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

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

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.

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

Modelling and Forecasting Noisy Realized Volatility

Several methods have recently been proposed in the ultra high frequency financial literature to remove the effects of microstructure noise and to obtain consistent estimates of the integrated volatility (IV) as a measure of ex-post daily volatility. Even bias-corrected and consistent (modified) realized volatility (RV) estimates of the integrated volatility can contain residual microstructure noise and other measurement errors. Such noise is called “realized volatility error”. Since such measurement errors are ignored, we need to take account of them in estimating and forecasting IV. This paper investigates through Monte Carlo simulations the effects of RV errors on estimating and forecasting IV with RV data. It is found that: (i) neglecting RV errors can lead to serious bias in estimators due to model misspecification; (ii) the effects of RV errors on one-step ahead forecasts are minor when consistent estimators are used and when the number of intraday observations is large; and (iii) even the partially corrected recently proposed in the literature should be fully corrected for evaluating forecasts. This paper proposes a full correction of , which can be applied to linear and nonlinear, short and long memory models. An empirical example for S&P 500 data is used to demonstrate that neglecting RV errors can lead to serious bias in estimating the model of integrated volatility, and that the new method proposed here can eliminate the effects of the RV noise. The empirical results also show that the full correction for is necessary for an accurate description of goodness-of-fit.Realized volatility; diffusion; financial econometrics; measurement errors; forecasting; model evaluation; goodness-of-fit

Asymmetry and Long Memory in Volatility Modelling

A wide variety of conditional and stochastic variance models has been used to estimate latent volatility (or risk). In this paper, we propose a new long memory asymmetric volatility model which captures more flexible asymmetric patterns as compared with several existing models. We extend the new specification to realized volatility by taking account of measurement errors, and use the Efficient Importance Sampling technique to estimate the model. As an empirical example, we apply the new model to the realized volatility of S&P500 to show that the new specification of asymmetry significantly improves the goodness of fit, and that the out-of-sample forecasts and Value-at-Risk (VaR) thresholds are satisfactory. Overall, the results of the out-of-sample forecasts show the adequacy of the new asymmetric and long memory volatility model for the period including the global financial crisis.Asymmetric volatility, Long memory, Realized volatility, Measurement errors, Efficient importance sampling.