In the previous blog post, we explored the Parametric Method for estimating Value at Risk (VaR). While the parametric method offers the advantage of optimal computational efficiency, it relies on strict assumptions, particularly that returns follow a specific distribution (e.g., normal distribution). For complex portfolios, nonlinear instruments, and scenarios where flexibility and precision are critical, the parametric method may not be suitable. In such … Continue reading The Monte Carlo Method of VaR Estimation →