Non-Gaussian Foreground Residuals of the WMAP First Year Maps

We investigate the effect of foreground residuals in the WMAP data (Bennet et al. 2004) by adding foreground contamination to Gaussian ensembles of CMB signal and noise maps. We evaluate a set of non-Gaussian estimators on the contaminated ensembles to determine with what accuracy any residual in the data can be constrained using higher order statistics. We apply the estimators to the raw and cleaned Q, V, and W band first year maps. The foreground subtraction method applied to clean the data in Bennet et al. (2004a) appears to have induced a correlation between the power spectra and normalized bispectra of the maps which is absent in Gaussian simulations. It also appears to increase the correlation between the dl=1 inter-l bispectrum of the cleaned maps and the foreground templates. In a number of cases the significance of the effect is above the 98% confidence level.Comment: 9 pages, 4 figure

Exotic tensor gauge theory and duality

Gauge fields in exotic representations of the Lorentz group in D dimensions - i.e. ones which are tensors of mixed symmetry corresponding to Young tableaux with arbitrary numbers of rows and columns - naturally arise through massive string modes and in dualising gravity and other theories in higher dimensions. We generalise the formalism of differential forms to allow the discussion of arbitrary gauge fields. We present the gauge symmetries, field strengths, field equations and actions for the free theory, and construct the various dual theories. In particular, we discuss linearised gravity in arbitrary dimensions, and its two dual forms.Comment: 28 pages, LaTeX, references added, minor change

Forecasting Realized Volatility with Linear and Nonlinear Univariate Models

In this paper we consider a nonlinear model based on neural networks as well as linear models to forecast the daily volatility of the S&P 500 and FTSE 100 futures. As a proxy for daily volatility, we consider a consistent and unbiased estimator of the integrated volatility that is computed from high frequency intra-day returns. We also consider a simple algorithm based on bagging (bootstrap aggregation) in order to specify the models analyzed.Financial econometrics; volatility forecasting; neural networks; nonlinear models; realized volatility; bagging

A multiple regime smooth transition heterogeneous autoregressive model for long memory and asymmetries

In this paper we propose a flexible model to capture nonlinearities and long-range dependence in time series dynamics. The new model is a multiple regime smooth transition extension of the Heterogenous Autoregressive (HAR) model, which is specifically designed to model the behavior of the volatility inherent in financial time series. The model is able to describe simultaneously long memory, as well as sign and size asymmetries. A sequence of tests is developed to determine the number of regimes, and an estimation and testing procedure is presented. Monte Carlo simulations evaluate the finite-sample properties of the proposed tests and estimation procedures. We apply the model to several Dow Jones Industrial Average index stocks using transaction level data from the Trades and Quotes database that covers ten years of data. We find strong support for long memory and both sign and size asymmetries. Furthermore, the new model, when combined with the linear HAR model, is viable and flexible for purposes of forecasting volatility.Realized volatility, smooth transition, heterogeneous autoregression, financial econometrics,leverage, sign and size asymmetries, forecasting, risk management, model combination.

Forecasting Realized Volatility with Linear and Nonlinear Models

In this paper we consider a nonlinear model based on neural networks as well as linear models to forecast the daily volatility of the S&P 500 and FTSE 100 indexes. As a proxy for daily volatility, we consider a consistent and unbiased estimator of the integrated volatility that is computed from high frequency intra-day returns. We also consider a simple algorithm based on bagging (bootstrap aggregation) in order to specify the models analyzed in the paper.

"Forecasting Realized Volatility with Linear and Nonlinear Models"

In this paper we consider a nonlinear model based on neural networks as well as linear models to forecast the daily volatility of the S&P 500 and FTSE 100 indexes. As a proxy for daily volatility, we consider a consistent and unbiased estimator of the integrated volatility that is computed from high frequency intra-day returns. We also consider a simple algorithm based on bagging (bootstrap aggregation) in order to specify the models analyzed in the paper.

How good are MatLab, Octave and Scilab for Computational Modelling?

In this article we test the accuracy of three platforms used in computational modelling: MatLab, Octave and Scilab, running on i386 architecture and three operating systems (Windows, Ubuntu and Mac OS). We submitted them to numerical tests using standard data sets and using the functions provided by each platform. A Monte Carlo study was conducted in some of the datasets in order to verify the stability of the results with respect to small departures from the original input. We propose a set of operations which include the computation of matrix determinants and eigenvalues, whose results are known. We also used data provided by NIST (National Institute of Standards and Technology), a protocol which includes the computation of basic univariate statistics (mean, standard deviation and first-lag correlation), linear regression and extremes of probability distributions. The assessment was made comparing the results computed by the platforms with certified values, that is, known results, computing the number of correct significant digits.Comment: Accepted for publication in the Computational and Applied Mathematics journa

Low-latitude boundary layer clouds as seen by CALIPSO

The distribution of low-level cloud in the tropical belt is investigated using 6 months of Level 2 retrievals from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) at 333 m and 1 km horizontal resolutions. Regional patterns of tropical clouds emerge from the data, matching expectations from existing observations. The advantage of the lidar is highlighted by the distribution of cloud-top height, revealing the preponderance of low-level clouds over the tropical oceans. Over land, cloud top is more uniformly distributed under the influence of diurnal variation. The integrated cloud-top distribution suggests tropical, marine low-cloud amount around 25-30%; a merged CALIPSO-CloudSat product has a similar cloud-top distribution and includes a complementary estimate of cloud fraction based on the lidar detections. The low-cloud distribution is similar to that found in fields of shallow cumulus observed during the Rain in Cumulus Over the Ocean (RICO) field study. The similarity is enhanced by sampling near the RICO site or sampling large-scale conditions similar to those during RICO. This finding shows how satellite observations can help to generalize findings from detailed field observations