSCI Europe simulations (4% WR)
│   ├── 05xx_*.ipynb         # MSCI EMU simulations (4% WR)
│   ├── 06xx_*.ipynb         # MSCI World simulations (3% WR)
│   ├── 07xx_*.ipynb         # MSCI World simulations (3.5% WR)
│   ├── 08xx_*.ipynb         # MSCI ACWI simulations (3% WR)
│   ├── 09xx_*.ipynb         # MSCI ACWI simulations (3.5% WR)
│   ├── 10xx_*.ipynb         # MSCI Europe simulations (3% WR)
│   ├── 11xx_*.ipynb         # MSCI Europe simulations (3.5% WR)
│   ├── 12xx_*.ipynb         # MSCI EMU simulations (3% WR)
│   ├── 13xx_*.ipynb         # MSCI EMU simulations (3.5% WR)
│   └── tools/               # Utility scripts
│       ├── buildtable.py    # Generate ANALYSIS_REGISTRY.md/.csv
│       └── plots.py         # Generate 34 analysis plots
│
├── plots/                   # 34 generated analysis plots
│   ├── 01-08_*.png          # General analysis (success rates, heatmaps)
│   ├── 09-14_*.png          # Depletion analysis (when portfolios fail)
│   ├── 15-20_*.png          # 4% WR specific analysis
│   ├── 21-26_*.png          # 3% WR specific analysis
│   ├── 27-32_*.png          # 3.5% WR specific analysis
│   └── 33-34_*.png          # Optimal bond strategy analysis
│
├── fire.sh                  # Podman container management script
├── ANALYSIS_REGISTRY.md     # Auto-generated results summary
├── ANALYSIS_REGISTRY.csv    # Results in CSV format
└── README.md

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▶️ How to Run the Project (Podman Required)

The recommended way to start the analysis environment is through the Podman-based script:

Requirements

• Podman installed on the host
• Optional: GPU support (the script auto-detects it)

Start the project

./fire.sh start              # Auto-detect GPU/CPU
./fire.sh start --gpu        # Force GPU mode
./fire.sh start --cpu        # Force CPU mode
./fire.sh stop               # Stop container

The script will:

• Detect whether the host supports GPU
• Start the appropriate container (GPU-enabled or CPU-only)
• Launch Jupyter Lab inside the container
• Expose the notebook environment on localhost

Utility Scripts

Run inside the container or via podman exec:

# Generate ANALYSIS_REGISTRY.md and .csv from notebook outputs
python src/tools/buildtable.py

# Generate analysis plots
python src/tools/plots.py

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🔬 Simulation Methodology

Block Bootstrap Monte Carlo

The simulations use block bootstrap methodology which preserves temporal structure and correlations in financial data:

1. Sample consecutive blocks of historical data (6-month blocks)
2. Randomly concatenate blocks to create simulated 30-year scenarios
3. Maintain serial correlation and regime persistence

Simulation Parameters

Parameter	Value
Number of simulations	100,000
Time horizon	30 years
Block size	6 months
Historical data	2000-2025
Withdrawal rates tested	3%, 3.5%, 4%
Inflation adjustment	HICP Euro Area (lagged)
Rebalancing	Annual

Portfolio Configurations

• Equity indices: MSCI World, MSCI ACWI, MSCI Europe, MSCI EMU
• Bond instruments: German Bund 10Y, Italian BTP 10Y, mixed portfolios
• Allocations: 60/40, 70/30, 80/20, 90/10, 100/0

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📚 Data Sources

The project uses:

• MSCI – equity indices (World, ACWI, Europe, EMU)
• Eurostat – HICP inflation data
• ISTAT – Italian inflation (FOI, NIC)
• Bundesbank – German Bund 10Y yields
• Banca d'Italia – Italian BTP 10Y yields
• Banque de France – French OAT 10Y yields

Note: Some datasets cannot be included due to licensing restrictions. Only scripts and derived data (when permitted) are stored in the repository.

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🛠️ Technologies

• Python 3.12+
• Main dependencies:
  • pandas, numpy, scipy – data manipulation and statistics
  • matplotlib, seaborn – visualization
  • statsmodels – statistical models
  • joblib – parallel processing
  • nbformat – notebook parsing
• Podman for reproducible execution environment
• Optional GPU acceleration for simulations

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🤝 Contributing

Contributions are welcome, especially improvements to clarity, structure, reproducibility, or data pipelines.

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📄 License — MIT License

This project is released under the MIT License, a permissive and widely used open-source license. It allows reuse, modification, distribution, and commercial usage with minimal restrictions.

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👤 Author

Amedeo Salvati Universitas Mercatorum – BSc in Statistics and Big Data