Value at Risk (VaR)

Parametric, Monte Carlo and Historical VaR methods and their pros and cons

Created by: Tim Glauner
Head of Summit Capital Markets Americas GSC Finastra

What Will I Learn?

• Understand VaR as a technique to estimate potential portfolio losses over a set period at a given confidence level​​​​.
• Parametric VaR: Learn the variance-covariance approach using mean return and volatility to estimate loss, with key assumptions and the covariance matrix​​​​.
• Monte Carlo VaR: Explore simulating risk scenarios to construct portfolio price distributions, covering steps from modeling to VaR computation​​​​.
• Historical VaR: Use historical market data to estimate potential losses, focusing on data requirements and the importance of clean, continuous data​​​​.
• Comparing VaR Methods: Compare Parametric, Monte Carlo, and Historical VaR, understanding their strengths, limitations, and suitability.

Requirements

• Basic Understanding of Finance
• Very basic Familiarity with Statistical Methods
• No programming experience required

Target audience

• Undergraduate Finance Students
• MBA Students
• Finance Professionals
• Risk Analysts
• Risk Analysts
• Quantitative Analysts
• Consultants
• Regulatory Compliance Officers
• Actuaries interested in Capital Markets risk management
• Continuing Education for Finance Professionals