ASSOCIATED A-TYPE SUBALKALINE AND HIGH-K CALC-ALKALINE GRANITES IN THE ITU GRANITE PROVINCE, SOUTHEASTERN BRAZIL: PETROLOGICAL AND TECTONIC SIGNIFICANCE

The 590-580 Ma Itu Granite Province (IGP) is a roughly linear belt of post-orogenic granite plutons similar to 60 km wide extending for some 350 km along the southern edge of the Apia-Guaxupe Terrane in southeastern Brazil. Typical components are subalkaline A-type granites (some with rapakivi texture) that crystallized at varied, but mostly strongly oxidizing conditions, and contrast with a coeval association of also oxidized high-K calc-alkaline granites in terms of major (e. g., lower Ca/Fe) and trace elements (higher Nb, Y, Zr). Mantle-derived magmas (such as those forming the LILE-rich Piracaia Monzodiorite, with epsilon(Nd(t)) = -7 to -10, (87)Sr/(86)Sr((t)) = 0.7045-0.7055) are inferred to derive from enriched subcontinental lithosphere modified during previous subduction, and may have played a role in the generation of the A-type granites, adding melts or fluids or both to the lower crust from which the latter were generated. The IGP is interpreted as a reflection of crust uplift and increased heat flux during ascent of hot, less dense asthenosphere after continental collision, probably reflecting breakoff of an oceanic slab coeval to the right-lateral accretion of a terrane related to the Mantiqueira Orogenic System.

Systematic risk estimation in symmetric models

The aim of this article is to discuss the estimation of the systematic risk in capital asset pricing models with heavy-tailed error distributions to explain the asset returns. Diagnostic methods for assessing departures from the model assumptions as well as the influence of observations on the parameter estimates are also presented. It may be shown that outlying observations are down weighted in the maximum likelihood equations of linear models with heavy-tailed error distributions, such as Student-t, power exponential, logistic II, so on. This robustness aspect may also be extended to influential observations. An application in which the systematic risk estimate of Microsoft is compared under normal and heavy-tailed errors is presented for illustration.