For deterministic step functions, define directly from Brownian motion incrementswith the left endpoint of the first interval and outside the listed intervals. The printed endpoint list omits ; the intended convention is , usually .
Given finitely many observation times, refine the deterministic partition of an interval to include all observation times and all . Every is a linear combination of the same finite family of independent centered normal Brownian motion increments. Thus every linear combination of is normal, including possibly a degenerate constant: is a Gaussian process. By independence, only increments in the common interval contribute to the covariance, givingThe same calculation for two step functions proves, rather than assumes, the Itô isometryFor , choose deterministic step functions in the L2 space. This identity makes a Cauchy sequence in the L2 space; the Riesz-Fischer theorem defines by mean-square convergence, independently of the approximation. The covariance formula passes to the limit by the Cauchy-Schwarz inequality. For finitely many times and coefficients , the characteristic functions converge toso the extension remains a Gaussian process.
For completeness, the construction has a continuous version. Each approximating is a martingale, directly by the centered independent increments of Brownian motion; the Doob L2 maximal inequality givesTo prove the inequality used here without assuming Itô integral properties, a continuous martingale satisfies , by conditioning at the first crossing on finite deterministic grids and then using continuity; integrating in and using the Cauchy-Schwarz inequality yields . A sufficiently fast subsequence therefore converges uniformly almost surely on every compact time interval, giving the continuous version of the stochastic integral.
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