VaR (Value-at-Risk) Calculator

Calculate VaR using the Interactive Dashboard: https://var-calculator.streamlit.app

This module provides an easy-to-use Python class, VaR, for calculating Value-at-Risk (VaR) using three different methods: Historical, Parametric, and Monte Carlo simulation.

🚀 Features:

1. Historical VaR Calculation: This method uses the actual distribution of historical returns to estimate VaR.
2. Parametric VaR Calculation: Also known as the variance-covariance method. It assumes returns are normally distributed.
3. Monte Carlo Simulation: Generates a large number of random portfolio returns and then determines an empirical distribution.

📦 Dependencies:

• yfinance: To fetch historical stock data.
• numpy: For numerical operations.
• matplotlib: For visualization.

📖 How to Use:

Initialize the VaR class with the following parameters:

• ticker: List of tickers of assets in the portfolio.
• start_date, end_date: Start and end dates for historical data fetch.
• rolling_window: Rolling window size for historical VaR.
• confidence_level: Confidence level for VaR calculation (e.g., 0.95 for 95%).
• portfolio_val: Total portfolio value.
• simulations: Number of simulations for Monte Carlo.

var_model = VaR(ticker=['AAPL', 'MSFT'], 
                start_date='2020-01-01', 
                end_date='2022-01-01', 
                rolling_window=252, 
                confidence_level=0.95, 
                portfolio_val=1000000, 
                simulations=1000)