Dark matter for excess of AMS-02 positrons and antiprotons

We propose a dark matter explanation to simultaneously account for the excess of antiproton-to-proton and positron power spectra observed in the AMS-02 experiment while having the right dark matter relic abundance and satisfying the current direct search bounds. We extend the Higgs triplet model with a hidden gauge symmetry of $SU(2)_X$ that is broken to $Z_3$ by a quadruplet scalar field, rendering the associated gauge bosons stable weakly-interacting massive particle dark matter candidates. By coupling the complex Higgs triplet and the $SU(2)_X$ quadruplet, the dark matter candidates can annihilate into triplet Higgs bosons each of which in turn decays into lepton or gauge boson final states. Such a mechanism gives rise to correct excess of positrons and antiprotons with an appropriate choice of the triplet vacuum expectation value. Besides, the model provides a link between neutrino mass and dark matter phenomenology.Comment: 12 pages, 3 figures, references and comments added, version to appear in Phys. Lett.

Predicting Stock Volatility Using After-Hours Information

We use realized volatilities based on after hours high frequency returns to predict next day volatility. We extend GARCH and long-memory forecasting models to include additional information: the whole night, the preopen, the postclose realized variance, and the overnight squared return. For four NASDAQ stocks (MSFT, AMGN, CSCO, and YHOO) we find that the inclusion of the preopen variance can improve the out-of-sample forecastability of the next day conditional day volatility. Additionally, we find that the postclose variance and the overnight squared return do not provide any predictive power for the next day conditional volatility. Our findings support the results of prior studies that traders trade for non-information reasons in the postclose period and trade for information reasons in the preopen period.