H-Si bonding-induced unusual electronic properties of silicene: a method to identify hydrogen concentration

Hydrogenated silicenes possess peculiar properties owing to the strong H-Si bonds, as revealed by an investigation using first principles calculations. The various charge distributions, bond lengths, energy bands, and densities of states strongly depend on different hydrogen configurations and concentrations. The competition of strong H-Si bondings and weak sp3 hybridization dominate the electronic properties. Chair configurations belong to semiconductors, while the top configurations show a nearly dispersionless energy band at the Fermi level. Both two systems display H-related partially flat bands at middle energy, and recovery of low-lying \pi bands during the reduction of concentration. Their densities of states exhibit prominent peaks at middle energy, and the top systems have a delta-funtion-like peak at E=0. The intensity of these peaks are gradually weakened as the concentration decreases, providing an effective method to identify the H-concentration in scanning tunneling spectroscopy experiments

Intraday forecasts of a volatility index: Functional time series methods with dynamic updating

As a forward-looking measure of future equity market volatility, the VIX index has gained immense popularity in recent years to become a key measure of risk for market analysts and academics. We consider discrete reported intraday VIX tick values as realisations of a collection of curves observed sequentially on equally spaced and dense grids over time and utilise functional data analysis techniques to produce one-day-ahead forecasts of these curves. The proposed method facilitates the investigation of dynamic changes in the index over very short time intervals as showcased using the 15-second high-frequency VIX index values. With the help of dynamic updating techniques, our point and interval forecasts are shown to enjoy improved accuracy over conventional time series models.Comment: 29 pages, 5 figures, To appear at the Annals of Operations Researc

On a Poissonian Change-Point Model with Variable Jump Size

A model of Poissonian observation having a jump (change-point) in the intensity function is considered. Two cases are studied. The first one corresponds to the situation when the jump size converges to a non-zero limit, while in the second one the limit is zero. The limiting likelihood ratios in these two cases are quite different. In the first case, like in the case of a fixed jump size, the normalized likelihood ratio converges to a log Poisson process. In the second case, the normalized likelihood ratio converges to a log Wiener process, and so, the statistical problems of parameter estimation and hypotheses testing are asymptotically equivalent in this case to the well known problems of change-point estimation and testing for the model of a signal in white Gaussian noise. The properties of the maximum likelihood and Bayesian estimators, as well as those of the general likelihood ratio, Wald's and Bayesian tests are deduced form the convergence of normalized likelihood ratios. The convergence of the moments of the estimators is also established. The obtained theoretical results are illustrated by numerical simulations