Value at Risk (VaR) Analysis – Multi-Asset Portfolio

A multi-asset VaR model built in Excel and Python, demonstrating portfolio risk aggregation, covariance-based analysis, and reproducible data workflows.

📄 Blog write-up: Happy Bytes

Overview

This project estimates portfolio risk using both Historical VaR and Parametric VaR (Variance-Covariance method) for a diversified multi-asset portfolio.

The analysis is implemented in both Excel and Python to demonstrate financial modeling and data-driven risk analysis.

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Portfolio

Asset	Weight
SPY	40%
QQQ	25%
AGG	25%
GLD	10%

• Portfolio Value: $100,000
• Data: Daily returns (~1 year)

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Methodology

1. Historical VaR

• Based on empirical distribution of historical returns
• 1-day VaR calculated at:
  • 90%
  • 95%
  • 99%

2. Parametric VaR (Variance-Covariance)

• Assumes normally distributed returns
• Uses:
  • Portfolio variance and standard deviation
  • Z-scores
  • Square-root-of-time scaling

Calculated for:

• 1-day horizon
• 10-day horizon (95%)

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Key Results

• Portfolio Daily Volatility: ~0.63%
• 1-Day Historical VaR (95%): ~$1,044
• 10-Day Parametric VaR (95%): ~$3,268

Interpretation:

Under normal market conditions, the portfolio is expected to lose no more than approximately $3,268 over a 10-day period with 95% confidence.

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Visualizations

These visualizations provide additional context on portfolio volatility and return distribution:

Distribution of Portfolio Returns

Portfolio Returns Over Time

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Files

All files can be viewed or downloaded directly from this repository.

• notebook/VaR_Analysis.ipynb
  Python notebook containing full analysis, calculations, and charts

• excel/Portfolio_VaR.xlsx
  Excel-based VaR model (Historical and Parametric methods)

• data/Price_Data.csv
  Input dataset (daily asset returns)

• output/var_summary.csv
  Output summary generated from Python analysis

• images/
  Visualizations used in this README

• README.md
  Project documentation

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How to Run (Python)

1. Open VaR_Analysis.ipynb in Google Colab: https://colab.research.google.com/

2. Run the upload cell and upload Price_Data.csv

3. Run all cells from top to bottom

4. Outputs will include:

   • Covariance matrix
   • Portfolio variance and standard deviation
   • Historical VaR
   • Parametric VaR
   • Charts
   • var_summary.csv export

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Outputs

• Portfolio covariance matrix
• Portfolio variance and volatility
• Historical VaR (1-day)
• Parametric VaR (1-day and 10-day)
• Return distribution histogram
• Time series of portfolio returns

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Assumptions & Limitations

• Parametric VaR assumes normally distributed returns
• Historical VaR depends on the selected sample period
• Square-root-of-time scaling assumes independent returns
• Model does not fully capture extreme tail risk

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Tools Used

• Python (pandas, numpy, matplotlib)
• Excel (financial modeling)
• Google Colab

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Notes

This project demonstrates:

• Portfolio risk aggregation using covariance
• Comparison of VaR methodologies
• Practical implementation of financial risk concepts in both Excel and Python

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⚠️ Disclaimer

This project is a simplified portfolio risk analysis for educational and portfolio purposes only. The data and assumptions are illustrative and do not constitute investment advice.

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📬 Contact

If you're interested in financial risk, data analysis, or finance–technology crossover roles, feel free to connect with me on LinkedIn.

Feedback and discussion are welcome. Thank you for reviewing this project. 🙏

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