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@MattMacarty *Master the calculation of Value at Risk (VaR), the essential statistical measure of downside risk.* In this advanced financial modeling tutorial, we demonstrate the *Parametric VaR* method using the Normal Distribution to estimate the maximum potential loss over a specific time horizon and confidence level. We cover both single-security VaR and the more complex multi-asset VaR, using portfolio volatility as the core input. --- ⏱️ Video Chapters / Timestamps 0:00 - Introduction: What is Value at Risk (VaR)? 0:31 - Non-Parametric vs. Parametric VaR methods 1:00 - The required inputs: Portfolio Value, Volatility, Time Horizon, and Confidence Level 1:47 - The problem with VaR: What happens beyond the confidence level? 2:42 - *Example 1: Single-Security VaR (Apple)* 3:30 - Using the *`NORMS.INV`* function to find the confidence Z-score (2.33 standard deviations) 4:05 - Calculating Single-Day and Multi-Day VaR 5:15 - *Example 2: Multi-Security VaR (Apple & Gold)* 5:46 - Calculating Portfolio Volatility for two securities (Manual formula) 6:55 - The full portfolio volatility formula explained 7:35 - Risk reduction benefits from diversification 7:52 - Final Multi-Asset VaR Calculation 8:45 - The need for Matrix Math in larger portfolios (10+ assets) --- 🔎 Summary of Skills Learned *Theory:* Understand the statistical assumptions of Parametric VaR (Normal Distribution). *Functions:* Learn to use the `NORMS.INV` function in conjunction with the standard deviation. *Application:* Calculate a 99% confidence VaR for both a single stock and a diversified portfolio, while correctly adjusting the volatility for the time horizon using the square root rule [04:17]. --- Get vidIQ to grow your channel faster! 🚀 https://vidiq.com/alphabench