Risk Management System

A framework for analyzing portfolio risk with metrics like VaR, CVaR, and Maximum Drawdown. Includes a stress test (COVID-19 crash, and Monte Carlo simulations to assess diversification and portfolio resilience.

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Features

• Risk Metrics:

  • Value at Risk (VaR): Historical, Gaussian, and Modified Gaussian methods.
  • Conditional VaR (CVaR): Historical and Gaussian methods to evaluate tail risks.
  • Maximum Drawdown (MDD): Identifies the worst portfolio declines and recovery times.

• Stress Testing:

  • Analyze portfolio performance during the COVID-19 crash (Feb–Mar 2020).
  • Simulate hypothetical market shocks, such as a 10% equity crash.

• Monte Carlo Simulations:

  • Generate thousands of random return paths to assess portfolio risk under various scenarios.

• Diversification Analysis:

  • Evaluate the impact of diversification on reducing portfolio risk using covariance and correlation matrices.

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Data

• Portfolio Composition:

  • AAPL (Tech)
  • JNJ (Healthcare)
  • XOM (Energy)
  • TLT (Bonds ETF)
  • GLD (Gold ETF)

• Portfolio Weights:

  • AAPL: 25%, JNJ: 20%, XOM: 25%, TLT: 15%, GLD: 15%.

• Historical Data:

  • Daily adjusted closing prices over 5 years (e.g., 2018–2023).

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Technologies

• Python Libraries:
  • pandas for data manipulation.
  • numpy for numerical operations.
  • matplotlib for visualizations.
  • scipy.stats for statistical analysis.

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Usage

1. Clone the repository:

   git clone https://github.com/your-username/risk-management-system.git
   

2. Install required dependencies::

   pip install -r requirements.txt
   

3. Run the Jupyter Notebook or script:

   jupyter main.ipynb
   
   

Future Enhancements

• Include machine learning models for risk prediction.
• Add support for multi-asset class portfolios (e.g., crypto, real estate).
• Automate data fetching using APIs like Yahoo Finance.t