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KDE Value-at-Risk Estimate

Hard · statistical_analysis · Quant Researcher interview question · kde, value_at_risk, non_parametric, density_estimation, tail_risk

Parametric VaR assumes a distribution (typically normal), which can severely underestimate tail risk for non-Gaussian return distributions. Kernel Density Estimation provides a non-parametric alternative that adapts to the empirical shape of the return distribution, capturing skewness, kurtosis, and multimodality without distributional assumptions. Task Implement kde_var_estimate(returns: list, confidence: float, bandwidth: float) to compute Value-at-Risk (VaR) using Gaussian Kernel Density Est