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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
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Curriculum/Chapter 2 · Brownian Motion Fundamentals/2.3
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2.3

Joint distributions and covariance

How strongly are the values of Brownian motion at two different times related?

Goal: measure that Cov(W(s), W(t)) = min(s, t), and see that the part independent of W(s) is exactly the increment.

Simulation controls

Time window [s, t]Also adjustable on the chart: drag the shaded window or its edges
0.30
0.050.95
0.70
0.11
1,500
503000
7
19999
Predict first

For the current window, what is Cov(W(0.30), W(0.70))?

Covariance matrix

0.300theory 0.300
?theory ?
?theory ?
0.701theory 0.700
Empirical correlation
?
Theoretical correlation
?

Predict the covariance first, then reveal to compare it with the empirical and theoretical values.

Try this

  1. Drag t towards s: what happens to the ellipse, and why?
  2. Switch the y-axis to W(t) − W(s): where does the tilt go?
  3. Drag the slice (the pink handle): how does the conditional law of W(t) depend on W(s) — and how does it not?
Two times: s and t

Drag the shaded window to slide it, its edges to resize. The coloured paths reappear as same-coloured points on the joint chart.

Joint distribution of (W(s), W(t))

W(s) = 0.80 ⇒ W(t) ~ N(0.80, 0.40). The dashed line is E[W(t) | W(s)] = W(s): the best guess is where you are now. Wherever you drag the slice, the conditional sd stays √(t − s).