Kernel Density Estimation of Returns - Quant Researcher Interview Question

Problem Description

Asset returns frequently exhibit non-normal characteristics like fat tails and skewness, rendering standard parametric assumptions insufficient for accurate risk modeling. Kernel Density Estimation (KDE) offers a robust non-parametric alternative by estimating the Probability Density Function (PDF) directly from historical data using smoothing kernels. This technique is essential for capturing the true distribution of market data without imposing rigid theoretical constraints. Task Implement th