Quantitative Risk Management uses mathematical models, statistical analysis, and data-driven approaches to measure, monitor, and manage trading and investment risks. This approach employs techniques including Value at Risk (VaR), stress testing, correlation analysis, and Monte Carlo simulations to quantify potential losses and optimize risk-return profiles.

Quantitative risk management provides objective, systematic approaches to risk assessment that complement qualitative judgment and experience. These methods help identify portfolio concentrations, tail risks, and optimal hedging strategies. Understanding quantitative risk concepts helps implement robust risk management frameworks and make informed decisions about risk tolerance and capital allocation.

Real-world example: A trading firm uses VaR models to determine that their portfolio has a 5% probability of losing more than $2 million in a single day, helping set position limits and capital reserves for risk management purposes.