A New Way of the Skeletal Metals Synthesis

1 Results Strongly disordered metals with the high chemical and catalytic activity are often called as skeletal metals.Usually for their preparation the metallides of d-metal (which afterwards will be left in the “skeleton“) and chemically active component(s) are firstly synthesized.Then the last one(s) is (are) removed by the leaching with aqueous solutions of alkalis or carbonates.However,this method sometime fails,first of all,for the reactions,which should be realized in non-aqueous conditions.In th...     

Structural Changes of Water Molecules Upon the Reduction of Quinones in The Reaction Center from Rhodobactery Sphaeroides

1 Results The photosynthetic bacterial reaction center (RC) is a membrane protein complex.The RC is composed of three protein subunits and redox components such as bacteriochlorophylls, bacteriopheophytins,and quinones.The RC performs the photochemical electron transfer from the bacteriochlorophyll dimer through a series of electron donor and acceptor molecules to a secondary quinone,QB.QB accepts electrons from a primary quinone,QA,in two sequential electron transfer reactions.The second electron trans...     

Forecasting volatility by means of threshold models

The aim of this paper is to compare the forecasting performance of competing threshold models, in order to capture the asymmetric effect in the volatility. We focus on examining the relative out‐of‐sample forecasting ability of the SETAR‐Threshold GARCH (SETAR‐TGARCH) and the SETAR‐Threshold Stochastic Volatility (SETAR‐THSV) models compared to the GARCH model and Stochastic Volatility (SV) model. However, the main problem in evaluating the predictive ability of volatility models is that the ‘true’ underlying volatility process is not observable and thus a proxy must be defined for the unobservable volatility. For the class of nonlinear state space models (SETAR‐THSV and SV), a modified version of the SIR algorithm has been used to estimate the unknown parameters. The forecasting performance of competing models has been compared for two return time series: IBEX 35 and S&P 500. We explore whether the increase in the complexity of the model implies that its forecasting ability improves. Copyright © 2007 John Wiley & Sons, Ltd.