Quant Sandbox
DerivativesPortfolio & RiskFixed IncomeCorporate FinanceProbability & MeasureStochastic ModelsMarket MicrostructureGuide
Stochastic Models
Random Walk
Random Walk labSymmetric Random WalkRescaled Random WalkStandard deviationCentral Limit Theorem
Brownian Motion
Brownian labDefining the Wiener processIncrement explorerJoint distributions and covarianceBrownian transformations and invarianceQuadratic variationBrownian bridgeRunning maximum and minimumReflection principleFirst passage timesHitting probabilities
Geometric Brownian Motion
LabDefinition: ABM vs GBMEuler–MaruyamaMean reversionJumpsCorrelated motions
Itô Process
LabItô integralItô's lemmaMartingalesGirsanov
Monte Carlo
LabVariance reductionFeynman–Kac
Connected Guides
  • Monte Carlo option pricing
  • Geometric Brownian motion
  • Project risk: sensitivity, scenarios and Monte Carlo NPV
  • Random walks and Brownian motion
  • The law of large numbers and the central limit theorem
  • Kolmogorov equations and the Feynman–Kac formula
  • Simulating SDEs: the Euler–Maruyama scheme
  • Correlated Brownian motions and multi-asset models
  • Jumps and the Merton jump-diffusion model
  • Exotic options: barriers, Asians and digitals

© 2026 Quant Sandbox · A study aid for quantitative finance

Privacy & dataNo cookies

This site does not use cookies and asks for no personal data. It keeps a few small settings in your browser's local storage — your chosen theme and language, the sidebar layout, and your progress and answers in the Guides — so it remembers them on your next visit. This data never leaves your device; clearing your browser storage removes it. Inputs you change can also be captured in the page URL, so you can share or bookmark a specific setup. The site uses Vercel Web Analytics, a privacy-friendly, cookieless service that records only anonymous, aggregated usage (such as page views) without identifying you or tracking you across other sites.

Variance reduction

Four estimators of the same call price from the same normal draws: the antithetic and control-variate tricks shrink the confidence band without adding a single simulation.

Parameters

100
10300
100
10300
1.00
0.253
3.0%
00.1
20.0%
0.050.6
5000
20020000
7
1100
Metrics
Black–Scholes price
9.4134
Running estimates and 95% bands

95% confidence band (±1.96·SE)

PlainAntitheticControl variateBothBlack–Scholes
What the tricks buy
EstimatorStandard errorVariance reductionPlain draws neededSaved
● Plain0.2006×1.05,0000
● Antithetic0.1491×1.89,0504,050
● Control variate0.0830×5.829,24224,242
● Both0.0419×22.9114,384109,384

Both: each Z and −Z pair is averaged first (antithetic), then the discounted S_T of the pairs serves as the control variate. Draws saved = the plain draws needed for the same standard error minus the draws used.