La Vecchia, Davide Trojani, Fabio 2010-01-25 We develop infinitesimally robust statistical procedures for the general diffusion processes. We first prove the existence and uniqueness of the times-series influence function of conditionally unbiased M-estimators for ergodic and stationary diffusions, under weak conditions on the (martingale) estimating function used. We then characterize the robustness of M-estimators for diffusions and derive a class of conditionally unbiased optimal robust estimators. To compute these estimators, we propose a general algorithm, which exploits approximation methods for diffusions in the computation of the robust estimating function. Monte Carlo simulation shows a good performance of our robust estimators and an application to the robust estimation of the exchange rate dynamics within a target zone illustrates the methodology in a real-data application. application/pdf https://susi.usi.ch/global/documents/318381 https://n2t.net/ark:/12658/srd1318381 https://susi.usi.ch/documents/318381/files/trojani_JASA_2010.pdf eng info:eu-repo/semantics/altIdentifier/doi/10.1198/jasa.2010.tm08383 info:eu-repo/semantics/altIdentifier/ark/12658/srd1318381 info:eu-repo/semantics/openAccess License undefined Journal of the american statistical association. - American statistical association. - 2010, vol. 105, no. 490, p. 703–712 Diffusion processes Eigenexpansion infinitesimal generator influence function m-estimators saddlepoint approximation info:eu-repo/classification/udc/33 Infinitesimal robustness for diffusions http://purl.org/coar/resource_type/c_6501