8,491 research outputs found
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 that is broken to 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 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.
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