Evidence for non-Gaussianity in the CMB

In a recent Letter we have shown how COBE-DMR maps may be used to disprove Gaussianity at a high confidence level. In this report we digress on a few issues closely related to this Letter. We present the general formalism for surveying non-Gaussianity employed. We present a few more tests for systematics. We wonder about the theoretical implications of our result.Comment: Proceedings of the Planck meeting, Santender 9

The 4 Year COBE DMR data is non-Gaussian

I review our recent claim that there is evidence of non-Gaussianity in the 4 Year COBE DMR data. I describe the statistic we apply, the result we obtain and make a detailed list of the systematics we have analysed. I finish with a qualitative understanding of what it might be and its implications.Comment: Proceedings of Rome 3K conference, 5 pages, 3 figure

A Bayesian estimate of the skewness of the Cosmic Microwave Background

We propose a formalism for estimating the skewness and angular power spectrum of a general Cosmic Microwave Background data set. We use the Edgeworth Expansion to define a non-Gaussian likelihood function that takes into account the anisotropic nature of the noise and the incompleteness of the sky coverage. The formalism is then applied to estimate the skewness of the publicly available 4 year Cosmic Background Explorer (COBE) Differential Microwave Radiometer data. We find that the data is consistent with a Gaussian skewness, and with isotropy. Inclusion of non Gaussian degrees of freedom has essentially no effect on estimates of the power spectrum, if each $C_\ell$ is regarded as a separate parameter or if the angular power spectrum is parametrized in terms of an amplitude (Q) and spectral index (n). Fixing the value of the angular power spectrum at its maxiumum likelihood estimate, the best fit skewness is S=6.5\pm6.0\times10^4(\muK)^3; marginalizing over Q the estimate of the skewness is S=6.5\pm8.4\times10^4(\muK)^3 and marginalizing over n one has S=6.5\pm8.5\times10^4(\muK)^3.Comment: submitted to Astrophysical Journal Letter

Different fractal properties of positive and negative returns

We perform an analysis of fractal properties of the positive and the negative changes of the German DAX30 index separately using Multifractal Detrended Fluctuation Analysis (MFDFA). By calculating the singularity spectra $f(\alpha)$ we show that returns of both signs reveal multiscaling. Curiously, these spectra display a significant difference in the scaling properties of returns with opposite sign. The negative price changes are ruled by stronger temporal correlations than the positive ones, what is manifested by larger values of the corresponding H\"{o}lder exponents. As regards the properties of dominant trends, a bear market is more persistent than the bull market irrespective of the sign of fluctuations.Comment: presented at FENS2007 conference, 8 pages, 4 Fig