We provide an overview of some of the major Monte Carlo approaches for para metric sensitivities in stochastic chemical system s. The efficiency of a Monte Carlo approach depen ds in part on the variance of the estimator. It ha s been numerically observed that in several examp les\, that the finite difference (FD) and the (re gularized) pathwise differentiation (RPD) methods tend to have lower variance than the Girsanov Tra nformation (GT) estimator while the latter has th e advantage of being unbiased. We present a theore tical explanation in terms of system volume asymp totics for the larger variance of the GT approach when compared to the FD methods. We also present an analysis of efficiency of the FD and GT method s in terms of desired error and system volume. LOCATION:Seminar Room 1\, Newton Institute CONTACT:INI IT END:VEVENT END:VCALENDAR