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Stochastic Models
Stochastic Learning StudioBeta
1.1Symmetric Random Walk1.2Rescaled Random Walk1.3Standard deviation1.4Central Limit Theorem2.1Defining the Wiener process2.2Increment explorer2.3Joint distributions and covariance3.1Running maximum and minimum3.2Reflection principle3.3First passage times3.4Hitting probabilities
Brownian MotionBeta
Geometric Brownian MotionBeta
Itô ProcessBeta
Monte CarloBeta
Curriculum/Chapter 1 · Discrete to Continuous/1.3
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1.3

Standard deviation

How fast does the typical deviation of a random walk grow with time?

Goal: estimate the spread from more and more paths and watch the empirical standard deviation settle onto the ±√n envelope.

Simulation controls

Simulated pathsAdd paths: the dashed empirical std moves closer and closer to ±√n.
5
2101001000
250
50500
7
19999
Axis scale
×4
216
Predict first

Use ×4 as many steps. By what factor does the typical spread of the walk grow?

Empirical vs theoretical std

Empirical std at n = 250
20.81
√n at n = 250
15.81
Max relative gap
55.9%
Fitted log–log slope (theory: 0.5)
0.524

More paths give a sharper estimate: the worst gap to √n shrinks roughly like 1/√(paths), and the fitted slope approaches 0.5.

Path cloud: ±√n and ±empirical std
5 paths
  • ±√n (theoretical std)
  • ±empirical std

The green ±√n curve is the theoretical standard deviation; the dashed curve is the std measured from the cloud at every step. With few paths the estimate is noisy; add paths and it settles onto √n.

Typical deviation vs number of steps
Linear
Empirical std√n law

On log–log axes every power law is a straight line whose slope is the exponent. The √n law has slope 1/2.