Interpolation Quant Interview Guide
Interpolation quant interview guide for linear interpolation, curves, surfaces, extrapolation, missing values, examples, and mistakes.
Candidates working with yield curves, volatility surfaces, and numerical approximations.
Interpolation fills between known points
Interpolation estimates values inside the range of observed data. It is common for curves, surfaces, missing values, and numerical grids.
Extrapolation is riskier
Estimating outside the observed range relies more heavily on assumptions. In interviews, say when you are extrapolating and why that may be fragile.
Concrete example
A yield curve may quote certain maturities, while pricing needs an intermediate maturity. Linear interpolation is simple but may not preserve all curve properties.
Method choice affects shape
Linear interpolation, splines, and model-based curves can produce different smoothness, monotonicity, and arbitrage properties. State the tradeoff.
Common mistakes
Candidates often interpolate mechanically without checking units, tenor, monotonicity, or whether the requested point lies outside the data range.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.