Prometheus Posted December 23, 2014 Posted December 23, 2014 (edited) So i want to find the expectation of a geometric Brownian motion: [math]E[e^{K^TW_t}][/math] Where K is a constant vector and [math]W_t[/math] is a vector of normal Brownian motions, both of length n. I assume that as [math]K^TW_t[/math] is a scalar I can just proceed in a similar fashion as the univariate case to get: [math]e^{\frac{1}{2}K^TK}[/math] But is it that simple or am i missing something as i suspect? As always, help very much appreciated. Edited December 23, 2014 by Prometheus
Prometheus Posted January 2, 2015 Author Posted January 2, 2015 The above is nearly correct, just a small typo. The bit missing from my understanding was simply realising that [latex] K^TW_t = ||K||^2 [/latex] Now stuck on a related question. I now have [latex] e^{iK^TdW_t}[/latex], where [latex]dW_t[/latex] is an infinitesimal Brownian increment. I want to find the Taylor expansion of this - I would know how to proceed in the univariate case, but i don't understand the multivariate case. Does anyone have any hints or good links?
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