Quantifying the contamination by old main-sequence stars in young moving groups: the case of the Local Association

Context. The associations and moving groups of young stars are excellent laboratories for investigating stellar formation in the solar neighborhood. Previous results have confirmed that a non-negligible fraction of old main-sequence stars is present in the lists of possible members of young stellar kinematic groups. A detailed study of the properties of these samples is needed to separate the young stars from old main-sequence stars with similar space motion, and identify the origin of these structures. Aims. Our intention is to characterize members of the young moving groups, determine their age distribution, and quantify the contamination by old main-sequence stars, in particular, for the Local Association. Methods. We used stars possible members of the young (~10-650 Myr) moving groups from the literature. To determine the age of the stars, we used several suitable age indicators for young main sequence stars, i.e., X-ray fluxes from the Rosat All-sky Survey database, photometric data from the Tycho-2, Hipparcos, and 2MASS database. We also used spectroscopic data, in particular the equivalent width of the lithium line Li I λ6707.8 Å and H_α, to constrain the range of ages of the stars. Results. By combining photometric and spectroscopic data, we were able to separate the young stars (10-650 Myr) from the old (> 1 Gyr) field ones. We found, in particular, that the Local Association is contaminated by old field stars at the level of ~30%. This value must be considered as the contamination for our particular sample, and not of the entire Local Association. For other young moving groups, it is more difficult to estimate the fraction of old stars among possible members. However, the level of X-ray emission can, at least, help to separate two age populations: stars with <200 Myr and stars older than this. Conclusions. Among the candidate members of the classical moving groups, there is a non-negligible fraction of old field stars that should be taken into account when studying the stellar birthrate in the solar neighborhood. Our results are consistent with a scenario in which the moving groups contain both groups of young stars formed in a recent star-formation episode and old field stars with similar space motion. Only by combining X-ray and optical spectroscopic data is it possible to distinguish between these two age populations.

Bayesian estimation in generalized autoregressive conditional heteroskedasticity models

Esta tesis doctoral nace con el propósito de entender, analizar y sobre todo modelizar el comportamiento estadístico de las series financieras. En este sentido, se puede afirmar que los modelos que mejor recogen las especiales características de estas series son los modelos de heterocedasticidad condicionada en tiempo discreto,si los intervalos de tiempo en los que se recogen los datos lo permiten, y en tiempo continuo si tenemos datos diarios o datos intradía. Con esta finalidad, en esta tesis se proponen distintos estimadores bayesianos para la estimación de los parámetros de los modelos GARCH en tiempo discreto (Bollerslev (1986)) y COGARCH en tiempo continuo (Kluppelberg et al. (2004)). En el capítulo 1 se introducen las características de las series financieras y se presentan los modelos ARCH, GARCH y COGARCH, así como sus principales propiedades. Mandelbrot (1963) destacó que las series financieras no presentan estacionariedad y que sus incrementos no presentan autocorrelación, aunque sus cuadrados sí están correlacionados. Señaló también que la volatilidad que presentan no es constante y que aparecen clusters de volatilidad. Observó la falta de normalidad de las series financieras, debida principalmente a su comportamiento leptocúrtico, y también destacó los efectos estacionales que presentan las series, analizando como se ven afectadas por la época del año o el día de la semana. Posteriormente Black (1976) completó la lista de características especiales incluyendo los denominados leverage effects relacionados con como las fluctuaciones positivas y negativas de los precios de los activos afectan a la volatilidad de las series de forma distinta.