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PDE Methods

Translate no-arbitrage dynamics into boundary-value problems.

Reviewed 2026-08-10TheQuantBateman ResearchReading note
01Intuition

Define the numerical question and error budget.

Treat the method as an approximation with a measurable error budget. Translate no-arbitrage dynamics into boundary-value problems. Inputs, convergence checks and failure conditions belong beside the output.

ONE-LINE DEFINITION

Translate no-arbitrage dynamics into boundary-value problems.

02Mathematics

Specify the estimator or discretization.

∂tV+(r−q)S∂SV+12σ2S2∂SSV−rV=0\partial_tV+(r-q)S\partial_SV+\tfrac12\sigma^2S^2\partial_{SS}V-rV=0
Notation and units

Decimal rates and volatilities, year-fraction time and continuous compounding unless stated otherwise.

03Assumptions

Expose convergence and stability conditions.

01

The numerical target, discretization and stopping rule must be fixed before comparing outputs.

02

Convergence is assessed against bias, variance or residual tolerances rather than visual smoothness.

03

Finite precision, boundary treatment and input conditioning can dominate model error.

“An unstated convention is a future reconciliation break.”— THEQUANTBATEMAN
04Market use

Connect controls to an observable output.

PDE Methods supports Foundations pricing or risk when the numerical target, tolerance and benchmark are explicit. Production use requires convergence evidence and reproducible inputs.

Intuition→Mathematics→Implementation→Desk risk
05Desk view
FRONT OFFICE VIEW

Monitor bias, variance, and failure modes.

Report the Foundations number with its convergence evidence. A stable-looking output can still carry discretization bias or an ill-conditioned input.

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06Related

Choose the next implementation dependency.