Numerical Integration Quant Interview Guide
Numerical integration quant interview guide for grids, quadrature, approximation error, dimensionality, Monte Carlo comparison, and examples.
Candidates approximating expectations, areas, payoffs, and model quantities.
Integration approximates accumulation
Numerical integration estimates an area, expectation, or weighted payoff when an analytic integral is unavailable or inconvenient.
Grid choice controls error
Step size, quadrature rule, smoothness, and boundary behavior affect accuracy. A finer grid is not always enough if the integrand has sharp features.
Concrete example
To approximate expected payoff under a distribution, evaluate payoff times density on a grid and sum weighted values, then check convergence as the grid changes.
Dimensionality changes the method
Grid integration becomes expensive in high dimensions, where Monte Carlo or quasi-Monte Carlo ideas may be more practical.
Common mistakes
Candidates often return one approximate number without error checks. Strong answers mention convergence, bounds, and why the chosen method fits the dimension.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.